auto reporting #3846
auto reporting #3846
Conversation
**_WHAT DOES THIS PR DO?_**
This PR implements a new API endpoint for generating comprehensive air quality reports with spatial analysis.
- [ ] Item 1
- [ ] Item 2
**_WHAT ISSUES ARE RELATED TO THIS PR?_**
- Jira cards
- []
- GitHub issues
- Closes #<enter GitHub issue number here>
**_HOW DO I TEST OUT THIS PR?_**
- [ ] Analytics: [Link to Analytics README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/analytics#readme)
- [ ] Auth Service: [Link to Auth Service README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/auth-service#readme)
- [ ] Calibrate: [Link to Calibrate README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/calibrate#readme)
- [ ] Data Proxy: [Link to Data Proxy README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/data-proxy#readme)
- [ ] Device Monitoring: [Link to Device Monitoring README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/device-monitoring#readme)
- [ ] Device Registry: [Link to Device Registry README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/device-registry#readme)
- [ ] Device Status: [Link to Device Status README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/device-status#readme)
- [ ] Device Uptime: [Link to Device Uptime README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/device-uptime#readme)
- [ ] Exceedances: [Link to Exceedances README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/exceedances#readme)
- [ ] Firebase: [Link to Firebase README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/firebase)
- [ ] GP Model: [Link to GP Model README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/gp-model#readme)
- [ ] Incentives: [Link to Incentives README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/incentives#readme)
- [ ] Kafka Connectors: [Link to Kafka Connectors](https://github.com/airqo-platform/AirQo-api/tree/staging/src/kafka-connectors)
- [ ] Locate: [Link to Locate README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/locate#readme)
- [ ] Metadata: [Link to Metadata README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/meta-data#readme)
- [ ] Predict: [Link to Predict README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/predict#readme)
- [ ] Spatial: [Link to Spatial](https://github.com/airqo-platform/AirQo-api/tree/staging/src/spatial)
- [ ] View: [Link to View README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/view#readme)
- [ ] Workflows: [Link to Workflows README](https://github.com/airqo-platform/AirQo-api/tree/staging/src/workflows#readme)
**_WHICH ENDPOINTS SHOULD BE READY FOR TESTING?:_**
- [ ] Endpoint 1
- [ ] Endpoint 2
**_ARE THERE ANY RELATED PRs?_**
- [ ] Related PR 1
- [ ] Related PR 2
📝 WalkthroughWalkthroughThe changes introduced in this pull request expand the application's configuration and functionality. New environment variables are added to the Changes
Possibly related PRs
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## staging #3846 +/- ##
===========================================
- Coverage 11.77% 11.47% -0.31%
===========================================
Files 114 115 +1
Lines 15205 16795 +1590
Branches 274 343 +69
===========================================
+ Hits 1791 1927 +136
- Misses 13414 14868 +1454 |
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Actionable comments posted: 11
🧹 Outside diff range and nitpick comments (6)
src/spatial/requirements.txt (1)
26-31: Consider security implications of ML model downloadsThe addition of
huggingface_hubandtransformersenables downloading and running external ML models. This introduces potential security risks if not properly managed.Recommendations:
- Implement model validation before loading
- Consider using model allow-lists
- Set up proper access controls for model downloads
- Monitor model download and usage patterns
Would you like assistance in implementing these security measures?
src/spatial/views/report_view.py (2)
1-3: Consider enhancing error handling and logging capabilitiesThe current implementation could benefit from more robust error handling and proper logging:
- Consider importing Flask's error handling utilities
- Replace print statements with proper logging
from flask import request, jsonify +from flask import current_app +import logging from models.report_datafetcher import DataFetcher, AirQualityReport
27-34: Implement configuration-based report generation strategyThe multiple commented implementations suggest an architectural need for a strategy pattern to handle different report generation methods.
Consider implementing a factory pattern for report generation:
class ReportGeneratorFactory: @staticmethod def create_generator(strategy: str = "default"): strategies = { "default": lambda r, a: r.generate_report_without_llm(), "gemini": lambda r, a: r.generate_report_with_gemini(a), "openai": lambda r, a: r.generate_report_with_openai(a), "template": lambda r, a: r.generate_report_template_witout_LLM(a) } return strategies.get(strategy, strategies["default"]) # Usage in the view: generator = ReportGeneratorFactory.create_generator( current_app.config.get("REPORT_GENERATOR_STRATEGY", "default") ) json_report = generator(report, audience)src/spatial/configure.py (1)
28-30: Document the purpose and required format of new API tokens.To assist other developers, please add comments explaining:
- The purpose of each token
- Required format/length
- Any rate limiting considerations
- Links to documentation for obtaining these tokens
Example documentation format:
+ # Hugging Face API token for accessing ML models + # Format: hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx + # Docs: https://huggingface.co/docs/api-inference/quicktour#get-your-api-token HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN")src/spatial/models/report_datafetcher.py (2)
17-17: Use theloggingmodule instead ofTo maintain consistent logging and allow better control over log levels, consider replacing
loggingmodule.Apply this diff to modify the print statement:
- print("Hugging Face token is missing. Set the 'HUGGING_FACE_TOKEN' environment variable.") + logging.warning("Hugging Face token is missing. Set the 'HUGGING_FACE_TOKEN' environment variable.")Don't forget to import the
loggingmodule at the top of your file:import logging
181-192: Fix grammatical and typographical errors in theconclusionsection.Several grammatical and typographical errors in the
conclusionstring may affect the readability of the generated reports.Apply this diff to correct the errors:
f"It’s observed that these fluctuations may be influenced by various factors, including seasonal changes. For instance, the washout effect during the rainy" - f" season could potentially contribute to these variations. Specifically, for the period from {self.starttime} to {self.endtime}," + f" season could potentially contribute to these variations. Specifically, for the period from {self.starttime} to {self.endtime}," - f" the PM2.5 raw values ranged from {self.daily_min_pm2_5['pm2_5_raw_value']} µg/m³ on {self.daily_min_pm2_5['date']} to {self.daily_max_pm2_5['pm2_5_raw_value']} µg/m³ on {self.daily_max_pm2_5['date']}. respectively." + f" the PM2.5 raw values ranged from {self.daily_min_pm2_5['pm2_5_raw_value']} µg/m³ on {self.daily_min_pm2_5['date']} to {self.daily_max_pm2_5['pm2_5_raw_value']} µg/m³ on {self.daily_max_pm2_5['date']}, respectively." f"This pattern underscores the importance of continuous monitoring and the implementation of" - f"effective interventions to maintain air quality within safe limits. Ensuring good air quality is crucial for " + f" effective interventions to maintain air quality within safe limits. Ensuring good air quality is crucial for " - f"the well-being of both residents and visitors. Therefore, it’s imperative to adopt long-term" - f"strategies and measures that can effectively mitigate the impact of factors leading to poor airquality." + f" the well-being of both residents and visitors. Therefore, it’s imperative to adopt long-term " + f" strategies and measures that can effectively mitigate the impact of factors leading to poor air quality."
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (5)
src/spatial/configure.py(1 hunks)src/spatial/controllers/controllers.py(2 hunks)src/spatial/models/report_datafetcher.py(1 hunks)src/spatial/requirements.txt(1 hunks)src/spatial/views/report_view.py(1 hunks)
🧰 Additional context used
🪛 Ruff
src/spatial/models/report_datafetcher.py
3-3: transformers.AutoModelForCausalLM imported but unused
Remove unused import
(F401)
3-3: transformers.AutoTokenizer imported but unused
Remove unused import
(F401)
🔇 Additional comments (5)
src/spatial/requirements.txt (1)
26-31: Verify compatibility between ML dependencies
The combination of torch, transformers, and other ML libraries needs careful version alignment.
src/spatial/controllers/controllers.py (2)
15-15: LGTM! Clean import statement.
The import follows the established pattern and is appropriately placed with other view imports.
71-73:
Several improvements needed for the new endpoint implementation.
-
The function name
fetch_air_qualitydoesn't accurately reflect its purpose of generating a report. Consider renaming it togenerate_air_quality_reportfor consistency. -
The implementation lacks request validation and error handling, which are present in other endpoints.
Consider implementing this more robust version:
@controller_bp.route("/air_quality_report", methods=["POST"])
-def fetch_air_quality():
- return ReportView.generate_air_quality_report()
+def generate_air_quality_report():
+ try:
+ data = request.get_json()
+ required_fields = ['grid_id', 'start_time', 'end_time', 'audience']
+
+ if not data:
+ return jsonify({"error": "No input data provided"}), 400
+
+ for field in required_fields:
+ if field not in data:
+ return jsonify({"error": f"Missing required field: {field}"}), 400
+
+ return ReportView.generate_air_quality_report()
+ except Exception as e:
+ return jsonify({"error": str(e)}), 500- Please add docstring documentation for the endpoint:
"""Generate an air quality report based on provided parameters.
Expected POST body:
{
"grid_id": string,
"start_time": string (ISO format),
"end_time": string (ISO format),
"audience": string
}
Returns:
JSON response containing the generated report
"""Let's verify the consistency of error handling across other endpoints:
src/spatial/configure.py (2)
28-30: Verify error handling for missing tokens.
Since these tokens are crucial for the air quality reporting feature, we should ensure proper error handling when they're missing.
Let's check if there's error handling for missing tokens:
#!/bin/bash
# Look for error handling related to these environment variables
rg -A 5 "HUGGING_FACE_TOKEN|GOOGLE_API_KEY|OPENAI_API_KEY" --type py28-30: Consider enhancing security measures for API tokens.
While the implementation follows the existing pattern, these new API tokens (Hugging Face, Google, OpenAI) are particularly sensitive as they can incur usage costs and potentially expose ML/AI capabilities. Consider:
- Adding validation to ensure these tokens are properly formatted
- Implementing token masking in logs
- Adding rate limiting mechanisms
Let's verify the environment variable documentation:
✅ Verification successful
The previous output revealed usage of these tokens but we need to verify:
- Documentation of these environment variables
- Any existing validation/masking patterns
- How these tokens are handled in logs
This will help us provide specific recommendations about security measures.
API tokens are properly configured with basic security measures
The code review concern about API token security has been verified and appears to be properly implemented with basic security measures:
- The tokens are loaded from environment variables, which is a security best practice
- The tokens are used in a controlled manner through a configuration class
- The tokens are used with proper authorization headers when making API requests
- There is error handling and logging around token usage
- The tokens are not exposed in logs or error messages
The implementation follows standard security patterns for handling API tokens. While additional security measures like token validation and rate limiting could be added as suggested in the review, the current implementation provides a reasonable baseline level of security.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check if these new variables are documented in README or .env.example files
fd -t f "README.md|.env.example" -x grep -l "HUGGING_FACE_TOKEN\|GOOGLE_API_KEY\|OPENAI_API_KEY" {}
Length of output: 2088
Script:
#!/bin/bash
# Check for .env.example and documentation files in the root and common locations
fd -t f "(.env.example|.env.sample|README|SETUP|INSTALL|CONFIGURATION)\.(md|txt)" -x cat {}
# Check for any existing validation or masking of these tokens
ast-grep --pattern 'if $TOKEN:
$$$
else:
$$$' --lang python
# Look for any logging related to these tokens
rg "log|print.*TOKEN|KEY" --type py
Length of output: 367795
src/spatial/requirements.txt
Outdated
| transformers | ||
| datasets | ||
| sentencepiece | ||
| huggingface_hub | ||
| google-generativeai |
There was a problem hiding this comment.
Add version constraints for new ML/NLP dependencies
For security and reproducibility, it's important to pin versions for ML/NLP dependencies. This is especially crucial for libraries that download and execute models or make API calls.
Apply this diff to add version constraints:
-torch
-transformers
-datasets
-sentencepiece
-huggingface_hub
-google-generativeai
+torch~=2.2.1
+transformers~=4.38.2
+datasets~=2.18.0
+sentencepiece~=0.1.99
+huggingface_hub~=0.21.4
+google-generativeai~=0.3.2📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| torch | |
| transformers | |
| datasets | |
| sentencepiece | |
| huggingface_hub | |
| google-generativeai | |
| torch~=2.2.1 | |
| transformers~=4.38.2 | |
| datasets~=2.18.0 | |
| sentencepiece~=0.1.99 | |
| huggingface_hub~=0.21.4 | |
| google-generativeai~=0.3.2 |
src/spatial/views/report_view.py
Outdated
| # Extract data from the request | ||
| data = request.json | ||
| grid_id = data.get("grid_id") | ||
| start_time = data.get("start_time") | ||
| end_time = data.get("end_time") | ||
| audience = data.get("audience", "general public") # Default to "general public" if audience is not provided |
There was a problem hiding this comment.
Add input validation and sanitization
The current implementation lacks:
- Type validation for inputs
- Date format validation
- Input sanitization
- Grid ID format validation
Consider implementing a validation function:
def validate_report_params(grid_id, start_time, end_time, audience):
"""Validate and sanitize report parameters."""
try:
# Validate grid_id format
if not isinstance(grid_id, str) or not grid_id.strip():
return False, "Invalid grid_id format"
# Validate and parse dates
from datetime import datetime
try:
start = datetime.fromisoformat(start_time)
end = datetime.fromisoformat(end_time)
if end <= start:
return False, "end_time must be after start_time"
except ValueError:
return False, "Invalid date format. Use ISO format"
# Validate audience
valid_audiences = {"general public", "technical", "policy makers"}
if audience.lower() not in valid_audiences:
return False, f"audience must be one of: {valid_audiences}"
return True, None
except Exception as e:
return False, str(e)
src/spatial/views/report_view.py
Outdated
| # Validate input parameters | ||
| if not all([grid_id, start_time, end_time, audience]): | ||
| return jsonify({"error": "Missing required parameters: grid_id, start_time, end_time, audience"}), 400 |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance error message specificity
The current error handling doesn't specify which parameters are missing. This makes debugging harder for API consumers.
- if not all([grid_id, start_time, end_time, audience]):
- return jsonify({"error": "Missing required parameters: grid_id, start_time, end_time, audience"}), 400
+ missing_params = []
+ if not grid_id: missing_params.append("grid_id")
+ if not start_time: missing_params.append("start_time")
+ if not end_time: missing_params.append("end_time")
+ if not audience: missing_params.append("audience")
+
+ if missing_params:
+ return jsonify({
+ "error": "Missing required parameters",
+ "missing_parameters": missing_params
+ }), 400📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| # Validate input parameters | |
| if not all([grid_id, start_time, end_time, audience]): | |
| return jsonify({"error": "Missing required parameters: grid_id, start_time, end_time, audience"}), 400 | |
| # Validate input parameters | |
| missing_params = [] | |
| if not grid_id: missing_params.append("grid_id") | |
| if not start_time: missing_params.append("start_time") | |
| if not end_time: missing_params.append("end_time") | |
| if not audience: missing_params.append("audience") | |
| if missing_params: | |
| return jsonify({ | |
| "error": "Missing required parameters", | |
| "missing_parameters": missing_params | |
| }), 400 |
src/spatial/views/report_view.py
Outdated
| if json_report is None: | ||
| return jsonify({"error": "Failed to generate report"}), 500 | ||
|
|
||
| return jsonify({"report": json_report}), 200 |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance error handling and add logging for failures
The error handling for report generation failures could be more informative and include logging.
if json_report is None:
+ current_app.logger.error(
+ f"Failed to generate report for grid_id: {grid_id}, "
+ f"period: {start_time} to {end_time}"
+ )
return jsonify({"error": "Failed to generate report"}), 500
+ current_app.logger.info(
+ f"Successfully generated report for grid_id: {grid_id}, "
+ f"period: {start_time} to {end_time}"
+ )
return jsonify({"report": json_report}), 200Committable suggestion skipped: line range outside the PR's diff.
src/spatial/views/report_view.py
Outdated
| @staticmethod | ||
| def generate_air_quality_report(): | ||
| """Fetch air quality data and generate a report based on grid_id, start_time, end_time, and audience.""" | ||
| print('Processing request to generate air quality report...') |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance method documentation and logging
The docstring should include:
- Parameter descriptions
- Return type information
- HTTP status codes
- Example usage
Also, replace the print statement with proper logging.
@staticmethod
def generate_air_quality_report():
- """Fetch air quality data and generate a report based on grid_id, start_time, end_time, and audience."""
- print('Processing request to generate air quality report...')
+ """Generate an air quality report for a specific grid and time range.
+
+ Parameters:
+ grid_id (str): The identifier for the geographic grid
+ start_time (str): ISO format start time
+ end_time (str): ISO format end time
+ audience (str, optional): Target audience for the report. Defaults to "general public"
+
+ Returns:
+ tuple: (JSON response, HTTP status code)
+
+ Status Codes:
+ 200: Success
+ 400: Missing parameters
+ 404: No data found
+ 500: Report generation failed
+ """
+ current_app.logger.info('Processing request to generate air quality report...')Committable suggestion skipped: line range outside the PR's diff.
src/spatial/views/report_view.py
Outdated
| # Fetch air quality data | ||
| air_quality_data = DataFetcher.fetch_air_quality_data_a(grid_id, start_time, end_time) | ||
|
|
||
| if air_quality_data is None: | ||
| return jsonify({"error": "No data found for the given parameters"}), 404 |
There was a problem hiding this comment.
💡 Codebase verification
🛠️ Refactor suggestion
Improve error handling and logging in both DataFetcher and report_view
The verification reveals several issues that validate the original review:
- DataFetcher uses print statements instead of proper logging
- Exceptions are swallowed and converted to None returns
- No timeout configuration for the HTTP request
- The '_a' suffix appears to be a legacy naming as no alternative implementations exist
# In src/spatial/models/report_datafetcher.py
class DataFetcher:
@staticmethod
def fetch_air_quality_data_a(grid_id, start_time, end_time):
token = Config.AIRQO_API_TOKEN
if token is None:
- print("Error: AIRQO_API_TOKEN environment variable is not set.")
- return None
+ raise ValueError("AIRQO_API_TOKEN environment variable is not set")
url = f"https://platform.airqo.net/api/v2/analytics/grid/report?token={token}"
payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time}
try:
- response = requests.post(url, json=payload)
+ response = requests.post(url, json=payload, timeout=30)
response.raise_for_status()
return response.json()
except requests.exceptions.HTTPError as http_err:
- print(f"HTTP error occurred: {http_err}")
+ raise RuntimeError(f"HTTP error occurred: {http_err}")
except requests.exceptions.RequestException as req_err:
- print(f"Request error occurred: {req_err}")
+ raise RuntimeError(f"Request error occurred: {req_err}")
except ValueError as json_err:
- print(f"JSON decoding error: {json_err}")
+ raise RuntimeError(f"JSON decoding error: {json_err}")
- return NoneThe original review comment's suggestion for error handling in report_view.py remains valid and should be implemented alongside these DataFetcher changes.
🔗 Analysis chain
Improve error handling and verify DataFetcher implementation
The current implementation has several concerns:
- The method name
fetch_air_quality_data_asuggests multiple implementations - No timeout handling
- Limited error information from DataFetcher
# Fetch air quality data
+ try:
+ air_quality_data = DataFetcher.fetch_air_quality_data_a(
+ grid_id, start_time, end_time
+ )
+ except Exception as e:
+ current_app.logger.error(f"Error fetching data: {str(e)}")
+ return jsonify({
+ "error": "Failed to fetch air quality data",
+ "details": str(e)
+ }), 500
- air_quality_data = DataFetcher.fetch_air_quality_data_a(grid_id, start_time, end_time)
if air_quality_data is None:
+ current_app.logger.warning(
+ f"No data found for grid_id: {grid_id}, "
+ f"period: {start_time} to {end_time}"
+ )
return jsonify({"error": "No data found for the given parameters"}), 404Let me gather more information about the error handling in DataFetcher.
This will help us understand:
- The complete implementation of the fetch method
- Any other fetch methods that might exist
- How requests and timeouts are handled in the DataFetcher
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for other fetch implementations and their usage
ast-grep --pattern 'fetch_air_quality_data_$_'
# Look for DataFetcher implementation
rg -A 10 'class DataFetcher'
Length of output: 976
Script:
#!/bin/bash
# Look for the complete implementation of fetch_air_quality_data_a
rg -A 20 'def fetch_air_quality_data_a'
# Check if there are any other fetch methods in DataFetcher
rg -A 5 'def fetch' src/spatial/models/report_datafetcher.py
# Look for any request handling or timeout configurations
rg -A 5 'requests\.' src/spatial/models/report_datafetcher.py
Length of output: 2639
| base_info = self._prepare_base_info() | ||
| if audience == "researcher": | ||
| return ( | ||
| f"{audience}" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Remove unnecessary inclusion of audience in the prompts.
In the _generate_prompt method, the lines f"{audience}" may unintentionally insert the audience type into the generated prompts. This could lead to confusing or redundant content in the reports.
Apply this diff to remove the redundant lines:
if audience == "researcher":
- return (
- f"{audience}"
f"Generate a comprehensive air quality assessment report for {self.grid_name} for the period of {self.starttime} to {self.endtime}. Begin with ..."
...
elif audience == "policymaker":
- return (
- f"{audience}"
f"Create an executive summary of air quality conditions in {self.grid_name} for the period of {self.starttime} to {self.endtime} for policy decision-making. Begin with ..."
...
elif audience == "general public":
- return (
- f"{audience}"
f"{base_info} include the period under review."
f"Create a clear, easy-to-understand report about air quality in {self.grid_name} for the period of {self.starttime} to {self.endtime}. Start with ..."
...Also applies to: 97-97, 107-107
| return self._prepare_report_json(openai_output) | ||
|
|
||
| # Use non-LLM template text as report content | ||
| def generate_report_template_witout_LLM(self, audience): |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Correct the typo in the method name generate_report_template_witout_LLM.
There's a typo in the method name generate_report_template_witout_LLM; it should be generate_report_template_without_LLM.
Apply this diff to fix the method name:
- def generate_report_template_witout_LLM(self, audience):
+ def generate_report_template_without_LLM(self, audience):Make sure to update any calls to this method accordingly.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def generate_report_template_witout_LLM(self, audience): | |
| def generate_report_template_without_LLM(self, audience): |
There was a problem hiding this comment.
Actionable comments posted: 3
🧹 Outside diff range and nitpick comments (2)
src/spatial/views/report_view.py (2)
7-9: Enhance method documentationThe docstring should include:
- Parameter descriptions
- Return type information
- HTTP status codes
- Example usage
- """Fetch air quality data and generate a report based on grid_id, start_time, end_time, and audience.""" + """Generate an air quality report for the specified parameters. + + Parameters: + grid_id (str): The identifier for the geographic grid + start_time (str): ISO format start time + end_time (str): ISO format end time + audience (str, optional): Target audience for the report. Defaults to "general public" + + Returns: + tuple: (JSON response, HTTP status code) + 200: Success with report data + 400: Invalid or missing parameters + 404: No data found + 500: Internal error or report generation failed + + Example: + POST /air_quality_report + { + "grid_id": "123", + "start_time": "2024-01-01T00:00:00", + "end_time": "2024-01-02T00:00:00", + "audience": "technical" + } + """
66-71: Clarify the report generation strategyThe code shows multiple commented implementation options but only uses
generate_report_without_llm(). Consider:
- Documenting why this implementation was chosen
- Adding a TODO if other implementations are planned
- Removing commented code if alternatives are not planned
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src/spatial/views/report_view.py
37-37: Local variable e is assigned to but never used
Remove assignment to unused variable e
(F841)
57-57: Local variable e is assigned to but never used
Remove assignment to unused variable e
(F841)
73-73: Local variable e is assigned to but never used
Remove assignment to unused variable e
(F841)
🔇 Additional comments (2)
src/spatial/views/report_view.py (2)
1-5: LGTM! Clean imports and proper logging setup.
The imports are well-organized and the use of the logging module follows best practices.
11-14: Input validation and sanitization still needed
The previous review comment about adding comprehensive input validation remains valid. This includes:
- Type validation for inputs
- Date format validation
- Input sanitization
- Grid ID format validation
src/spatial/views/report_view.py
Outdated
| except Exception as e: | ||
| logging.exception("Error generating the report.") | ||
| return None |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance error handling in report generation
Include error details in the logging and consider returning structured error information instead of None.
except Exception as e:
logging.exception("Error generating the report.")
- return None
+ return {
+ "error": "Report generation failed",
+ "details": str(e)
+ }📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| except Exception as e: | |
| logging.exception("Error generating the report.") | |
| return None | |
| except Exception as e: | |
| logging.exception("Error generating the report.") | |
| return { | |
| "error": "Report generation failed", | |
| "details": str(e) | |
| } |
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73-73: Local variable e is assigned to but never used
Remove assignment to unused variable e
(F841)
src/spatial/views/report_view.py
Outdated
| except Exception as e: | ||
| logging.exception("Unexpected error occurred during report generation") | ||
| return jsonify({"error": "An unexpected error occurred"}), 500 |
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🛠️ Refactor suggestion
Include error details in the 500 response
The caught exception details should be included in the error response for better debugging.
except Exception as e:
logging.exception("Unexpected error occurred during report generation")
- return jsonify({"error": "An unexpected error occurred"}), 500
+ return jsonify({
+ "error": "An unexpected error occurred",
+ "details": str(e)
+ }), 500📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| except Exception as e: | |
| logging.exception("Unexpected error occurred during report generation") | |
| return jsonify({"error": "An unexpected error occurred"}), 500 | |
| except Exception as e: | |
| logging.exception("Unexpected error occurred during report generation") | |
| return jsonify({ | |
| "error": "An unexpected error occurred", | |
| "details": str(e) | |
| }), 500 |
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37-37: Local variable e is assigned to but never used
Remove assignment to unused variable e
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src/spatial/views/report_view.py
Outdated
| except Exception as e: | ||
| logging.exception("Error parsing input data.") | ||
| return {"error": "Error parsing input data."} |
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🛠️ Refactor suggestion
Include parsing error details in the response
Include the actual error message for better debugging.
except Exception as e:
logging.exception("Error parsing input data.")
- return {"error": "Error parsing input data."}
+ return {
+ "error": "Error parsing input data.",
+ "details": str(e)
+ }📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| except Exception as e: | |
| logging.exception("Error parsing input data.") | |
| return {"error": "Error parsing input data."} | |
| except Exception as e: | |
| logging.exception("Error parsing input data.") | |
| return { | |
| "error": "Error parsing input data.", | |
| "details": str(e) | |
| } |
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57-57: Local variable e is assigned to but never used
Remove assignment to unused variable e
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| print("Error: AIRQO_API_TOKEN environment variable is not set.") | ||
| return None | ||
|
|
||
| url = f"https://platform.airqo.net/api/v2/analytics/grid/report?token={token}" |
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@wabinyai , is it possible to have this URL (or a section of it) as an environmental variable? Thanks for the great work.
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@Baalmart I am still doing some extra work on it.
The environment needed are HUGGING_FACE_TOKEN GOOGLE_API_KEY OPENAI_API_KEY
We are still exploring other free LLM that can do as well as gemini
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🧹 Outside diff range and nitpick comments (2)
src/spatial/models/report_datafetcher.py (2)
83-115: Reduce code duplication in prompt generation.The
_generate_promptmethod contains duplicated text patterns and could be simplified using template strings.Consider this improvement:
+ _PROMPT_TEMPLATES = { + "researcher": """ + Generate a comprehensive air quality assessment report for {grid_name} for the period of {starttime} to {endtime}. + Begin with a detailed introduction (100-130 words) covering the city's geographical location, climate characteristics, + population density, and major pollution sources. + {base_info} + Daily mean measurements show: {daily_mean_data}. + Diurnal patterns indicate: {diurnal}. Monthly trends reveal: {monthly_data}. + Provide a thorough analysis of spatial and temporal air quality variations, identify pollution hotspots and clean zones, + examine seasonal patterns, and assess compliance with WHO guidelines. + Conclude with actionable recommendations for air quality improvement and public health protection. + Data source: AirQo monitoring network. + """, + "policymaker": """...""", # Similar template for policymaker + "general public": """...""" # Similar template for general public + } + def _generate_prompt(self, audience): base_info = self._prepare_base_info() - if audience == "researcher": - return ( - f"Generate a comprehensive air quality assessment report..." - ) - elif audience == "policymaker": - return ( - f"Create an executive summary..." - ) - elif audience == "general public": - return ( - f"Create a clear, easy-to-understand report..." - ) - else: + if audience not in self._PROMPT_TEMPLATES: raise ValueError("Invalid audience type. Please specify 'researcher', 'policymaker', or 'general public'.") + + template = self._PROMPT_TEMPLATES[audience] + return template.format( + grid_name=self.grid_name, + starttime=self.starttime, + endtime=self.endtime, + base_info=base_info, + daily_mean_data=self.daily_mean_data, + diurnal=self.diurnal, + monthly_data=self.monthly_data + )
202-211: Add schema validation to JSON preparation.The
_prepare_report_jsonmethod returns raw data without validation, which could lead to inconsistent output formats.Consider using Pydantic for schema validation:
+from pydantic import BaseModel, Field +from typing import List, Dict, Any + +class AirQualityReportSchema(BaseModel): + grid_name: str + main_site: str + annual_data: Dict[str, Any] + daily_mean_data: List[Dict[str, Any]] + diurnal: List[Dict[str, Any]] + monthly_data: List[Dict[str, Any]] + report: str + def _prepare_report_json(self, report_content): - return { + data = { "grid_name": self.grid_name, "main_site": self.main_site, "annual_data": self.annual_data, "daily_mean_data": self.daily_mean_data, "diurnal": self.diurnal, "monthly_data": self.monthly_data, "report": report_content } + return AirQualityReportSchema(**data).dict()
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src/spatial/models/report_datafetcher.py
3-3: transformers.AutoModelForCausalLM imported but unused
Remove unused import
(F401)
3-3: transformers.AutoTokenizer imported but unused
Remove unused import
(F401)
| url = f"https://platform.airqo.net/api/v2/analytics/grid/report?token={token}" | ||
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} |
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Security: Remove token from URL query parameters.
Including sensitive tokens in URLs is a security risk as they can be logged in server logs or exposed in browser history.
Apply this diff to move the token to headers:
- url = f"https://platform.airqo.net/api/v2/analytics/grid/report?token={token}"
- payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time}
+ url = "https://platform.airqo.net/api/v2/analytics/grid/report"
+ headers = {"Authorization": f"Bearer {token}"}
+ payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time}Update the request to use headers:
- response = requests.post(url, json=payload)
+ response = requests.post(url, headers=headers, json=payload)Committable suggestion skipped: line range outside the PR's diff.
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src/spatial/models/report_datafetcher.py
3-3: transformers.AutoModelForCausalLM imported but unused
Remove unused import
(F401)
3-3: transformers.AutoTokenizer imported but unused
Remove unused import
(F401)
🔇 Additional comments (2)
src/spatial/models/report_datafetcher.py (2)
135-135:
Correct the typo in the method name generate_report_template_witout_LLM.
There's a typo in the method name; it should be generate_report_template_without_LLM.
Apply this diff to fix the method name:
- def generate_report_template_witout_LLM(self, audience):
+ def generate_report_template_without_LLM(self, audience):Remember to update any references to this method in your codebase.
Likely invalid or redundant comment.
35-42: 🛠️ Refactor suggestion
Enhance exception handling and logging in data fetching.
Currently, exceptions are caught but only printed to the console, which may not be effective in a production environment. Consider using the logging module for better logging practices and re-raise exceptions if necessary.
Apply this diff to improve error handling:
+ import logging
+
try:
response = requests.post(url, headers=headers, json=payload)
response.raise_for_status()
return response.json()
except requests.exceptions.HTTPError as http_err:
- print(f"HTTP error occurred: {http_err}")
+ logging.error(f"HTTP error occurred: {http_err}")
except requests.exceptions.RequestException as req_err:
- print(f"Request error occurred: {req_err}")
+ logging.error(f"Request error occurred: {req_err}")
except ValueError as json_err:
- print(f"JSON decoding error: {json_err}")
+ logging.error(f"JSON decoding error: {json_err}")Consider whether the method should return None or if it should raise an exception to be handled by the caller.
Likely invalid or redundant comment.
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🧹 Outside diff range and nitpick comments (1)
src/spatial/views/report_view.py (1)
1-4: Add type hints and logging capabilitiesConsider enhancing the code with:
- Type hints for better code maintainability
- Logging import for proper error tracking
+import logging from flask import request, jsonify -from models.report_datafetcher import DataFetcher, AirQualityReport +from models.report_datafetcher import DataFetcher, AirQualityReport +from typing import Tuple, Dict, Any, Optional
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src/spatial/views/report_view.py
[warning] 30-32: Information exposure through an exception
Stack trace information flows to this location and may be exposed to an external user.
Stack trace information flows to this location and may be exposed to an external user.
src/spatial/views/report_view.py
Outdated
| @staticmethod | ||
| def _handle_error(exception): | ||
| return jsonify({ | ||
| "error": str(exception) | ||
| }), 500 | ||
|
|
There was a problem hiding this comment.
Secure error handling to prevent information exposure
The current implementation exposes raw exception messages, which could leak sensitive information. Consider implementing a more secure error handling approach.
@staticmethod
- def _handle_error(exception):
+ def _handle_error(exception: Exception) -> Tuple[Any, int]:
+ """Handle exceptions securely.
+
+ Args:
+ exception: The caught exception
+ Returns:
+ Tuple of (response, status_code)
+ """
+ # Log the full error for debugging
+ logging.error(f"Error in report generation: {str(exception)}", exc_info=True)
+
+ # Return a sanitized error message
return jsonify({
- "error": str(exception)
+ "error": "An internal error occurred while processing your request",
+ "request_id": request.headers.get('X-Request-ID', 'unknown')
}), 500Committable suggestion skipped: line range outside the PR's diff.
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[warning] 30-32: Information exposure through an exception
Stack trace information flows to this location and may be exposed to an external user.
Stack trace information flows to this location and may be exposed to an external user.
| @staticmethod | ||
| def generate_air_quality_report_without_llm(): | ||
| """Generate a report without using LLM.""" | ||
| data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data() | ||
| if error_response: | ||
| return error_response | ||
|
|
||
| try: | ||
| # Create an air quality report | ||
| report = AirQualityReport(air_quality_data) | ||
| # Generate the report without LLM | ||
| json_report = report.generate_report_without_llm() | ||
|
|
||
| if json_report is None: | ||
| return jsonify({ | ||
| "error": "Failed to generate report" | ||
| }), 500 | ||
|
|
||
| return jsonify({ | ||
| "report": json_report, | ||
| "model": "rule_based" | ||
| }), 200 | ||
|
|
||
| except Exception as e: | ||
| return ReportView._handle_error(e) |
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🛠️ Refactor suggestion
Reduce code duplication in report generation methods
The non-LLM report generation method shares significant code with the Gemini version. Consider extracting common functionality.
+ @staticmethod
+ def _generate_report(generator_func: callable, model_name: str) -> Tuple[Any, int]:
+ """Generic report generation method.
+
+ Args:
+ generator_func: Function to generate the report
+ model_name: Name of the model being used
+ Returns:
+ Tuple of (response, status_code)
+ """
+ data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data()
+ if error_response:
+ return error_response
+
+ try:
+ with ThreadPoolExecutor() as executor:
+ future = executor.submit(generator_func)
+ try:
+ json_report = future.result(timeout=30)
+ except TimeoutError:
+ return jsonify({
+ "error": f"Report generation with {model_name} timed out",
+ "details": "The request took too long to process"
+ }), 504
+
+ if json_report is None:
+ return jsonify({
+ "error": f"Failed to generate report with {model_name}",
+ "details": "The model failed to generate a valid response"
+ }), 500
+
+ return jsonify({
+ "report": json_report,
+ "model": model_name
+ }), 200
+
+ except Exception as e:
+ return ReportView._handle_error(e)
@staticmethod
def generate_air_quality_report_without_llm():
- """Generate a report without using LLM."""
- data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data()
- if error_response:
- return error_response
-
- try:
- # Create an air quality report
- report = AirQualityReport(air_quality_data)
- # Generate the report without LLM
- json_report = report.generate_report_without_llm()
-
- if json_report is None:
- return jsonify({
- "error": "Failed to generate report"
- }), 500
-
- return jsonify({
- "report": json_report,
- "model": "rule_based"
- }), 200
-
- except Exception as e:
- return ReportView._handle_error(e)
+ """Generate a report without using LLM.
+
+ Returns:
+ Tuple of (response, status_code)
+ """
+ report = AirQualityReport(air_quality_data)
+ return ReportView._generate_report(
+ generator_func=report.generate_report_without_llm,
+ model_name="rule_based"
+ )Committable suggestion skipped: line range outside the PR's diff.
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🧹 Outside diff range and nitpick comments (1)
src/spatial/models/report_datafetcher.py (1)
88-88: Reconsider placement of "include the period under review" in prompts.The phrase "include the period under review" may be redundant or misplaced within the prompts, potentially causing confusion in the generated reports. Ensure that this instruction is necessary and positioned appropriately within the prompt.
Apply this diff to remove or reposition the phrase:
if audience == "researcher": return ( f"Generate a comprehensive air quality assessment report for {self.grid_name} for the period of {self.starttime} to {self.endtime}. Begin with a detailed introduction (100-130 words) covering the city's geographical location, climate characteristics, population density, and major pollution sources.\n" - f"{base_info} include the period under review." + f"{base_info}\n"Make similar adjustments for the other audience types if necessary.
Also applies to: 98-98, 106-106
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src/spatial/models/report_datafetcher.py
3-3: transformers.AutoModelForCausalLM imported but unused
Remove unused import
(F401)
3-3: transformers.AutoTokenizer imported but unused
Remove unused import
(F401)
🔇 Additional comments (4)
src/spatial/models/report_datafetcher.py (4)
24-24: Previous comment on ANALTICS_URL typo is still valid.
The variable ANALTICS_URL appears to be misspelled. It should likely be ANALYTICS_URL. This typo could cause issues when accessing the configuration.
29-29: Previous comment on passing token in URL is still valid.
Including sensitive tokens in URL query parameters is insecure. The token should be passed in the request headers instead.
127-132: 🛠️ Refactor suggestion
Add exception handling for OpenAI API calls.
Interacting with external APIs can lead to unexpected errors. Implement exception handling around the OpenAI API call to manage potential issues gracefully and maintain application stability.
Apply this diff to add error handling:
def generate_report_with_openai(self, audience):
prompt = self._generate_prompt(audience)
+ try:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}]
)
openai_output = response.choices[0].message['content']
return self._prepare_report_json(openai_output)
+ except openai.error.OpenAIError as e:
+ logging.error(f"OpenAI API error: {e}")
+ return self._prepare_report_json("An error occurred while generating the report.")Ensure that the logging module is imported if it's not already.
Likely invalid or redundant comment.
120-123: 🛠️ Refactor suggestion
Add exception handling for Gemini API calls.
To prevent the application from crashing due to unexpected errors with the Gemini API, add exception handling around the API call.
Apply this diff to add error handling:
def generate_report_with_gemini(self, audience):
prompt = self._generate_prompt(audience)
+ try:
response = self.gemini_model.generate_content(prompt)
gemini_output = response.text
return self._prepare_report_json(gemini_output)
+ except Exception as e:
+ logging.error(f"Gemini API error: {e}")
+ return self._prepare_report_json("An error occurred while generating the report.")Likely invalid or redundant comment.
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🧹 Outside diff range and nitpick comments (4)
src/spatial/models/report_datafetcher.py (4)
16-19: Enhance error handling for missing Hugging Face token.Consider using the logging module instead of print statements for better error tracking and consistency.
if hf_token: login(hf_token) else: - print("Hugging Face token is missing. Set the 'HUGGING_FACE_TOKEN' environment variable.") + logging.error("Hugging Face token is missing. Set the 'HUGGING_FACE_TOKEN' environment variable.")
51-65: Consider refactoring data extraction logic.The nested dictionary access with multiple
.get()calls could be simplified using a helper method.+ def _extract_data(self, *keys, default=None): + result = self.data + for key in keys: + if not isinstance(result, dict): + return default + result = result.get(key, default) + return result def __init__(self, data): self.data = data - self.grid_name = data.get('airquality', {}).get('sites', {}).get('grid name', [None]) + self.grid_name = self._extract_data('airquality', 'sites', 'grid name', [None])
104-106: Simplify strings without interpolation.Remove unnecessary f-string prefixes from strings without placeholders.
- f"Diurnal patterns indicate peak pollution levels at {self._format_diurnal_peak()}.\n" - f"Monthly trends reveal fluctuations correlated with seasonal changes.\n" - f"Provide a thorough analysis of spatial and temporal air quality variations, identify pollution hotspots and clean zones, examine seasonal patterns, and assess compliance with WHO guidelines. " + f"Diurnal patterns indicate peak pollution levels at {self._format_diurnal_peak()}.\n" + "Monthly trends reveal fluctuations correlated with seasonal changes.\n" + "Provide a thorough analysis of spatial and temporal air quality variations, identify pollution hotspots and clean zones, examine seasonal patterns, and assess compliance with WHO guidelines. "🧰 Tools
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104-104: f-string without any placeholders
Remove extraneous
fprefix(F541)
105-105: f-string without any placeholders
Remove extraneous
fprefix(F541)
106-106: f-string without any placeholders
Remove extraneous
fprefix(F541)
1-225: Consider implementing a caching mechanism for API responses.Given that this system makes multiple API calls to external services (AirQo API, Gemini, OpenAI), implementing a caching mechanism would:
- Improve response times for frequently requested reports
- Reduce API costs
- Provide resilience against API outages
Consider using Redis or a similar caching solution to store:
- Air quality data responses
- Generated reports with appropriate TTL
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3-3:
transformers.AutoModelForCausalLMimported but unusedRemove unused import
(F401)
3-3:
transformers.AutoTokenizerimported but unusedRemove unused import
(F401)
104-104: f-string without any placeholders
Remove extraneous
fprefix(F541)
105-105: f-string without any placeholders
Remove extraneous
fprefix(F541)
106-106: f-string without any placeholders
Remove extraneous
fprefix(F541)
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src/spatial/models/report_datafetcher.py
3-3: transformers.AutoModelForCausalLM imported but unused
Remove unused import
(F401)
3-3: transformers.AutoTokenizer imported but unused
Remove unused import
(F401)
104-104: f-string without any placeholders
Remove extraneous f prefix
(F541)
105-105: f-string without any placeholders
Remove extraneous f prefix
(F541)
106-106: f-string without any placeholders
Remove extraneous f prefix
(F541)
🔇 Additional comments (2)
src/spatial/models/report_datafetcher.py (2)
33-46: Well-implemented error handling!
The comprehensive error handling with specific exception types and proper logging is a good practice.
132-136: Add error handling for Gemini API calls.
This is a duplicate of a past review comment. The Gemini API calls should include error handling similar to the OpenAI implementation.
| @@ -0,0 +1,235 @@ | |||
| import requests | |||
| import openai | |||
| from transformers import AutoModelForCausalLM, AutoTokenizer | |||
There was a problem hiding this comment.
🛠️ Refactor suggestion
Remove unused imports from the transformers library.
The AutoModelForCausalLM and AutoTokenizer imports are not used in the current implementation.
-from transformers import AutoModelForCausalLM, AutoTokenizer📝 Committable suggestion
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| from transformers import AutoModelForCausalLM, AutoTokenizer |
🧰 Tools
🪛 Ruff
3-3: transformers.AutoModelForCausalLM imported but unused
Remove unused import
(F401)
3-3: transformers.AutoTokenizer imported but unused
Remove unused import
(F401)
| # Initialize models once in the constructor | ||
| self.gemini_model = genai.GenerativeModel('gemini-pro') | ||
| openai.api_key = Config.OPENAI_API_KEY |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for model initialization.
The model initialization could fail silently if API keys are invalid or if there are connection issues.
# Initialize models once in the constructor
- self.gemini_model = genai.GenerativeModel('gemini-pro')
- openai.api_key = Config.OPENAI_API_KEY
+ try:
+ self.gemini_model = genai.GenerativeModel('gemini-pro')
+ openai.api_key = Config.OPENAI_API_KEY
+ except Exception as e:
+ logging.error(f"Failed to initialize AI models: {e}")
+ raise📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| # Initialize models once in the constructor | |
| self.gemini_model = genai.GenerativeModel('gemini-pro') | |
| openai.api_key = Config.OPENAI_API_KEY | |
| # Initialize models once in the constructor | |
| try: | |
| self.gemini_model = genai.GenerativeModel('gemini-pro') | |
| openai.api_key = Config.OPENAI_API_KEY | |
| except Exception as e: | |
| logging.error(f"Failed to initialize AI models: {e}") | |
| raise |
|
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📒 Files selected for processing (1)
src/spatial/models/report_datafetcher.py(1 hunks)
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🪛 Ruff
src/spatial/models/report_datafetcher.py
3-3: transformers.AutoModelForCausalLM imported but unused
Remove unused import
(F401)
3-3: transformers.AutoTokenizer imported but unused
Remove unused import
(F401)
104-104: f-string without any placeholders
Remove extraneous f prefix
(F541)
105-105: f-string without any placeholders
Remove extraneous f prefix
(F541)
106-106: f-string without any placeholders
Remove extraneous f prefix
(F541)
| import requests | ||
| import openai | ||
| from transformers import AutoModelForCausalLM, AutoTokenizer | ||
| from huggingface_hub import login | ||
| from configure import Config | ||
| import google.generativeai as genai | ||
| import logging | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Clean up imports and improve error handling.
- Remove unused imports from the transformers library.
- Use logging consistently instead of print statements for error handling.
Apply this diff:
import requests
import openai
-from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import login
from configure import Config
import google.generativeai as genai
import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| import requests | |
| import openai | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from huggingface_hub import login | |
| from configure import Config | |
| import google.generativeai as genai | |
| import logging | |
| import requests | |
| import openai | |
| from huggingface_hub import login | |
| from configure import Config | |
| import google.generativeai as genai | |
| import logging | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) |
🧰 Tools
🪛 Ruff
3-3: transformers.AutoModelForCausalLM imported but unused
Remove unused import
(F401)
3-3: transformers.AutoTokenizer imported but unused
Remove unused import
(F401)
| # Configure API keys | ||
| GOOGLE_API_KEY = Config.GOOGLE_API_KEY | ||
| genai.configure(api_key=GOOGLE_API_KEY) | ||
| hf_token = Config.HUGGING_FACE_TOKEN | ||
|
|
||
|
|
||
| if hf_token: | ||
| login(hf_token) | ||
| else: | ||
| print("Hugging Face token is missing. Set the 'HUGGING_FACE_TOKEN' environment variable.") | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve API configuration error handling.
The current implementation uses print statements and doesn't properly handle configuration errors.
Apply this diff:
# Configure API keys
-GOOGLE_API_KEY = Config.GOOGLE_API_KEY
-genai.configure(api_key=GOOGLE_API_KEY)
-hf_token = Config.HUGGING_FACE_TOKEN
+try:
+ GOOGLE_API_KEY = Config.GOOGLE_API_KEY
+ if not GOOGLE_API_KEY:
+ raise ValueError("GOOGLE_API_KEY is not set")
+ genai.configure(api_key=GOOGLE_API_KEY)
+ hf_token = Config.HUGGING_FACE_TOKEN
+ if hf_token:
+ login(hf_token)
+ else:
+ logger.warning("Hugging Face token is missing. Set the 'HUGGING_FACE_TOKEN' environment variable.")
+except Exception as e:
+ logger.error(f"Failed to configure API keys: {e}")
+ raise📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| # Configure API keys | |
| GOOGLE_API_KEY = Config.GOOGLE_API_KEY | |
| genai.configure(api_key=GOOGLE_API_KEY) | |
| hf_token = Config.HUGGING_FACE_TOKEN | |
| if hf_token: | |
| login(hf_token) | |
| else: | |
| print("Hugging Face token is missing. Set the 'HUGGING_FACE_TOKEN' environment variable.") | |
| # Configure API keys | |
| try: | |
| GOOGLE_API_KEY = Config.GOOGLE_API_KEY | |
| if not GOOGLE_API_KEY: | |
| raise ValueError("GOOGLE_API_KEY is not set") | |
| genai.configure(api_key=GOOGLE_API_KEY) | |
| hf_token = Config.HUGGING_FACE_TOKEN | |
| if hf_token: | |
| login(hf_token) | |
| else: | |
| logger.warning("Hugging Face token is missing. Set the 'HUGGING_FACE_TOKEN' environment variable.") | |
| except Exception as e: | |
| logger.error(f"Failed to configure API keys: {e}") | |
| raise |
| class AirQualityReport: | ||
| def __init__(self, data): | ||
| self.data = data | ||
| self.grid_name = data.get('airquality', {}).get('sites', {}).get('grid name', [None]) | ||
| self.annual_data = data.get('airquality', {}).get('annual_pm', [None])[0] | ||
| self.daily_mean_data = data.get('airquality', {}).get('daily_mean_pm', []) | ||
| self.diurnal = data.get('airquality', {}).get('diurnal', []) | ||
| self.monthly_data = data.get('airquality', {}).get('site_monthly_mean_pm', []) | ||
| self.monthly_name_data = data.get('airquality', {}).get('pm_by_month_name', []) | ||
| main_site_info = self.monthly_data[0] if self.monthly_data else {} | ||
| self.main_site = main_site_info.get('site_name') | ||
| self.site_names = [item.get('site_name', None) for item in self.data.get('airquality', {}).get('site_annual_mean_pm', [])] | ||
| self.site_latitude = main_site_info.get('site_latitude') | ||
| self.site_longitude = main_site_info.get('site_longitude') | ||
| self.num_sites = data.get('airquality', {}).get('sites', {}).get('number_of_sites') | ||
| self.starttime = data.get('airquality', {}).get('period', {}).get('startTime', '')[:10] | ||
| self.endtime = data.get('airquality', {}).get('period', {}).get('endTime', '')[:10] | ||
|
|
||
| self.annual_pm2_5_calibrated_value = self.annual_data.get("pm2_5_calibrated_value") | ||
| self.annual_pm10_calibrated_value = self.annual_data.get("pm10_calibrated_value") | ||
|
|
||
| # Finding the minimum and maximum values | ||
| if self.daily_mean_data: | ||
| self.daily_min_pm2_5 = min(self.daily_mean_data, key=lambda x: x['pm2_5_calibrated_value']) | ||
| self.daily_max_pm2_5 = max(self.daily_mean_data, key=lambda x: x['pm2_5_calibrated_value']) | ||
| else: | ||
| self.daily_min_pm2_5 = None | ||
| self.daily_max_pm2_5 = None | ||
|
|
||
|
|
||
| # Initialize models once in the constructor | ||
| self.gemini_model = genai.GenerativeModel('gemini-pro') | ||
| openai.api_key = Config.OPENAI_API_KEY |
There was a problem hiding this comment.
Add proper data validation and error handling.
The class initialization needs several improvements:
- Validate data structure before access
- Handle model initialization errors
- Add type hints for better maintainability
Apply this diff:
class AirQualityReport:
+ def __validate_data(self, data):
+ if not isinstance(data, dict):
+ raise ValueError("Input data must be a dictionary")
+ if 'airquality' not in data:
+ raise ValueError("Missing 'airquality' key in data")
+
def __init__(self, data):
+ self.__validate_data(data)
self.data = data
- self.grid_name = data.get('airquality', {}).get('sites', {}).get('grid name', [None])
+ self.grid_name = data.get('airquality', {}).get('sites', {}).get('grid name', ['Unknown'])[0]
self.annual_data = data.get('airquality', {}).get('annual_pm', [{}])[0]
self.daily_mean_data = data.get('airquality', {}).get('daily_mean_pm', [])
self.diurnal = data.get('airquality', {}).get('diurnal', [])
self.monthly_data = data.get('airquality', {}).get('site_monthly_mean_pm', [])
self.monthly_name_data = data.get('airquality', {}).get('pm_by_month_name', [])
main_site_info = self.monthly_data[0] if self.monthly_data else {}
# Initialize models with error handling
try:
self.gemini_model = genai.GenerativeModel('gemini-pro')
openai.api_key = Config.OPENAI_API_KEY
if not openai.api_key:
raise ValueError("OpenAI API key is not set")
except Exception as e:
logger.error(f"Failed to initialize AI models: {e}")
raiseCommittable suggestion skipped: line range outside the PR's diff.
| class DataFetcher: | ||
| @staticmethod | ||
| def fetch_air_quality_data_a(grid_id, start_time, end_time): | ||
| token = Config.AIRQO_API_TOKEN | ||
| analytics_url = Config.ANALTICS_URL | ||
| if token is None: | ||
| print("Error: AIRQO_API_TOKEN environment variable is not set.") | ||
| return None | ||
|
|
||
| url= f"{analytics_url}?token={token}" | ||
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} | ||
|
|
||
| try: | ||
| response = requests.post(url, json=payload) | ||
| response.raise_for_status() | ||
| return response.json() | ||
| except requests.exceptions.HTTPError as http_err: | ||
| print(f"HTTP error occurred: {http_err}") | ||
| logging.error(f"HTTP error occurred: {http_err}") | ||
| except requests.exceptions.RequestException as req_err: | ||
| print(f"Request error occurred: {req_err}") | ||
| logging.error(f"Request error occurred: {req_err}") | ||
| except ValueError as json_err: | ||
| print(f"JSON decoding error: {json_err}") | ||
| logging.error(f"JSON decoding error: {json_err}") | ||
|
|
||
| return None | ||
|
|
There was a problem hiding this comment.
Enhance security and robustness of data fetching.
Several improvements needed for security and reliability:
- Move token from URL to headers
- Add request timeout
- Use consistent logging
Apply this diff:
@staticmethod
def fetch_air_quality_data_a(grid_id, start_time, end_time):
token = Config.AIRQO_API_TOKEN
analytics_url = Config.ANALTICS_URL
if token is None:
- print("Error: AIRQO_API_TOKEN environment variable is not set.")
+ logger.error("AIRQO_API_TOKEN environment variable is not set.")
return None
- url= f"{analytics_url}?token={token}"
+ url = analytics_url
payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time}
+ headers = {"Authorization": f"Bearer {token}"}
try:
- response = requests.post(url, json=payload)
+ response = requests.post(url, json=payload, headers=headers, timeout=30)
response.raise_for_status()
return response.json()
except requests.exceptions.HTTPError as http_err:
- print(f"HTTP error occurred: {http_err}")
logging.error(f"HTTP error occurred: {http_err}")
except requests.exceptions.RequestException as req_err:
- print(f"Request error occurred: {req_err}")
logging.error(f"Request error occurred: {req_err}")
except ValueError as json_err:
- print(f"JSON decoding error: {json_err}")
logging.error(f"JSON decoding error: {json_err}")
return None📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| class DataFetcher: | |
| @staticmethod | |
| def fetch_air_quality_data_a(grid_id, start_time, end_time): | |
| token = Config.AIRQO_API_TOKEN | |
| analytics_url = Config.ANALTICS_URL | |
| if token is None: | |
| print("Error: AIRQO_API_TOKEN environment variable is not set.") | |
| return None | |
| url= f"{analytics_url}?token={token}" | |
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} | |
| try: | |
| response = requests.post(url, json=payload) | |
| response.raise_for_status() | |
| return response.json() | |
| except requests.exceptions.HTTPError as http_err: | |
| print(f"HTTP error occurred: {http_err}") | |
| logging.error(f"HTTP error occurred: {http_err}") | |
| except requests.exceptions.RequestException as req_err: | |
| print(f"Request error occurred: {req_err}") | |
| logging.error(f"Request error occurred: {req_err}") | |
| except ValueError as json_err: | |
| print(f"JSON decoding error: {json_err}") | |
| logging.error(f"JSON decoding error: {json_err}") | |
| return None | |
| class DataFetcher: | |
| @staticmethod | |
| def fetch_air_quality_data_a(grid_id, start_time, end_time): | |
| token = Config.AIRQO_API_TOKEN | |
| analytics_url = Config.ANALTICS_URL | |
| if token is None: | |
| logger.error("AIRQO_API_TOKEN environment variable is not set.") | |
| return None | |
| url = analytics_url | |
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} | |
| headers = {"Authorization": f"Bearer {token}"} | |
| try: | |
| response = requests.post(url, json=payload, headers=headers, timeout=30) | |
| response.raise_for_status() | |
| return response.json() | |
| except requests.exceptions.HTTPError as http_err: | |
| logging.error(f"HTTP error occurred: {http_err}") | |
| except requests.exceptions.RequestException as req_err: | |
| logging.error(f"Request error occurred: {req_err}") | |
| except ValueError as json_err: | |
| logging.error(f"JSON decoding error: {json_err}") | |
| return None |
|
Spatial changes in this PR available for preview here |
1 similar comment
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Spatial changes in this PR available for preview here |
There was a problem hiding this comment.
Actionable comments posted: 7
🧹 Outside diff range and nitpick comments (6)
src/spatial/controllers/controllers.py (1)
72-72: Consider more descriptive function names.The current function names (
fetch_air_quality,fetch_air_quality_without_llm,fetch_air_quality_with_customised_prompt) could be more descriptive about their specific purposes.Suggested names:
-def fetch_air_quality(): +def generate_gemini_air_quality_report(): -def fetch_air_quality_without_llm(): +def generate_basic_air_quality_report(): -def fetch_air_quality_with_customised_prompt(): +def generate_custom_prompt_air_quality_report():Also applies to: 76-76, 80-80
src/spatial/views/report_view.py (2)
1-4: Consider adding type hints for better code maintainabilityAdding type hints would improve code maintainability and IDE support. Consider:
-from flask import request, jsonify +from flask import Request, Response, jsonify +from typing import Dict, Any, Tuple, Optional from models.report_datafetcher import DataFetcher, AirQualityReport
13-13: Remove unused variable assignmentThe
customise_promptvariable is assigned but never used in this method. Since it's used ingenerate_air_quality_report_with_customised_prompt_gemini(), consider moving the assignment there.🧰 Tools
🪛 Ruff (0.8.0)
13-13: Local variable
customise_promptis assigned to but never usedRemove assignment to unused variable
customise_prompt(F841)
src/spatial/models/report_datafetcher.py (3)
106-108: Remove extraneous 'f' prefixes in strings without placeholdersLines 106 to 108 contain f-strings without any placeholders. The
fprefix is unnecessary and can be removed to clean up the code.Apply this diff to remove the extraneous
fprefixes:) - f"Monthly trends reveal fluctuations correlated with seasonal changes.\n" - f"Provide a thorough analysis of spatial and temporal air quality variations, identify pollution hotspots and clean zones, examine seasonal patterns, and assess compliance with WHO guidelines. " - f"Conclude with actionable recommendations for air quality improvement and public health protection. Data source: AirQo monitoring network." + "Monthly trends reveal fluctuations correlated with seasonal changes.\n" + "Provide a thorough analysis of spatial and temporal air quality variations, identify pollution hotspots and clean zones, examine seasonal patterns, and assess compliance with WHO guidelines. " + "Conclude with actionable recommendations for air quality improvement and public health protection. Data source: AirQo monitoring network." )🧰 Tools
🪛 Ruff (0.8.0)
106-106: f-string without any placeholders
Remove extraneous
fprefix(F541)
107-107: f-string without any placeholders
Remove extraneous
fprefix(F541)
108-108: f-string without any placeholders
Remove extraneous
fprefix(F541)
212-213: Handle 'None' values in report content to avoid confusing outputsVariables like
least_pm2_5_timeandleast_pm2_5might beNonewhen there's insufficient data. Including them in the report without checking can lead to outputs like "None :00 hr" or "PM2.5 None µg/m³ levels," which could be misleading.Consider adding conditional statements to handle
Nonevalues:if peak_pm2_5 and peak_time: peak_info = f"The peak in PM2.5 levels ({peak_pm2_5} µg/m³) occurs around {peak_time}:00 hr, indicating a period of higher pollution, often associated with increased activity or traffic." else: peak_info = "Peak PM2.5 levels could not be determined due to insufficient data." if least_pm2_5 and least_pm2_5_time: least_info = f"Conversely, the period with the least PM2.5 levels ({least_pm2_5} µg/m³) is around {least_pm2_5_time}:00 hr, usually representing a period of lower activity or better atmospheric dispersion." else: least_info = "Least PM2.5 levels could not be determined due to insufficient data." diurnal_description = ( f"Diurnal patterns observed include the following: {diurnal_patterns}. " f"These patterns provide insight into air quality fluctuations throughout the day. " f"{peak_info} " f"{least_info} " "Understanding the patterns of pollution and their impacts on public health is crucial for effective environmental management and policy-making. " "Throughout this report, we will explore key trends in PM2.5 and PM10 concentrations, the diurnal variations, and the impact of these levels on air quality across the region." )
141-175: Use logging instead of print statements for error messagesThroughout the report generation methods,
loggingmodule provides better control over logging levels and integrates well with production environments.Apply this diff to replace
def generate_report_with_gemini(self, audience): prompt = self._generate_prompt(audience) try: response = self.gemini_model.generate_content(prompt) gemini_output = response.text return self._prepare_report_json(gemini_output) except Exception as e: - print(f"Error: {e}") + logging.error(f"Error generating report with Gemini: {e}") return None # Generate report with customised prompt def generate_report_with_customised_prompt_gemini(self, custom_prompt): # [code omitted for brevity] try: response = self.gemini_model.generate_content(full_prompt) gemini_output = response.text return self._prepare_customised_report_json(gemini_output) except Exception as e: - print(f"Error: {e}") + logging.error(f"Error generating customised report with Gemini: {e}") return None def generate_report_with_openai(self, audience): prompt = self._generate_prompt(audience) try: response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": prompt}] ) openai_output = response.choices[0].message['content'] return self._prepare_report_json(openai_output) except Exception as e: - print(f"Error: {e}") + logging.error(f"Error generating report with OpenAI: {e}") return None
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (3)
src/spatial/controllers/controllers.py(2 hunks)src/spatial/models/report_datafetcher.py(1 hunks)src/spatial/views/report_view.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.0)
src/spatial/models/report_datafetcher.py
3-3: transformers.AutoModelForCausalLM imported but unused
Remove unused import
(F401)
3-3: transformers.AutoTokenizer imported but unused
Remove unused import
(F401)
106-106: f-string without any placeholders
Remove extraneous f prefix
(F541)
107-107: f-string without any placeholders
Remove extraneous f prefix
(F541)
108-108: f-string without any placeholders
Remove extraneous f prefix
(F541)
src/spatial/views/report_view.py
13-13: Local variable customise_prompt is assigned to but never used
Remove assignment to unused variable customise_prompt
(F841)
🔇 Additional comments (2)
src/spatial/controllers/controllers.py (2)
15-15: LGTM! Clean import addition.
The import statement follows the established pattern and is appropriately placed with other view imports.
71-81: Verify the integration with external services.
The routes interact with multiple external services (Gemini, LLaMA) and data sources. Let's verify the configuration and dependencies are properly set up.
| @controller_bp.route("/air_quality_report", methods=["POST"]) | ||
| def fetch_air_quality(): | ||
| return ReportView.generate_air_quality_report_with_gemini() | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider enhancing the route implementations with proper error handling and documentation.
While the implementations are concise, there are several opportunities for improvement:
- Add error handling to gracefully handle potential exceptions from the report generation process
- Include request validation to ensure required parameters are present
- Add API documentation to describe the expected request format and response structure
Here's a suggested implementation pattern:
@controller_bp.route("/air_quality_report", methods=["POST"])
def fetch_air_quality():
- return ReportView.generate_air_quality_report_with_gemini()
+ """Generate air quality report using Gemini model.
+
+ Expected payload:
+ {
+ "grid_id": str,
+ "start_time": str,
+ "end_time": str
+ }
+ """
+ try:
+ return ReportView.generate_air_quality_report_with_gemini()
+ except Exception as e:
+ return jsonify({"error": str(e)}), 500Apply similar patterns to the other two routes as well.
Also applies to: 75-78, 79-81
| def _fetch_and_validate_request_data(): | ||
| """Extract and validate request data.""" | ||
| data = request.json | ||
| grid_id = data.get("grid_id") | ||
| start_time = data.get("start_time") | ||
| end_time = data.get("end_time") | ||
| audience = data.get("audience", "general public") | ||
| customise_prompt = data.get("customise_prompt", "") # Optional customization | ||
|
|
||
| # Validate input parameters | ||
| if not all([grid_id, start_time, end_time, audience]): | ||
| return None, jsonify({ | ||
| "error": "Missing required parameters: grid_id, start_time, end_time, audience" | ||
| }), 400 | ||
|
|
||
| # Fetch air quality data | ||
| air_quality_data = DataFetcher.fetch_air_quality_data_a(grid_id, start_time, end_time) | ||
| if air_quality_data is None: | ||
| return None, jsonify({ | ||
| "error": "No data found for the given parameters" | ||
| }), 404 | ||
|
|
||
| return data, air_quality_data, None |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance input validation and return type clarity
The current implementation could benefit from more robust input validation and clearer return types:
- Add type hints and improve docstring
- Validate input formats (e.g., date formats, grid_id format)
- Return a TypedDict instead of a tuple for better type safety
@staticmethod
- def _fetch_and_validate_request_data():
+ def _fetch_and_validate_request_data() -> Tuple[Optional[Dict[str, Any]], Optional[Dict[str, Any]], Optional[Response]]:
"""Extract and validate request data.
Returns:
Tuple[Optional[Dict], Optional[Dict], Optional[Response]]: Contains
- Request data dictionary
- Air quality data dictionary
- Error response (if validation fails)
"""
data = request.json
+ if not isinstance(data, dict):
+ return None, None, jsonify({"error": "Invalid JSON payload"}), 400Committable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff (0.8.0)
13-13: Local variable customise_prompt is assigned to but never used
Remove assignment to unused variable customise_prompt
(F841)
| def _handle_error(exception): | ||
| return jsonify({ | ||
| # "error": str(exception) | ||
| "error": "An internal error has occurred!" | ||
| }), 500 |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance error handling with secure logging
While hiding exception details from responses is good for security, we should log them for debugging:
@staticmethod
- def _handle_error(exception):
+ def _handle_error(exception: Exception) -> Tuple[Response, int]:
+ """Handle exceptions securely while preserving debug information."""
+ # Log the full error for debugging
+ current_app.logger.error(
+ f"Error in report generation: {str(exception)}",
+ exc_info=True,
+ extra={
+ "request_id": request.headers.get("X-Request-ID", "unknown")
+ }
+ )
return jsonify({
"error": "An internal error has occurred!",
+ "request_id": request.headers.get("X-Request-ID", "unknown")
}), 500📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def _handle_error(exception): | |
| return jsonify({ | |
| # "error": str(exception) | |
| "error": "An internal error has occurred!" | |
| }), 500 | |
| @staticmethod | |
| def _handle_error(exception: Exception) -> Tuple[Response, int]: | |
| """Handle exceptions securely while preserving debug information.""" | |
| # Log the full error for debugging | |
| current_app.logger.error( | |
| f"Error in report generation: {str(exception)}", | |
| exc_info=True, | |
| extra={ | |
| "request_id": request.headers.get("X-Request-ID", "unknown") | |
| } | |
| ) | |
| return jsonify({ | |
| "error": "An internal error has occurred!", | |
| "request_id": request.headers.get("X-Request-ID", "unknown") | |
| }), 500 |
| @staticmethod | ||
| def generate_air_quality_report_with_gemini(): | ||
| """Generate a report using Gemini model.""" | ||
| data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data() | ||
| if error_response: | ||
| return error_response | ||
|
|
||
| try: | ||
| # Create an air quality report | ||
| report = AirQualityReport(air_quality_data) | ||
| # Generate the report with Gemini | ||
| json_report = report.generate_report_with_gemini(data.get("audience", "general public")) | ||
|
|
||
| if json_report is None: | ||
| return jsonify({ | ||
| "error": "Failed to generate report with Gemini" | ||
| }), 500 | ||
|
|
||
| return jsonify({ | ||
| "report": json_report, | ||
| "model": "gemini" | ||
| }), 200 | ||
|
|
||
| except Exception as e: | ||
| return ReportView._handle_error(e) | ||
|
|
||
| @staticmethod | ||
| def generate_air_quality_report_without_llm(): | ||
| """Generate a report without using LLM.""" | ||
| data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data() | ||
| if error_response: | ||
| return error_response | ||
|
|
||
| try: | ||
| # Create an air quality report | ||
| report = AirQualityReport(air_quality_data) | ||
| # Generate the report without LLM | ||
| json_report = report.generate_report_without_llm() | ||
|
|
||
| if json_report is None: | ||
| return jsonify({ | ||
| "error": "Failed to generate report" | ||
| }), 500 | ||
|
|
||
| return jsonify({ | ||
| "report": json_report, | ||
| "model": "rule_based" | ||
| }), 200 | ||
|
|
||
| except Exception as e: | ||
| return ReportView._handle_error(e) | ||
|
|
||
|
|
||
| @staticmethod | ||
| def generate_air_quality_report_with_customised_prompt_gemini(): | ||
| data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data() | ||
| if error_response: | ||
| return error_response | ||
|
|
||
| try: | ||
| # Create an air quality report | ||
| report = AirQualityReport(air_quality_data) | ||
| # Generate the report with Gemini | ||
| json_report = report.generate_report_with_customised_prompt_gemini(data.get("customise_prompt", "")) | ||
|
|
||
| if json_report is None: | ||
| return jsonify({ | ||
| "error": "Failed to generate report with Gemini" | ||
| }), 500 | ||
|
|
||
| return jsonify({ | ||
| "report": json_report, | ||
| "model": "gemini" | ||
| }), 200 | ||
|
|
||
| except Exception as e: | ||
| return ReportView._handle_error(e) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Reduce code duplication in report generation methods
The three report generation methods share significant code structure. Consider extracting the common pattern:
+ @staticmethod
+ def _generate_report(
+ generator_func: callable,
+ model_name: str,
+ audience: Optional[str] = None
+ ) -> Tuple[Response, int]:
+ """Generic report generation method.
+
+ Args:
+ generator_func: Function to generate the report
+ model_name: Name of the model being used
+ audience: Optional audience parameter
+ """
+ data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data()
+ if error_response:
+ return error_response
+
+ try:
+ report = AirQualityReport(air_quality_data)
+ json_report = generator_func(report, data)
+
+ if json_report is None:
+ return jsonify({
+ "error": f"Failed to generate report with {model_name}"
+ }), 500
+
+ return jsonify({
+ "report": json_report,
+ "model": model_name
+ }), 200
+
+ except Exception as e:
+ return ReportView._handle_error(e)Then simplify the existing methods:
@staticmethod
def generate_air_quality_report_with_gemini():
"""Generate a report using Gemini model."""
- data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data()
- if error_response:
- return error_response
-
- try:
- report = AirQualityReport(air_quality_data)
- json_report = report.generate_report_with_gemini(data.get("audience", "general public"))
-
- if json_report is None:
- return jsonify({
- "error": "Failed to generate report with Gemini"
- }), 500
-
- return jsonify({
- "report": json_report,
- "model": "gemini"
- }), 200
-
- except Exception as e:
- return ReportView._handle_error(e)
+ return ReportView._generate_report(
+ lambda r, d: r.generate_report_with_gemini(d.get("audience", "general public")),
+ "gemini"
+ )📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| @staticmethod | |
| def generate_air_quality_report_with_gemini(): | |
| """Generate a report using Gemini model.""" | |
| data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data() | |
| if error_response: | |
| return error_response | |
| try: | |
| # Create an air quality report | |
| report = AirQualityReport(air_quality_data) | |
| # Generate the report with Gemini | |
| json_report = report.generate_report_with_gemini(data.get("audience", "general public")) | |
| if json_report is None: | |
| return jsonify({ | |
| "error": "Failed to generate report with Gemini" | |
| }), 500 | |
| return jsonify({ | |
| "report": json_report, | |
| "model": "gemini" | |
| }), 200 | |
| except Exception as e: | |
| return ReportView._handle_error(e) | |
| @staticmethod | |
| def generate_air_quality_report_without_llm(): | |
| """Generate a report without using LLM.""" | |
| data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data() | |
| if error_response: | |
| return error_response | |
| try: | |
| # Create an air quality report | |
| report = AirQualityReport(air_quality_data) | |
| # Generate the report without LLM | |
| json_report = report.generate_report_without_llm() | |
| if json_report is None: | |
| return jsonify({ | |
| "error": "Failed to generate report" | |
| }), 500 | |
| return jsonify({ | |
| "report": json_report, | |
| "model": "rule_based" | |
| }), 200 | |
| except Exception as e: | |
| return ReportView._handle_error(e) | |
| @staticmethod | |
| def generate_air_quality_report_with_customised_prompt_gemini(): | |
| data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data() | |
| if error_response: | |
| return error_response | |
| try: | |
| # Create an air quality report | |
| report = AirQualityReport(air_quality_data) | |
| # Generate the report with Gemini | |
| json_report = report.generate_report_with_customised_prompt_gemini(data.get("customise_prompt", "")) | |
| if json_report is None: | |
| return jsonify({ | |
| "error": "Failed to generate report with Gemini" | |
| }), 500 | |
| return jsonify({ | |
| "report": json_report, | |
| "model": "gemini" | |
| }), 200 | |
| except Exception as e: | |
| return ReportView._handle_error(e) | |
| @staticmethod | |
| def _generate_report( | |
| generator_func: callable, | |
| model_name: str, | |
| audience: Optional[str] = None | |
| ) -> Tuple[Response, int]: | |
| """Generic report generation method. | |
| Args: | |
| generator_func: Function to generate the report | |
| model_name: Name of the model being used | |
| audience: Optional audience parameter | |
| """ | |
| data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data() | |
| if error_response: | |
| return error_response | |
| try: | |
| report = AirQualityReport(air_quality_data) | |
| json_report = generator_func(report, data) | |
| if json_report is None: | |
| return jsonify({ | |
| "error": f"Failed to generate report with {model_name}" | |
| }), 500 | |
| return jsonify({ | |
| "report": json_report, | |
| "model": model_name | |
| }), 200 | |
| except Exception as e: | |
| return ReportView._handle_error(e) | |
| @staticmethod | |
| def generate_air_quality_report_with_gemini(): | |
| """Generate a report using Gemini model.""" | |
| return ReportView._generate_report( | |
| lambda r, d: r.generate_report_with_gemini(d.get("audience", "general public")), | |
| "gemini" | |
| ) | |
| @staticmethod | |
| def generate_air_quality_report_without_llm(): | |
| """Generate a report without using LLM.""" | |
| data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data() | |
| if error_response: | |
| return error_response | |
| try: | |
| # Create an air quality report | |
| report = AirQualityReport(air_quality_data) | |
| # Generate the report without LLM | |
| json_report = report.generate_report_without_llm() | |
| if json_report is None: | |
| return jsonify({ | |
| "error": "Failed to generate report" | |
| }), 500 | |
| return jsonify({ | |
| "report": json_report, | |
| "model": "rule_based" | |
| }), 200 | |
| except Exception as e: | |
| return ReportView._handle_error(e) | |
| @staticmethod | |
| def generate_air_quality_report_with_customised_prompt_gemini(): | |
| data, air_quality_data, error_response = ReportView._fetch_and_validate_request_data() | |
| if error_response: | |
| return error_response | |
| try: | |
| # Create an air quality report | |
| report = AirQualityReport(air_quality_data) | |
| # Generate the report with Gemini | |
| json_report = report.generate_report_with_customised_prompt_gemini(data.get("customise_prompt", "")) | |
| if json_report is None: | |
| return jsonify({ | |
| "error": "Failed to generate report with Gemini" | |
| }), 500 | |
| return jsonify({ | |
| "report": json_report, | |
| "model": "gemini" | |
| }), 200 | |
| except Exception as e: | |
| return ReportView._handle_error(e) |
| def _generate_prompt(self, audience): | ||
| base_info = self._prepare_base_info() | ||
| if audience == "researcher": | ||
| return ( | ||
| - f"Generate a comprehensive air quality assessment report for {self.grid_name} for the period of {self.starttime} to {self.endtime}. Begin with a detailed introduction (100-130 words) covering the city's geographical location, climate characteristics, population density, and major pollution sources. " | ||
| - f"{base_info} include the period under review." | ||
| - f"Daily mean measurements show: {self.daily_mean_data}. " | ||
| - f"Diurnal patterns indicate: {self.diurnal}. Monthly trends reveal: {self.monthly_data}. " | ||
| + f"Generate a comprehensive air quality assessment report for {self.grid_name} for the period of {self.starttime} to {self.endtime}. Begin with a detailed introduction (100-130 words) covering the city's geographical location, climate characteristics, population density, and major pollution sources.\n" | ||
| + f"{base_info}\n" | ||
| + f"Daily mean measurements show values ranging from {self.daily_min_pm2_5['pm2_5_calibrated_value']} to {self.daily_max_pm2_5['pm2_5_calibrated_value']} µg/m³.\n" | ||
| + f"Diurnal patterns indicate peak pollution levels at {self._format_diurnal_peak()}.\n" | ||
| + f"Monthly trends reveal fluctuations correlated with seasonal changes.\n" | ||
| f"Provide a thorough analysis of spatial and temporal air quality variations, identify pollution hotspots and clean zones, examine seasonal patterns, and assess compliance with WHO guidelines. " | ||
| f"Conclude with actionable recommendations for air quality improvement and public health protection. Data source: AirQo monitoring network." | ||
| ) | ||
|
|
||
| elif audience == "policymaker": | ||
| return ( | ||
| f"Create an executive summary of air quality conditions in {self.grid_name} for the period of {self.starttime} to {self.endtime}. for policy decision-making. Begin with key findings and their policy implications (50-75 words). " | ||
| f"{base_info} include the period under review." | ||
| f"Highlight critical trends: {self.monthly_data}. Diurnal patterns indicate: {self.diurnal}. " | ||
| f"Focus on: 1) Areas exceeding air quality standards, 2) Population exposure risk assessment, " | ||
| f"3) Economic implications of poor air quality. Present clear, actionable policy recommendations with expected outcomes and implementation timeframes. " | ||
| f"Include cost-benefit considerations and potential regulatory measures. Data source: AirQo monitoring network." | ||
| ) | ||
| elif audience == "general public": | ||
| return ( | ||
| f"{base_info} include the period under review." | ||
| f"Create a clear, easy-to-understand report about air quality in {self.grid_name} for the period of {self.starttime} to {self.endtime}. Start with a simple explanation of why air quality matters for public health. " | ||
| f"We have {self.num_sites} air quality monitors in your area. The average PM2.5 level this year is {self.annual_pm2_5_calibrated_value} µg/m³. " | ||
| f"Diurnal patterns indicate: {self.diurnal}. Monthly trends reveal: {self.monthly_data}. " | ||
| f"Explain what these numbers mean for daily activities. Include: 1) When air quality is best and worst during the day, " | ||
| f"2) Which areas have better or worse air quality, 3) Simple steps people can take to protect their health, " | ||
| f"4) How to access daily air quality updates. Use plain language and avoid technical terms. " | ||
| f"Add practical tips for reducing exposure to air pollution. Data source: AirQo monitoring network." | ||
| ) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Format data before including in prompts to improve readability
Including raw data structures like lists and dictionaries directly in the prompts may result in less coherent or unreadable text in the generated reports. It's advisable to format the data into human-readable strings before incorporating them into the prompts.
For example, transform self.daily_mean_data and self.diurnal into summarized strings:
# Format daily mean data
daily_means = ', '.join([f"{item['date']}: {item['pm2_5_calibrated_value']} µg/m³" for item in self.daily_mean_data if 'date' in item and 'pm2_5_calibrated_value' in item])
# Format diurnal patterns
diurnal_patterns = ', '.join([f"{item['hour']}h: {item['pm2_5_calibrated_value']} µg/m³" for item in self.diurnal if 'hour' in item and 'pm2_5_calibrated_value' in item])Then update the prompts:
if audience == "researcher":
return (
f"Generate a comprehensive air quality assessment report for {self.grid_name} for the period of {self.starttime} to {self.endtime}. Begin with a detailed introduction (100-130 words) covering the city's geographical location, climate characteristics, population density, and major pollution sources.\n"
f"{base_info}\n"
- f"Daily mean measurements show values ranging from {self.daily_min_pm2_5['pm2_5_calibrated_value']} to {self.daily_max_pm2_5['pm2_5_calibrated_value']} µg/m³.\n"
- f"Diurnal patterns indicate peak pollution levels at {self._format_diurnal_peak()}.\n"
+ f"Daily mean measurements: {daily_means}.\n"
+ f"Diurnal patterns: {diurnal_patterns}.\n"
f"Monthly trends reveal fluctuations correlated with seasonal changes.\n"
"Provide a thorough analysis of spatial and temporal air quality variations, identify pollution hotspots and clean zones, examine seasonal patterns, and assess compliance with WHO guidelines. "
"Conclude with actionable recommendations for air quality improvement and public health protection. Data source: AirQo monitoring network."
) 🧰 Tools
🪛 Ruff (0.8.0)
106-106: f-string without any placeholders
Remove extraneous f prefix
(F541)
107-107: f-string without any placeholders
Remove extraneous f prefix
(F541)
108-108: f-string without any placeholders
Remove extraneous f prefix
(F541)
| if self.daily_mean_data: | ||
| self.daily_min_pm2_5 = min(self.daily_mean_data, key=lambda x: x['pm2_5_calibrated_value']) | ||
| self.daily_max_pm2_5 = max(self.daily_mean_data, key=lambda x: x['pm2_5_calibrated_value']) | ||
| else: | ||
| self.daily_min_pm2_5 = None | ||
| self.daily_max_pm2_5 = None | ||
|
|
There was a problem hiding this comment.
Add error handling for missing 'pm2_5_calibrated_value' in daily mean data
When calculating daily_min_pm2_5 and daily_max_pm2_5, there is a potential for a KeyError if any item in self.daily_mean_data lacks the 'pm2_5_calibrated_value' key. To prevent this, introduce error handling or data validation to ensure the key exists before performing the calculations.
Apply the following changes to add a safe extraction of the values:
if self.daily_mean_data:
- self.daily_min_pm2_5 = min(self.daily_mean_data, key=lambda x: x['pm2_5_calibrated_value'])
- self.daily_max_pm2_5 = max(self.daily_mean_data, key=lambda x: x['pm2_5_calibrated_value'])
+ filtered_data = [item for item in self.daily_mean_data if 'pm2_5_calibrated_value' in item]
+ if filtered_data:
+ self.daily_min_pm2_5 = min(filtered_data, key=lambda x: x['pm2_5_calibrated_value'])
+ self.daily_max_pm2_5 = max(filtered_data, key=lambda x: x['pm2_5_calibrated_value'])
+ else:
+ self.daily_min_pm2_5 = None
+ self.daily_max_pm2_5 = None
else:
self.daily_min_pm2_5 = None
self.daily_max_pm2_5 = None📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| if self.daily_mean_data: | |
| self.daily_min_pm2_5 = min(self.daily_mean_data, key=lambda x: x['pm2_5_calibrated_value']) | |
| self.daily_max_pm2_5 = max(self.daily_mean_data, key=lambda x: x['pm2_5_calibrated_value']) | |
| else: | |
| self.daily_min_pm2_5 = None | |
| self.daily_max_pm2_5 = None | |
| if self.daily_mean_data: | |
| filtered_data = [item for item in self.daily_mean_data if 'pm2_5_calibrated_value' in item] | |
| if filtered_data: | |
| self.daily_min_pm2_5 = min(filtered_data, key=lambda x: x['pm2_5_calibrated_value']) | |
| self.daily_max_pm2_5 = max(filtered_data, key=lambda x: x['pm2_5_calibrated_value']) | |
| else: | |
| self.daily_min_pm2_5 = None | |
| self.daily_max_pm2_5 = None | |
| else: | |
| self.daily_min_pm2_5 = None | |
| self.daily_max_pm2_5 = None |
|
Spatial changes in this PR available for preview here |
There was a problem hiding this comment.
Actionable comments posted: 5
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (1)
src/spatial/models/report_datafetcher.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.0)
src/spatial/models/report_datafetcher.py
3-3: transformers.AutoModelForCausalLM imported but unused
Remove unused import
(F401)
3-3: transformers.AutoTokenizer imported but unused
Remove unused import
(F401)
116-116: f-string without any placeholders
Remove extraneous f prefix
(F541)
117-117: f-string without any placeholders
Remove extraneous f prefix
(F541)
118-118: f-string without any placeholders
Remove extraneous f prefix
(F541)
154-154: Use of functools.lru_cache or functools.cache on methods can lead to memory leaks
(B019)
🔇 Additional comments (1)
src/spatial/models/report_datafetcher.py (1)
11-21: 🛠️ Refactor suggestion
Improve API configuration error handling and security.
The current implementation lacks proper error handling for API configuration and uses print statements instead of logging.
# Configure API keys
-GOOGLE_API_KEY = Config.GOOGLE_API_KEY
-genai.configure(api_key=GOOGLE_API_KEY)
-hf_token = Config.HUGGING_FACE_TOKEN
+try:
+ GOOGLE_API_KEY = Config.GOOGLE_API_KEY
+ if not GOOGLE_API_KEY:
+ raise ValueError("GOOGLE_API_KEY is not set")
+ genai.configure(api_key=GOOGLE_API_KEY)
+
+ hf_token = Config.HUGGING_FACE_TOKEN
+ if not hf_token:
+ logger.warning("HUGGING_FACE_TOKEN is not set")
+ else:
+ login(hf_token)
+except Exception as e:
+ logger.error(f"Failed to configure API keys: {e}")
+ raiseLikely invalid or redundant comment.
| + f"Generate a comprehensive air quality assessment report for {self.grid_name} for the period of {self.starttime} to {self.endtime}. Begin with a detailed introduction (100-130 words) covering the city's geographical location, climate characteristics, population density, and major pollution sources.\n" | ||
| + f"{base_info}\n" | ||
| + f"Daily mean measurements show values ranging from {self.daily_min_pm2_5['pm2_5_calibrated_value']} to {self.daily_max_pm2_5['pm2_5_calibrated_value']} µg/m³.\n" | ||
| + f"Diurnal patterns indicate peak pollution levels at {self._format_diurnal_peak()}.\n" |
There was a problem hiding this comment.
Define missing method _format_diurnal_peak().
The method _format_diurnal_peak() is called but not defined in the class.
Add the following implementation:
def _format_diurnal_peak(self):
"""Format the diurnal peak information."""
if not self.diurnal:
return "N/A"
try:
peak_data = max(self.diurnal, key=lambda x: x['pm2_5_calibrated_value'])
return f"{peak_data['hour']}:00 hr with {peak_data['pm2_5_calibrated_value']} µg/m³"
except (KeyError, ValueError) as e:
logger.error(f"Error formatting diurnal peak: {e}")
return "N/A"| import requests | ||
| import openai | ||
| from transformers import AutoModelForCausalLM, AutoTokenizer | ||
| from huggingface_hub import login | ||
| from configure import Config | ||
| import google.generativeai as genai | ||
| import logging | ||
| from functools import lru_cache |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Clean up imports and configure logging properly.
- Remove unused imports from the transformers library
- Add logging configuration
import requests
import openai
-from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import login
from configure import Config
import google.generativeai as genai
import logging
from functools import lru_cache
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| import requests | |
| import openai | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from huggingface_hub import login | |
| from configure import Config | |
| import google.generativeai as genai | |
| import logging | |
| from functools import lru_cache | |
| import requests | |
| import openai | |
| from huggingface_hub import login | |
| from configure import Config | |
| import google.generativeai as genai | |
| import logging | |
| from functools import lru_cache | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) |
🧰 Tools
🪛 Ruff (0.8.0)
3-3: transformers.AutoModelForCausalLM imported but unused
Remove unused import
(F401)
3-3: transformers.AutoTokenizer imported but unused
Remove unused import
(F401)
| class AirQualityReport: | ||
| def __init__(self, data): | ||
| self.data = data | ||
| self.grid_name = data.get('airquality', {}).get('sites', {}).get('grid name', [None]) | ||
| self.annual_data = data.get('airquality', {}).get('annual_pm', [None])[0] | ||
| self.daily_mean_data = data.get('airquality', {}).get('daily_mean_pm', []) | ||
| self.diurnal = data.get('airquality', {}).get('diurnal', []) | ||
| self.monthly_data = data.get('airquality', {}).get('site_monthly_mean_pm', []) | ||
| self.monthly_name_data = data.get('airquality', {}).get('pm_by_month_name', []) | ||
| self.site_annual_mean_pm = data.get('airquality', {}).get('site_annual_mean_pm', []) | ||
| self.site_mean_pm = data.get('airquality', {}).get('site_mean_pm', []) | ||
| main_site_info = self.monthly_data[0] if self.monthly_data else {} | ||
| self.main_site = main_site_info.get('site_name') | ||
| self.site_names = [item.get('site_name', None) for item in self.data.get('airquality', {}).get('site_annual_mean_pm', [])] | ||
| self.site_latitude = main_site_info.get('site_latitude') | ||
| self.site_longitude = main_site_info.get('site_longitude') | ||
| self.num_sites = data.get('airquality', {}).get('sites', {}).get('number_of_sites') | ||
| self.starttime = data.get('airquality', {}).get('period', {}).get('startTime', '')[:10] | ||
| self.endtime = data.get('airquality', {}).get('period', {}).get('endTime', '')[:10] | ||
|
|
||
| self.annual_pm2_5_calibrated_value = self.annual_data.get("pm2_5_calibrated_value") | ||
| self.annual_pm10_calibrated_value = self.annual_data.get("pm10_calibrated_value") | ||
|
|
||
| # Finding the minimum and maximum values | ||
| if self.daily_mean_data: | ||
| filtered_data = [ | ||
| item for item in self.daily_mean_data | ||
| if 'pm2_5_calibrated_value' in item and isinstance(item['pm2_5_calibrated_value'], (int, float)) | ||
| ] | ||
| if filtered_data: | ||
| self.daily_min_pm2_5 = min(filtered_data, key=lambda x: x['pm2_5_calibrated_value']) | ||
| self.daily_max_pm2_5 = max(filtered_data, key=lambda x: x['pm2_5_calibrated_value']) | ||
| else: | ||
| self.daily_min_pm2_5 = None | ||
| self.daily_max_pm2_5 = None | ||
| else: | ||
| self.daily_min_pm2_5 = None | ||
| self.daily_max_pm2_5 = None | ||
|
|
||
|
|
||
| # Initialize models once in the constructor | ||
| self.gemini_model = genai.GenerativeModel('gemini-pro') | ||
| openai.api_key = Config.OPENAI_API_KEY | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add proper data validation and error handling in initialization.
The class initialization needs several improvements:
- Validate input data structure
- Handle potential KeyErrors
- Add error handling for model initialization
+ def __validate_data(self, data: dict) -> None:
+ if not isinstance(data, dict):
+ raise ValueError("Input data must be a dictionary")
+ if 'airquality' not in data:
+ raise ValueError("Missing 'airquality' key in data")
def __init__(self, data):
+ self.__validate_data(data)
self.data = data
- self.grid_name = data.get('airquality', {}).get('sites', {}).get('grid name', [None])
+ self.grid_name = data.get('airquality', {}).get('sites', {}).get('grid name', ['Unknown'])[0]
# Initialize models with error handling
+ try:
self.gemini_model = genai.GenerativeModel('gemini-pro')
openai.api_key = Config.OPENAI_API_KEY
+ if not openai.api_key:
+ raise ValueError("OpenAI API key is not set")
+ except Exception as e:
+ logger.error(f"Failed to initialize AI models: {e}")
+ raiseCommittable suggestion skipped: line range outside the PR's diff.
| @lru_cache(maxsize=64) # Cache up to 64 most recent reports | ||
| def generate_report_with_customised_prompt_gemini(self, custom_prompt): | ||
| """ | ||
| Generate an air quality report using a customised user-provided prompt. | ||
| """ | ||
| base_info = self._prepare_base_info() | ||
| full_prompt = ( | ||
|
|
||
| f"{base_info} include the period under review." | ||
| f"diurnal patterns indicate: {self.diurnal}. " | ||
| f"number of sites or devices or airqo binos: {self.num_sites}. " | ||
| f"{self.daily_mean_data}" | ||
| f"site mean{self.site_mean_pm}" | ||
| f" daily {self.daily_mean_data}" | ||
| f"{custom_prompt}" | ||
| ) | ||
| try: | ||
| response = self.gemini_model.generate_content(full_prompt) | ||
| gemini_output = response.text | ||
| return self._prepare_customised_report_json(gemini_output) | ||
| except Exception as e: | ||
| print(f"Error: {e}") | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve error handling and caching in report generation.
- Using
lru_cacheon methods can lead to memory leaks - Error handling needs improvement
- @lru_cache(maxsize=64) # Cache up to 64 most recent reports
def generate_report_with_customised_prompt_gemini(self, custom_prompt):
"""
Generate an air quality report using a customised user-provided prompt.
"""
base_info = self._prepare_base_info()
full_prompt = (
f"{base_info} include the period under review."
f"diurnal patterns indicate: {self.diurnal}. "
f"number of sites or devices or airqo binos: {self.num_sites}. "
f"{custom_prompt}"
)
try:
response = self.gemini_model.generate_content(full_prompt)
gemini_output = response.text
return self._prepare_customised_report_json(gemini_output)
except Exception as e:
- print(f"Error: {e}")
+ logger.error(f"Failed to generate report: {e}")
+ return None📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| @lru_cache(maxsize=64) # Cache up to 64 most recent reports | |
| def generate_report_with_customised_prompt_gemini(self, custom_prompt): | |
| """ | |
| Generate an air quality report using a customised user-provided prompt. | |
| """ | |
| base_info = self._prepare_base_info() | |
| full_prompt = ( | |
| f"{base_info} include the period under review." | |
| f"diurnal patterns indicate: {self.diurnal}. " | |
| f"number of sites or devices or airqo binos: {self.num_sites}. " | |
| f"{self.daily_mean_data}" | |
| f"site mean{self.site_mean_pm}" | |
| f" daily {self.daily_mean_data}" | |
| f"{custom_prompt}" | |
| ) | |
| try: | |
| response = self.gemini_model.generate_content(full_prompt) | |
| gemini_output = response.text | |
| return self._prepare_customised_report_json(gemini_output) | |
| except Exception as e: | |
| print(f"Error: {e}") | |
| def generate_report_with_customised_prompt_gemini(self, custom_prompt): | |
| """ | |
| Generate an air quality report using a customised user-provided prompt. | |
| """ | |
| base_info = self._prepare_base_info() | |
| full_prompt = ( | |
| f"{base_info} include the period under review." | |
| f"diurnal patterns indicate: {self.diurnal}. " | |
| f"number of sites or devices or airqo binos: {self.num_sites}. " | |
| f"{custom_prompt}" | |
| ) | |
| try: | |
| response = self.gemini_model.generate_content(full_prompt) | |
| gemini_output = response.text | |
| return self._prepare_customised_report_json(gemini_output) | |
| except Exception as e: | |
| logger.error(f"Failed to generate report: {e}") | |
| return None |
🧰 Tools
🪛 Ruff (0.8.0)
154-154: Use of functools.lru_cache or functools.cache on methods can lead to memory leaks
(B019)
| class DataFetcher: | ||
| @staticmethod | ||
| @lru_cache(maxsize=128) # Cache up to 128 most recent queries | ||
| def fetch_air_quality_data_a(grid_id, start_time, end_time): | ||
| token = Config.AIRQO_API_TOKEN | ||
| analytics_url = Config.ANALTICS_URL | ||
| if token is None: | ||
| print("Error: AIRQO_API_TOKEN environment variable is not set.") | ||
| return None | ||
|
|
||
| url= f"{analytics_url}?token={token}" | ||
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} | ||
|
|
||
| try: | ||
| response = requests.post(url, json=payload) | ||
| response.raise_for_status() | ||
| return response.json() | ||
| except requests.exceptions.HTTPError as http_err: | ||
| print(f"HTTP error occurred: {http_err}") | ||
| logging.error(f"HTTP error occurred: {http_err}") | ||
| except requests.exceptions.RequestException as req_err: | ||
| print(f"Request error occurred: {req_err}") | ||
| logging.error(f"Request error occurred: {req_err}") | ||
| except ValueError as json_err: | ||
| print(f"JSON decoding error: {json_err}") | ||
| logging.error(f"JSON decoding error: {json_err}") | ||
|
|
||
| return None |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance security and reliability of data fetching.
Several improvements needed:
- Move token from URL to headers
- Use consistent logging
- Add request timeout
- Add type hints
@staticmethod
@lru_cache(maxsize=128) # Cache up to 128 most recent queries
-def fetch_air_quality_data_a(grid_id, start_time, end_time):
+def fetch_air_quality_data_a(grid_id: str, start_time: str, end_time: str) -> dict | None:
token = Config.AIRQO_API_TOKEN
analytics_url = Config.ANALTICS_URL
if token is None:
- print("Error: AIRQO_API_TOKEN environment variable is not set.")
+ logger.error("AIRQO_API_TOKEN environment variable is not set.")
return None
- url= f"{analytics_url}?token={token}"
+ url = analytics_url
payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time}
+ headers = {"Authorization": f"Bearer {token}"}
try:
- response = requests.post(url, json=payload)
+ response = requests.post(url, json=payload, headers=headers, timeout=30)
response.raise_for_status()
return response.json()
except requests.exceptions.HTTPError as http_err:
- print(f"HTTP error occurred: {http_err}")
logging.error(f"HTTP error occurred: {http_err}")
except requests.exceptions.RequestException as req_err:
- print(f"Request error occurred: {req_err}")
logging.error(f"Request error occurred: {req_err}")
except ValueError as json_err:
- print(f"JSON decoding error: {json_err}")
logging.error(f"JSON decoding error: {json_err}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| class DataFetcher: | |
| @staticmethod | |
| @lru_cache(maxsize=128) # Cache up to 128 most recent queries | |
| def fetch_air_quality_data_a(grid_id, start_time, end_time): | |
| token = Config.AIRQO_API_TOKEN | |
| analytics_url = Config.ANALTICS_URL | |
| if token is None: | |
| print("Error: AIRQO_API_TOKEN environment variable is not set.") | |
| return None | |
| url= f"{analytics_url}?token={token}" | |
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} | |
| try: | |
| response = requests.post(url, json=payload) | |
| response.raise_for_status() | |
| return response.json() | |
| except requests.exceptions.HTTPError as http_err: | |
| print(f"HTTP error occurred: {http_err}") | |
| logging.error(f"HTTP error occurred: {http_err}") | |
| except requests.exceptions.RequestException as req_err: | |
| print(f"Request error occurred: {req_err}") | |
| logging.error(f"Request error occurred: {req_err}") | |
| except ValueError as json_err: | |
| print(f"JSON decoding error: {json_err}") | |
| logging.error(f"JSON decoding error: {json_err}") | |
| return None | |
| class DataFetcher: | |
| @staticmethod | |
| @lru_cache(maxsize=128) # Cache up to 128 most recent queries | |
| def fetch_air_quality_data_a(grid_id: str, start_time: str, end_time: str) -> dict | None: | |
| token = Config.AIRQO_API_TOKEN | |
| analytics_url = Config.ANALTICS_URL | |
| if token is None: | |
| logger.error("AIRQO_API_TOKEN environment variable is not set.") | |
| return None | |
| url = analytics_url | |
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} | |
| headers = {"Authorization": f"Bearer {token}"} | |
| try: | |
| response = requests.post(url, json=payload, headers=headers, timeout=30) | |
| response.raise_for_status() | |
| return response.json() | |
| except requests.exceptions.HTTPError as http_err: | |
| logging.error(f"HTTP error occurred: {http_err}") | |
| except requests.exceptions.RequestException as req_err: | |
| logging.error(f"Request error occurred: {req_err}") | |
| except ValueError as json_err: | |
| logging.error(f"JSON decoding error: {json_err}") | |
| return None |
|
Spatial changes in this PR available for preview here |
WHAT DOES THIS PR DO?
This PR implements a new API endpoint for generating comprehensive air quality reports with spatial analysis.
The endpoint supports both LLaMA and Gemini models for report generation, providing detailed analysis of air quality data across specified grid locations and time periods.
Added environment variables:
ANALTICS_URL=https://platform.airqo.net/api/v2/analytics/grid/report
GOOGLE_API_KEY=
HUGGING_FACE_TOKEN=
OPENAI_API_KEY=
To test the code, please have GOOGLE_API_KEY https://ai.google.dev/gemini-api
[Provide a brief description of the changes made in this PR]
There are 3 API to test
http://127.0.0.1:5000/api/v2/spatial/air_quality_reportThis uses gemini LLM , there are three types of audience
general public, policymaker, and researcher
http://127.0.0.1:5000/api/v2/spatial/air_quality_report_without_llmThis uses rule base
http://127.0.0.1:5000/api/v2/spatial/air_quality_report_with_customised_promptThis API uses gemini to act as a chat tool
WHAT ISSUES ARE RELATED TO THIS PR?
HOW DO I TEST OUT THIS PR?
WHICH ENDPOINTS SHOULD BE READY FOR TESTING?:
ARE THERE ANY RELATED PRs?
Summary by CodeRabbit
Release Notes
New Features
Dependencies
torch,transformers, andopenai.