Report with AI +prompts #3937
Report with AI +prompts #3937
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## staging #3937 +/- ##
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- Coverage 11.76% 11.69% -0.07%
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Files 114 114
Lines 15294 15480 +186
Branches 306 375 +69
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+ Hits 1799 1811 +12
- Misses 13495 13668 +173
- Partials 0 1 +1 |
📝 Walkthrough📝 WalkthroughWalkthroughThe changes in this pull request involve modifications to the Changes
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (5)
src/spatial/models/report_datafetcher.py (5)
Line range hint
108-115: Missing method implementation:_format_diurnal_peak()The prompt references
_format_diurnal_peak(), but this method appears to be undefined in the class. This could lead to runtime errors when generating reports for researchers.Consider implementing the method like this:
def _format_diurnal_peak(self): if not self.diurnal: return "data not available" peak_data = max(self.diurnal, key=lambda x: x.get('pm2_5_calibrated_value', 0)) return f"{peak_data.get('hour', 'unknown')}:00 with {peak_data.get('pm2_5_calibrated_value', 'unknown')} µg/m³"
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108-115: Add defensive programming for missing dataThe prompt construction assumes the presence of
daily_min_pm2_5anddaily_max_pm2_5data. Consider adding null checks to prevent potential runtime errors.Consider this safer approach:
- 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"Daily mean measurements show values ranging from {self.daily_min_pm2_5.get('pm2_5_calibrated_value', 'N/A')} to {self.daily_max_pm2_5.get('pm2_5_calibrated_value', 'N/A')} µg/m³.\n"
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266-269: Fix the least PM2.5 time assignmentThere's a bug in the assignment of
least_pm2_5_time. The indentation suggests it's outside theifblock, and it's being reassigned toNoneimmediately after being set.Apply this fix:
else: peak_time = None peak_pm2_5 = None least_pm2_5 = None - least_pm2_5_time = None + least_pm2_5_time = None
Line range hint
313-326: Improve text formatting in conclusion sectionThe conclusion text has several formatting issues:
- Missing spaces after periods
- Missing spaces around "raw"
- Inconsistent quotation marks
Consider this improved formatting:
- f"Overall, the air quality report highlights the importance of monitoring and understanding the patterns of PM2.5 and PM10 concentrations in the {self.grid_name} " - f"The analysis of the data reveals that air quality varies significantly over time, with periods of both moderate and unhealthy conditions. " - 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" 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"Overall, the air quality report highlights the importance of monitoring and understanding the patterns of PM2.5 and PM10 concentrations in {self.grid_name}. " + f"The analysis of the data reveals that air quality varies significantly over time, with periods of both moderate and unhealthy conditions. " + 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"the PM2.5 raw values ranged from {self.daily_min_pm2_5.get('pm2_5_raw_value', 'N/A')} µg/m³ on {self.daily_min_pm2_5.get('date', 'N/A')} to {self.daily_max_pm2_5.get('pm2_5_raw_value', 'N/A')} µg/m³ on {self.daily_max_pm2_5.get('date', 'N/A')}. "
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1-350: Consider architectural improvements for better maintainabilityA few suggestions to enhance the codebase:
- Consider implementing a common interface for different LLM providers to make it easier to add new providers in the future.
- Add comprehensive logging for better debugging and monitoring.
- Consider implementing retry mechanisms for API calls to handle temporary failures.
Here's a suggested approach for the LLM interface:
from abc import ABC, abstractmethod class LLMProvider(ABC): @abstractmethod def generate_content(self, prompt: str) -> str: pass class GeminiProvider(LLMProvider): def __init__(self): self.model = genai.GenerativeModel('gemini-pro') def generate_content(self, prompt: str) -> str: response = self.model.generate_content(prompt) return response.text class OpenAIProvider(LLMProvider): def __init__(self): self.client = openai.ChatCompletion def generate_content(self, prompt: str) -> str: response = self.client.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": prompt}] ) return response.choices[0].message['content']
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (2)
src/spatial/requirements.txt (2)
12-13: Excellent addition of BigQuery optimizations!The addition of
google-cloud-bigquery-storagealong with pandas extras for BigQuery will significantly improve data transfer performance, especially for large datasets. This is particularly relevant for the air quality reports mentioned in the PR objectives.Consider implementing connection pooling and data caching strategies to further optimize BigQuery interactions.
Line range hint
1-34: Consider organizing dependencies by categoryThe requirements file mixes different types of dependencies (web, data, ML, cloud). Consider organizing them into logical groups with comments for better maintainability.
Here's a suggested organization:
# Web Framework Dependencies flask~=3.0.3 gunicorn flask-cors~=5.0.0 # Data Processing pandas~=2.2.3 numpy~=1.25.2 geopandas scikit-learn~=1.5.2 # Geospatial pysal~=24.7 geojson~=3.1.0 libpysal~=4.12.1 shapely~=2.0.6 # Google Cloud google-cloud-bigquery[pandas] google-cloud-bigquery-storage google-cloud-storage # Machine Learning & AI lightgbm~=4.1.0 torch transformers datasets sentencepiece huggingface_hub google-generativeai openai
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src/spatial/requirements.txt (1)
Line range hint 1-34: Verify dependency compatibility
Let's ensure there are no known conflicts between these dependencies, particularly with the AI/ML stack.
✅ Verification successful
Dependencies appear compatible with some version considerations
The analysis of the requirements reveals no immediate compatibility issues, but there are a few points worth noting:
- The ML stack (scikit-learn 1.5.2, lightgbm 4.1.0) versions are consistent with recent releases and are compatible with pandas 2.2.3
- Numpy is left unpinned which is generally safe as it's a core dependency managed by other packages
- torch and transformers are also unpinned, allowing for compatible versions to be resolved by pip
Recommendations:
- Consider pinning numpy to
~=1.26.4to match the version used in other parts of the codebase - The AI/ML dependencies (torch, transformers) can remain unpinned as they're commonly used together and maintain compatibility
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check for known conflicts between dependencies
# Create a temporary requirements file with exact versions
echo "Creating temporary requirements file..."
cat > temp_req.txt <<EOL
flask==3.0.3
pandas==2.2.3
scikit-learn==1.5.2
lightgbm==4.1.0
torch
transformers
EOL
# Use pip-compile to check for conflicts
pip-compile --dry-run temp_req.txt 2>&1 || true
# Clean up
rm temp_req.txt
Length of output: 259
Script:
#!/bin/bash
# Let's check for any potential version conflicts by examining the dependencies
# of key packages in the requirements.txt
# First, let's check if numpy is imported by multiple packages with different version requirements
rg "numpy[>=<~=]" -A 1 -B 1
# Check for torch-related dependencies
rg "torch[>=<~=]" -A 1 -B 1
# Check for potential conflicts in ML-related packages
rg "(scikit-learn|pandas|lightgbm)[>=<~=]" -A 1 -B 1
# Look for any constraint specifications in setup files or dependency manifests
fd "setup.py|pyproject.toml|requirements.txt" --exec cat {}
Length of output: 10650
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Description
[Provide a brief description of the changes made in this PR]
Updated dependencies in the requirements file, including the addition of pandas-gbq and google-cloud-bigquery-storage, along with adjustments to existing libraries.
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Summary by CodeRabbit
New Features
Chores
pandas-gbqandgoogle-cloud-bigquery-storage, along with adjustments to existing libraries.