setup backend code to make satellite predictions #3613
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📝 Walkthrough📝 WalkthroughWalkthroughThe pull request introduces several enhancements across multiple files within the spatial module. Key changes include the addition of cloud storage capabilities for satellite data access, the introduction of a new route for satellite predictions, and the creation of classes and methods to facilitate satellite data processing and model retrieval. Notably, a new dictionary organizes satellite collections, and methods for initializing the Earth Engine and extracting data based on geographical coordinates are implemented. Changes
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Codecov ReportAll modified and coverable lines are covered by tests ✅
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| return jsonify({'error': str(e)}), 500 |
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Copilot Autofix AI 4 months ago
To fix the problem, we need to ensure that detailed error information is not exposed to the end user. Instead, we should log the error details on the server and return a generic error message to the user. This can be achieved by using Python's logging module to log the exception details and then returning a generic error message in the response.
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| from flask import request, jsonify | ||
| import logging | ||
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| logging.basicConfig(level=logging.ERROR) | ||
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| class SatellitePredictionView: | ||
| @@ -50,3 +53,4 @@ | ||
| except Exception as e: | ||
| return jsonify({'error': str(e)}), 500 | ||
| app.logger.error('An error occurred: %s', str(e)) | ||
| return jsonify({'error': 'An internal error has occurred!'}), 500 | ||
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Actionable comments posted: 11
🧹 Outside diff range and nitpick comments (5)
src/spatial/controllers/controllers.py (1)
51-53: Well-structured new route for satellite predictions.The new route is correctly implemented and follows the existing patterns in the file. Great job on maintaining consistency! A couple of suggestions to consider:
It might be beneficial to add some basic error handling or input validation in this controller function. This could help catch and handle any potential issues before they reach the view layer.
Consider adding a docstring to the
get_satellite_predictionfunction to describe its purpose and any expected request parameters.Here's a potential enhancement to consider:
@controller_bp.route('/satellite_prediction', methods=['POST']) def get_satellite_prediction(): """ Handle POST requests for satellite predictions. Expected request format: { // Add expected request parameters here } Returns: JSON response with prediction results or error message. """ try: return SatellitePredictionView.make_predictions() except Exception as e: return jsonify({"error": str(e)}), 400This addition provides documentation and basic error handling. Adjust the docstring and error handling as needed for your specific use case.
src/spatial/views/satellite_predictions.py (1)
33-33: Unnecessary f-string: RemovefPrefixThe string
"satellite_model.pkl"does not contain any placeholders, so thefprefix is unnecessary.Apply this diff to remove the unnecessary
fprefix:- project_name, bucket_name, f"satellite_model.pkl" + project_name, bucket_name, "satellite_model.pkl"🧰 Tools
🪛 Ruff
33-33: Undefined name
project_name(F821)
33-33: Undefined name
bucket_name(F821)
33-33: f-string without any placeholders
Remove extraneous
fprefix(F541)
src/spatial/models/SatellitePredictionModel.py (2)
41-46: Double-check the calculation of the week numberThe week number is calculated using
strftime('%V'), which follows the ISO 8601 standard. Depending on your application's requirements, this may or may not align with your expected week numbering system.If you need the week number as per the Gregorian calendar, you might use:
all_features['week'] = int(current_time.strftime('%V')) # ISO 8601 week number + # Alternatively, for Gregorian calendar week number: + # all_features['week'] = current_time.isocalendar()[1]
4-5: Reorganize imports according to PEP 8 guidelinesPEP 8 recommends grouping imports into three sections: standard library imports, third-party imports, and local application imports, separated by blank lines. This enhances readability.
You can reorganize the imports as follows:
from datetime import datetime, timezone from typing import Dict +import ee +from google.oauth2 import service_account from configure import Config, satellite_collectionsAlso applies to: 7-7
src/spatial/configure.py (1)
140-140: Remove unnecessary listing of bucket contentsOn line 140, the call to
fs.ls(bucket_name)lists the contents of the bucket but doesn't use the result. Removing this line can reduce unnecessary overhead and improve performance.Here's the adjusted code:
def get_trained_model_from_gcs(project_name, bucket_name, source_blob_name): fs = gcsfs.GCSFileSystem(project=project_name) - fs.ls(bucket_name) with fs.open(bucket_name + "/" + source_blob_name, "rb") as handle: job = joblib.load(handle) return job
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📒 Files selected for processing (4)
- src/spatial/configure.py (2 hunks)
- src/spatial/controllers/controllers.py (2 hunks)
- src/spatial/models/SatellitePredictionModel.py (1 hunks)
- src/spatial/views/satellite_predictions.py (1 hunks)
🧰 Additional context used
🪛 Ruff
src/spatial/views/satellite_predictions.py
3-3:
gcsfsimported but unusedRemove unused import:
gcsfs(F401)
4-4:
joblibimported but unusedRemove unused import:
joblib(F401)
33-33: Undefined name
project_name(F821)
33-33: Undefined name
bucket_name(F821)
33-33: f-string without any placeholders
Remove extraneous
fprefix(F541)
🪛 GitHub Check: CodeQL
src/spatial/views/satellite_predictions.py
[warning] 44-44: Information exposure through an exception
Stack trace information flows to this location and may be exposed to an external user.
🔇 Additional comments (4)
src/spatial/controllers/controllers.py (1)
7-7: Excellent addition of the SatellitePredictionView import.The new import statement is well-placed and follows the existing naming conventions. It's a necessary addition to support the new satellite prediction functionality.
src/spatial/models/SatellitePredictionModel.py (1)
7-7: Verify the availability of the 'configure' moduleThe import statement
from configure import Config, satellite_collectionsassumes the presence of aconfiguremodule containingConfigandsatellite_collections. Please ensure that this module exists in your project and is correctly set up.To confirm the existence of
configure.py, you can run:✅ Verification successful
Re-attempting to Verify the 'configure' Module
Let's try a corrected command to locate
configure.py:
Verifying Definitions in
configure.pyModulesLet's identify which
configure.pycontains theConfigclass andsatellite_collections:
'configure' Module Verification Successful
The
configuremodule is present insrc/spatial/configure.pyand contains bothConfigandsatellite_collections. The import statement inSatellitePredictionModel.pyis correctly referencing this module.
- Module Path:
src/spatial/configure.py- Contains:
Configclasssatellite_collectionsvariable🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for the presence of 'configure.py' in the project directory. # Test: Search for 'configure.py'. Expected output: Path to 'configure.py'. fd --type f --name 'configure.py'Length of output: 284
Script:
#!/bin/bash # Description: Check for the presence of 'configure.py' in the project directory. # Test: Search for 'configure.py'. Expected output: Path to 'configure.py'. fd -t f 'configure.py'Length of output: 104
Script:
#!/bin/bash # Description: Verify that 'Config' and 'satellite_collections' are defined in the correct 'configure.py'. # Search for the 'Config' class grep -H "class Config" src/predict/jobs/predict_places_air_quality/configure.py src/spatial/configure.py # Search for the 'satellite_collections' variable grep -H "satellite_collections" src/predict/jobs/predict_places_air_quality/configure.py src/spatial/configure.pyLength of output: 381
src/spatial/configure.py (2)
47-47: Using fallback configuration enhances stabilityAssigning
configuration = app_config.get(environment, "staging")ensures that we have a default configuration if the specifiedenvironmentis not found. This is a good approach to maintain application stability.
49-135: Verify the correctness of satellite data fieldsIt's important to ensure that all parameters within the
satellite_collectionsdictionary are accurate and correspond to the actual data fields provided by the satellite data sources. Please double-check these entries to prevent any potential data retrieval issues.To assist with verification, you can run the following script to cross-reference the fields with the available datasets:
Please adjust the script according to the tools available in our environment.
| prediction = get_trained_model_from_gcs( | ||
| project_name, bucket_name, f"satellite_model.pkl" | ||
| ).predict(feature_array)[0] |
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Undefined Variables: project_name and bucket_name
The variables project_name and bucket_name used in get_trained_model_from_gcs are not defined within the scope of this function. Ensure that these variables are properly defined or passed as parameters to avoid NameError exceptions.
Would you like assistance in defining these variables or retrieving them from your configuration?
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33-33: Undefined name
project_name(F821)
33-33: Undefined name
bucket_name(F821)
33-33: f-string without any placeholders
Remove extraneous
fprefix(F541)
| import gcsfs | ||
| import joblib |
There was a problem hiding this comment.
Remove Unused Imports: gcsfs and joblib
It appears that gcsfs and joblib are imported but not used in this file. Removing unused imports helps keep the code clean and maintainable.
Apply this diff to remove the unused imports:
- import gcsfs
- import joblib📝 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 gcsfs | |
| import joblib |
🧰 Tools
🪛 Ruff
3-3:
gcsfsimported but unusedRemove unused import:
gcsfs(F401)
4-4:
joblibimported but unusedRemove unused import:
joblib(F401)
| for collection, fields in satellite_collections.items(): | ||
| image = ee.ImageCollection(collection) \ | ||
| .filterDate(current_time.strftime('%Y-%m-%d')) \ | ||
| .filterBounds(aoi) \ | ||
| .first() | ||
|
|
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Ensure proper handling when no images are found in the collection
When filtering the ImageCollection, there may be cases where no images match the criteria, resulting in image being None. It's important to handle this scenario to avoid unexpected errors downstream.
You might consider adding a check to continue the loop if no image is found:
for collection, fields in satellite_collections.items():
image = ee.ImageCollection(collection) \
.filterDate(current_time.strftime('%Y-%m-%d')) \
.filterBounds(aoi) \
.first()
+ if not image:
+ print(f"No images found for collection {collection} on {current_time.strftime('%Y-%m-%d')}")
+ continue📝 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.
| for collection, fields in satellite_collections.items(): | |
| image = ee.ImageCollection(collection) \ | |
| .filterDate(current_time.strftime('%Y-%m-%d')) \ | |
| .filterBounds(aoi) \ | |
| .first() | |
| for collection, fields in satellite_collections.items(): | |
| image = ee.ImageCollection(collection) \ | |
| .filterDate(current_time.strftime('%Y-%m-%d')) \ | |
| .filterBounds(aoi) \ | |
| .first() | |
| if not image: | |
| print(f"No images found for collection {collection} on {current_time.strftime('%Y-%m-%d')}") | |
| continue |
| for field in fields: | ||
| all_features[f"{collection}_{field}"] = values.get(field) |
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Handle cases where 'values' may be empty or missing keys
There may be situations where values is empty or doesn't contain all the expected fields. To prevent issues, it's prudent to handle missing keys when populating all_features.
Consider using a default value when accessing fields:
for field in fields:
- all_features[f"{collection}_{field}"] = values.get(field)
+ all_features[f"{collection}_{field}"] = values.get(field, None)📝 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.
| for field in fields: | |
| all_features[f"{collection}_{field}"] = values.get(field) | |
| for field in fields: | |
| all_features[f"{collection}_{field}"] = values.get(field, None) |
| if image: | ||
| values = image.reduceRegion( | ||
| reducer=ee.Reducer.mean(), | ||
| geometry=aoi, | ||
| scale=1113.2 | ||
| ).getInfo() | ||
|
|
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Handle potential exceptions during data retrieval with 'getInfo()'
The getInfo() call communicates with the Earth Engine servers and may raise exceptions due to network issues or server errors. To enhance robustness, consider wrapping this call in a try-except block.
Here's how you might implement this:
if image:
try:
values = image.reduceRegion(
reducer=ee.Reducer.mean(),
geometry=aoi,
scale=1113.2
).getInfo()
+ except Exception as e:
+ print(f"Error retrieving data from collection {collection}: {e}")
+ continue📝 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 image: | |
| values = image.reduceRegion( | |
| reducer=ee.Reducer.mean(), | |
| geometry=aoi, | |
| scale=1113.2 | |
| ).getInfo() | |
| if image: | |
| try: | |
| values = image.reduceRegion( | |
| reducer=ee.Reducer.mean(), | |
| geometry=aoi, | |
| scale=1113.2 | |
| ).getInfo() | |
| except Exception as e: | |
| print(f"Error retrieving data from collection {collection}: {e}") | |
| continue |
| @staticmethod | ||
| def extract_single_point_data(longitude: float, latitude: float) -> Dict[str, float]: | ||
| aoi = ee.Geometry.Point([longitude, latitude]) | ||
| current_time = datetime.now(timezone.utc) | ||
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| all_features = {} | ||
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| for collection, fields in satellite_collections.items(): | ||
| image = ee.ImageCollection(collection) \ | ||
| .filterDate(current_time.strftime('%Y-%m-%d')) \ | ||
| .filterBounds(aoi) \ | ||
| .first() | ||
|
|
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| if image: | ||
| values = image.reduceRegion( | ||
| reducer=ee.Reducer.mean(), | ||
| geometry=aoi, | ||
| scale=1113.2 | ||
| ).getInfo() | ||
|
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| for field in fields: | ||
| all_features[f"{collection}_{field}"] = values.get(field) | ||
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| # Add time-related features | ||
| all_features['year'] = current_time.year | ||
| all_features['month'] = current_time.month | ||
| all_features['day'] = current_time.day | ||
| all_features['dayofweek'] = current_time.weekday() | ||
| all_features['week'] = int(current_time.strftime('%V')) | ||
|
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| return all_features |
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🛠️ Refactor suggestion
Enhance code clarity with detailed docstrings and type annotations
Adding comprehensive docstrings and precise type hints improves code readability and assists other developers in understanding the code's purpose and usage.
Here's how you can augment the method:
@staticmethod
-def extract_single_point_data(longitude: float, latitude: float) -> Dict[str, float]:
+def extract_single_point_data(longitude: float, latitude: float) -> Dict[str, Optional[float]]:
+ """
+ Extract satellite data features for a given geographic point.
+
+ Parameters:
+ longitude (float): The longitude of the point of interest.
+ latitude (float): The latitude of the point of interest.
+
+ Returns:
+ Dict[str, Optional[float]]: A dictionary containing satellite data features and time-related features.
+ """📝 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 extract_single_point_data(longitude: float, latitude: float) -> Dict[str, float]: | |
| aoi = ee.Geometry.Point([longitude, latitude]) | |
| current_time = datetime.now(timezone.utc) | |
| all_features = {} | |
| for collection, fields in satellite_collections.items(): | |
| image = ee.ImageCollection(collection) \ | |
| .filterDate(current_time.strftime('%Y-%m-%d')) \ | |
| .filterBounds(aoi) \ | |
| .first() | |
| if image: | |
| values = image.reduceRegion( | |
| reducer=ee.Reducer.mean(), | |
| geometry=aoi, | |
| scale=1113.2 | |
| ).getInfo() | |
| for field in fields: | |
| all_features[f"{collection}_{field}"] = values.get(field) | |
| # Add time-related features | |
| all_features['year'] = current_time.year | |
| all_features['month'] = current_time.month | |
| all_features['day'] = current_time.day | |
| all_features['dayofweek'] = current_time.weekday() | |
| all_features['week'] = int(current_time.strftime('%V')) | |
| return all_features | |
| @staticmethod | |
| def extract_single_point_data(longitude: float, latitude: float) -> Dict[str, Optional[float]]: | |
| """ | |
| Extract satellite data features for a given geographic point. | |
| Parameters: | |
| longitude (float): The longitude of the point of interest. | |
| latitude (float): The latitude of the point of interest. | |
| Returns: | |
| Dict[str, Optional[float]]: A dictionary containing satellite data features and time-related features. | |
| """ | |
| aoi = ee.Geometry.Point([longitude, latitude]) | |
| current_time = datetime.now(timezone.utc) | |
| all_features = {} | |
| for collection, fields in satellite_collections.items(): | |
| image = ee.ImageCollection(collection) \ | |
| .filterDate(current_time.strftime('%Y-%m-%d')) \ | |
| .filterBounds(aoi) \ | |
| .first() | |
| if image: | |
| values = image.reduceRegion( | |
| reducer=ee.Reducer.mean(), | |
| geometry=aoi, | |
| scale=1113.2 | |
| ).getInfo() | |
| for field in fields: | |
| all_features[f"{collection}_{field}"] = values.get(field) | |
| # Add time-related features | |
| all_features['year'] = current_time.year | |
| all_features['month'] = current_time.month | |
| all_features['day'] = current_time.day | |
| all_features['dayofweek'] = current_time.weekday() | |
| all_features['week'] = int(current_time.strftime('%V')) | |
| return all_features |
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (10)
src/spatial/requirements.txt (1)
11-18: Excellent additions for API and cloud integration!The inclusion of flask-cors, earthengine-api, overpy, and geopy with specific version constraints is a commendable step towards robust API development and cloud integration. These additions align perfectly with the PR's objective of setting up API endpoints for satellite predictions.
However, I noticed that the existing entries for google-cloud-bigquery, google-cloud-storage, and gunicorn lack version constraints. For consistency and to prevent potential compatibility issues in the future, consider adding version constraints to these packages as well.
Here's a suggested update for the Google Cloud packages and gunicorn:
-google-cloud-bigquery -google-cloud-storage -google-cloud-bigquery[pandas] -gunicorn +google-cloud-bigquery~=3.19.0 +google-cloud-storage~=2.16.0 +google-cloud-bigquery[pandas]~=3.19.0 +gunicorn~=21.2.0Please verify these versions against your current setup before implementing.
src/spatial/views/satellite_predictions.py (3)
16-23: Request parsing and validation look solid!The code effectively extracts the required parameters (latitude and longitude) and the optional city parameter from the JSON request. The validation check for latitude and longitude is appropriate, ensuring that these required fields are present before proceeding.
As a minor suggestion, consider adding a validation check for the city parameter if there are any specific requirements (e.g., non-empty string, list of valid cities). This would depend on your application's needs and could enhance data integrity.
25-33: Earth Engine initialization and data extraction look great!The code effectively initializes the Earth Engine, creates a well-structured location dictionary, and extracts the necessary data for the given location. The conversion of features to a numpy array is also handled appropriately.
As a minor suggestion, consider adding error handling around the Earth Engine initialization and data extraction steps. This could help catch and handle any potential issues specific to these operations, improving the robustness of the code.
35-45: Model retrieval, prediction, and response formatting look excellent!The code effectively retrieves the trained model from Google Cloud Storage, makes a prediction using the feature array, and formats the response as a JSON object with all the relevant information. This is a well-structured approach to handling the prediction process.
There's a minor optimization we can make:
On line 36, the f-string doesn't contain any placeholders. We can simplify it by removing the
fprefix:Config.GOOGLE_CLOUD_PROJECT_ID, Config.PROJECT_BUCKET, "satellite_prediction_model.pkl"This small change will make the code slightly more efficient and cleaner.
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36-36: f-string without any placeholders
Remove extraneous
fprefix(F541)
src/spatial/models/SatellitePredictionModel.py (3)
12-16: Consider adding exception handling to Earth Engine initializationThe
initialize_earth_enginemethod looks good overall. However, to enhance robustness, it would be beneficial to add exception handling. This can help manage potential issues gracefully, such as missing or invalid credential files.Here's a suggested modification:
@staticmethod def initialize_earth_engine(): try: credentials = service_account.Credentials.from_service_account_file( Config.CREDENTIALS, scopes=['https://www.googleapis.com/auth/earthengine'] ) ee.Initialize(credentials=credentials, project=Config.GOOGLE_CLOUD_PROJECT_ID) except Exception as e: print(f"Failed to initialize Earth Engine: {e}") raiseThis change will catch any exceptions during initialization, log the error, and re-raise it for proper handling by the caller.
18-32: Well-structured methods with good use of Earth Engine APIThe
extract_data_for_imageandget_satellite_datamethods are well-implemented, making good use of Earth Engine API and functional programming principles. The use of type hints is commendable, enhancing code readability and maintainability.Consider making the scale parameter in
extract_data_for_imageconfigurable:@staticmethod def extract_data_for_image(image: ee.Image, aoi: ee.Geometry.Point, scale: float = 1113.2) -> ee.Feature: return ee.Feature(None, image.reduceRegion( reducer=ee.Reducer.mean(), geometry=aoi, scale=scale ).set('date', image.date().format('YYYY-MM-dd')))This change allows for more flexibility in different use cases where a different scale might be needed.
34-61: Comprehensive method with good error handlingThe
extract_data_for_locationmethod is well-implemented, handling various scenarios including cases where no data is available. The addition of time-related features is a thoughtful touch that will be valuable for data analysis.Consider adding some inline comments to explain the logic, especially for the data extraction and time feature addition. This will make the code even more readable and maintainable. For example:
# Extract latest satellite data for each collection for collection, fields in collections.items(): satellite_data = SatellitePredictionModel.get_satellite_data(aoi, collection, fields) time_series = satellite_data.getInfo() # Use the latest available data or set to 0 if no data if time_series['features']: latest_feature = time_series['features'][-1]['properties'] for field in fields: field_key = f"{collection}_{field}" result[field_key] = latest_feature.get(field, 0) or 0 else: for field in fields: field_key = f"{collection}_{field}" result[field_key] = 0 # Add time-related features for temporal analysis current_time = datetime.now(timezone.utc) result['year'] = current_time.year result['month'] = current_time.month result['day'] = current_time.day result['dayofweek'] = current_time.weekday() result['week'] = int(current_time.strftime('%V'))These comments provide context for each section of the method, making it easier for other developers to understand and maintain the code.
src/spatial/configure.py (3)
20-20: Well-integrated PROJECT_BUCKET variableThe addition of the
PROJECT_BUCKETvariable seamlessly integrates with the existing configuration structure. It's a prudent choice for managing cloud storage references.Consider adding a brief comment to explain the purpose of this variable, enhancing code readability:
# Google Cloud Storage bucket for project assets PROJECT_BUCKET = os.getenv("PROJECT_BUCKET")
48-134: Comprehensive satellite data mappingThe
satellite_collectionsdictionary provides a well-structured and extensive mapping of satellite data collections. This implementation offers strong type checking and IDE support, which can be beneficial for development.While externalizing this configuration (as suggested in a previous comment) remains a valid option for improved maintainability, the current implementation has its merits. To further enhance maintainability and type safety, consider adding type hints:
from typing import Dict, List satellite_collections: Dict[str, List[str]] = { 'COPERNICUS/S5P/OFFL/L3_SO2': [ 'SO2_column_number_density', # ... other fields ... ], # ... other collections ... }This addition would provide even stronger type checking and improve code documentation.
137-142: Efficient model retrieval from GCSThe
get_trained_model_from_gcsfunction is a valuable addition for retrieving trained models from Google Cloud Storage. To further enhance its robustness and clarity, consider the following suggestions:
Implement exception handling as mentioned in the previous comment to ensure graceful error management.
Add type hints and a docstring for improved clarity and maintainability:
from typing import Any import gcsfs import joblib def get_trained_model_from_gcs(project_name: str, bucket_name: str, source_blob_name: str) -> Any: """ Retrieve a trained model from Google Cloud Storage. Args: project_name (str): The name of the Google Cloud project. bucket_name (str): The name of the GCS bucket. source_blob_name (str): The path to the model file within the bucket. Returns: Any: The loaded model object. Raises: Exception: If there's an error in accessing or loading the model. """ fs = gcsfs.GCSFileSystem(project=project_name) try: with fs.open(f"{bucket_name}/{source_blob_name}", "rb") as handle: return joblib.load(handle) except Exception as e: print(f"Error loading model from GCS: {e}") raiseThese enhancements will significantly improve the function's robustness and usability.
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- src/spatial/configure.py (3 hunks)
- src/spatial/models/SatellitePredictionModel.py (1 hunks)
- src/spatial/requirements.txt (1 hunks)
- src/spatial/views/satellite_predictions.py (1 hunks)
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src/spatial/views/satellite_predictions.py
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src/spatial/views/satellite_predictions.py
[warning] 48-48: Information exposure through an exception
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src/spatial/requirements.txt (3)
6-10: Impressive enhancement of geospatial and data processing capabilities!The updates to pysal, geojson, libpysal, requests, and python-dotenv, along with the addition of numpy, protobuf, esda, shapely, scikit-learn, gcsfs, and joblib, significantly bolster our geospatial and data processing capabilities. These changes align perfectly with the PR objectives for satellite predictions and data processing.
The inclusion of lightgbm is particularly intriguing. While it's an excellent choice for gradient boosting, I'm curious about its specific application in our satellite prediction models.
Could you kindly elaborate on how lightgbm is being utilized in our satellite prediction workflow? This information would be valuable for the team's understanding and future maintenance.
Also applies to: 19-26
1-26: Comprehensive and well-structured dependency management!This update to the
requirements.txtfile is a significant step forward in aligning our dependencies with the PR objectives of setting up backend code for satellite predictions. The careful version management, inclusion of essential geospatial and machine learning libraries, and enhancement of API and cloud integration capabilities are all commendable.The changes demonstrate a thoughtful approach to dependency management, balancing the need for specific versions with the flexibility for minor updates. This will contribute to a more stable and maintainable codebase.
A few key points to highlight:
- Core dependencies (Flask, pandas, geopandas) have been updated with appropriate version constraints.
- New geospatial and machine learning libraries have been added, enhancing our data processing capabilities.
- API and cloud integration tools have been included, supporting the implementation of new endpoints.
Overall, these changes provide a solid foundation for the satellite prediction functionality we're aiming to implement. Great work on setting up a robust environment for this feature!
2-4: Excellent update to core dependencies!The version constraints for Flask, pandas, and geopandas have been thoughtfully updated using the
~=operator. This approach ensures compatibility with the specified versions while allowing for minor updates, which is a prudent practice in dependency management.To ensure these updates don't introduce any breaking changes, please run the following verification script:
src/spatial/views/satellite_predictions.py (1)
1-15: Imports and class definition look good!The imports are relevant to the functionality, and the class
SatellitePredictionViewis properly defined with a static method. It's great to see that the previously mentioned unused imports have been removed, keeping the code clean and maintainable.src/spatial/models/SatellitePredictionModel.py (1)
1-10: Imports and class definition look good!The imports are well-organized and appropriate for the functionality of the
SatellitePredictionModelclass. The class name is descriptive and follows the PascalCase convention, which is excellent.src/spatial/configure.py (2)
5-6: Excellent addition of cloud storage capabilities!The inclusion of
gcsfsandjobliblibraries sets the stage for robust cloud storage interactions and efficient model serialization. These are solid choices for enhancing our spatial data processing capabilities.
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1-142: Excellent enhancements to spatial configurationThe changes to
src/spatial/configure.pysignificantly improve the project's capabilities, particularly in cloud storage integration and satellite data management. The additions are well-structured and align perfectly with the project's objectives.Key improvements include:
- Integration of Google Cloud Storage functionality.
- Comprehensive mapping of satellite data collections.
- Utility function for retrieving trained models from GCS.
The suggested minor enhancements (adding comments, type hints, and exception handling) will further elevate the code's robustness and maintainability. Overall, these changes lay a solid foundation for advanced spatial data processing and analysis.
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src/spatial/views/satellite_predictions.py (2)
15-23: Great implementation of request processing!The method signature update to handle the optional 'city' parameter aligns well with the PR objectives. The extraction of latitude, longitude, and city from the request JSON is done efficiently, and the error handling for missing coordinates is appropriate.
As a minor suggestion, consider adding a validation check for the data type of latitude and longitude to ensure they are numeric values. This could prevent potential issues down the line.
Would you like me to provide an example of how to implement this additional validation?
37-48: Excellent implementation of model retrieval and prediction!The process of retrieving the trained model from Google Cloud Storage and making predictions is well-implemented. The response formatting is comprehensive, including all necessary information such as the prediction, coordinates, and timestamp.
I noticed a minor optimization opportunity:
On line 39, there's an f-string being used without any placeholders. We can simplify this by removing the
fprefix:Config.GOOGLE_CLOUD_PROJECT_ID, Config.PROJECT_BUCKET, "satellite_prediction_model.pkl"This small change will make the code slightly more efficient and cleaner.
Would you like me to provide a diff for this change?
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src/spatial/views/satellite_predictions.py (2)
1-10: Imports and class definition look good!The imports are relevant to the functionality, and the class definition is straightforward. It's great to see that the previously unused imports (gcsfs and joblib) have been removed, keeping the code clean and maintainable.
25-35: Earth Engine initialization and data extraction look solid!The code efficiently initializes the Earth Engine and extracts the necessary data for the given location. The feature preparation for prediction is well-implemented.
I noticed that a previous comment mentioned potential undefined variables (project_name and bucket_name). It appears this issue has been resolved, as these variables are not present in the current implementation. Great job addressing that!
To ensure the issue with undefined variables has been completely resolved, let's run a quick check:
This will help confirm that these variables are either properly defined or no longer used in this file.
✅ Verification successful
Updating Verification Script for Correct File Type Recognition
It appears there was an issue with the previous script regarding the file type specification. Let's correct the
rgcommand to use the appropriate file type identifier for Python files.
Undefined Variables Successfully Resolved
The verification confirms that
project_nameandbucket_nameare no longer present insrc/spatial/views/satellite_predictions.py. This addresses and resolves the previously raised concern regarding undefined variables.🏁 Scripts executed
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Script:
#!/bin/bash # Verify that project_name and bucket_name are properly defined or not used rg --type python 'project_name|bucket_name' src/spatial/views/satellite_predictions.pyLength of output: 119
Script:
#!/bin/bash # Verify that project_name and bucket_name are properly defined or not used rg --type py 'project_name|bucket_name' src/spatial/views/satellite_predictions.pyLength of output: 84
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src/spatial/configure.py (1)
137-145: Excellent implementation of exception handling in get_trained_model_from_gcsHello there! I'm delighted to see the implementation of exception handling in the
get_trained_model_from_gcsfunction. This is a significant improvement in robustness and error management. Well done!A couple of suggestions to further enhance this function:
Consider using a logging library instead of print statements for error reporting. This would be more suitable for a production environment and provide more control over log levels and output destinations.
You might want to add a docstring to explain the function's purpose, parameters, and return value, including the fact that it can return None in case of an error.
Here's a quick example of how you might implement these suggestions:
import logging def get_trained_model_from_gcs(project_name, bucket_name, source_blob_name): """ Retrieve a trained model from Google Cloud Storage. Args: project_name (str): The name of the GCP project. bucket_name (str): The name of the GCS bucket. source_blob_name (str): The name of the blob (file) in the bucket. Returns: object: The loaded model, or None if an error occurred. """ fs = gcsfs.GCSFileSystem(project=project_name) try: with fs.open(f"{bucket_name}/{source_blob_name}", "rb") as handle: return joblib.load(handle) except Exception as e: logging.error(f"Error loading model from GCS: {e}") return NoneWhat do you think about these suggestions? Would you like any help implementing them?
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src/spatial/configure.py (2)
5-6: Ensure 'gcsfs' and 'joblib' are included in project dependenciesHello there! I see you've added imports for
gcsfsandjoblib. These are excellent choices for working with Google Cloud Storage and serializing Python objects. To ensure smooth sailing in all environments, it's crucial to add these libraries to your project'srequirements.txtorsetup.py. This will help prevent any unexpected issues during deployment or when other team members run the code.
48-134: 🛠️ Refactor suggestionConsider externalizing 'satellite_collections' for improved maintainability
Greetings! I must say, I'm impressed by the comprehensive
satellite_collectionsdictionary you've crafted. It's a treasure trove of information about various satellite datasets and their attributes. Well done!However, I do concur with the previous suggestion about externalizing this configuration. Given its size and the likelihood of future updates, moving it to a separate JSON or YAML file could offer several benefits:
- Improved readability of the main Python file
- Easier updates without touching the core code
- Potential for dynamic loading or hot-reloading of configurations
That said, I understand if there are reasons to keep it inline. If you decide to keep it here, consider adding a comment explaining why this approach was chosen.
Would you like any assistance in externalizing this configuration or adding explanatory comments?
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src/spatial/Dockerfile (3)
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9-12: Well-structured dependency management!The approach to installing dependencies is clean and follows best practices. Upgrading
pip,setuptools, andwheelbefore installing other dependencies is a smart move that can prevent potential issues.Consider combining the two
RUNcommands to reduce the number of layers in the Docker image:-RUN pip install --upgrade pip setuptools wheel -RUN pip3 install -r requirements.txt +RUN pip install --upgrade pip setuptools wheel && \ + pip3 install -r requirements.txtThis change can slightly reduce the image size and build time.
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18-20: Solid Flask app configuration!The setup for the Flask application follows standard practices, making it clear and easy to understand.
To enhance flexibility, consider using an environment variable for the port number:
ENV FLASK_APP=app.py -EXPOSE 5000 +ARG PORT=5000 +ENV PORT=$PORT +EXPOSE $PORTThis change allows for easier configuration of the port number at build or run time, which can be particularly useful in different deployment scenarios.
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22-31: Excellent use of multi-stage builds!The implementation of separate stages for development, staging, and production environments is a great practice. It allows for environment-specific configurations and optimizations.
To reduce redundancy and improve maintainability, consider consolidating the staging and production stages:
FROM base as dev ENV FLASK_ENV=development CMD flask run --host=0.0.0.0 -FROM base as staging -ENV FLASK_ENV=staging -CMD gunicorn --bind=0.0.0.0:5000 app:app - -FROM base as production -ENV FLASK_ENV=production -CMD gunicorn --bind=0.0.0.0:5000 app:app +FROM base as deploy +ARG FLASK_ENV=production +ENV FLASK_ENV=$FLASK_ENV +CMD gunicorn --bind=0.0.0.0:$PORT app:appThis change allows you to use the same stage for both staging and production, differentiating them by passing the appropriate
FLASK_ENVvalue at build time. It also uses thePORTenvironment variable we suggested earlier.You can then build the images like this:
docker build --target=deploy --build-arg FLASK_ENV=staging -t myapp:staging . docker build --target=deploy --build-arg FLASK_ENV=production -t myapp:production .This approach reduces duplication and makes it easier to maintain consistent configurations between staging and production environments.
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src/spatial/Dockerfile (1)
2-2: Excellent choice on updating the base image!The upgrade to
python:3.10-slim-bullseyeis a positive change that brings both a newer Python version and a more recent Debian distribution. This update can potentially improve performance and security.To ensure smooth operation with the new base image, please verify the compatibility of all dependencies with Python 3.10. You can use the following script to check for any potential issues:
Description
[x] Setup API endpoints to retrieve satellite predictions
Related Issues
Changes Made
Testing
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API Documentation Updated?
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Summary by CodeRabbit
New Features
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