Backend for satellite imagery pollutants prediction tool #59
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📝 WalkthroughWalkthroughThe project for pollution prediction has been significantly enhanced with the addition of a comprehensive Changes
Sequence DiagramsequenceDiagram
Client->>Flask App: Request to /api/v2/spatial/upload-image
Flask App->>Controller: Route to upload_image
Controller->>PollutantApis: Call upload_image method
PollutantApis-->>Controller: Return success/error message
Controller-->>Flask App: Return response
Flask App-->>Client: Return response
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Actionable comments posted: 2
🧹 Nitpick comments (2)
pollution_prediction/app.py (2)
1-3: Consider adding essential dependencies for a pollution prediction service.The current setup is quite minimal. For a robust pollution prediction service, you'll want to consider:
- Data processing libraries (numpy, pandas)
- Model handling (scikit-learn, tensorflow)
- Configuration management
- Proper error handling
Here's a suggested enhancement:
from flask import Flask +from flask import jsonify, request +import numpy as np +import pandas as pd +from config import Config +from error_handlers import register_error_handlers app = Flask(__name__) +app.config.from_object(Config) +register_error_handlers(app)
1-12: Consider a more comprehensive project structure.For a machine learning service, consider organizing your project with these components:
models/: ML model definitions and training scriptsschemas/: Request/response validation schemasservices/: Business logic and model inferenceutils/: Helper functions and data preprocessingtests/: Unit and integration testsconfig/: Environment-specific configurationsWould you like me to generate a detailed project structure template or open an issue to track this enhancement?
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📒 Files selected for processing (2)
pollution_prediction/.gitignore(1 hunks)pollution_prediction/app.py(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- pollution_prediction/.gitignore
Co-Authored-By: Noble Mutabazi <[email protected]>
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Actionable comments posted: 10
🧹 Nitpick comments (6)
pollution_prediction/views/pollutant_views.py (2)
2-2: Remove unused importget_trained_model_from_gcsThe function
get_trained_model_from_gcsis imported but not used in this file. Removing unused imports helps keep the code clean and maintainable.Apply this diff to remove the unused import:
-from configure import get_trained_model_from_gcs, Config +from configure import Config🧰 Tools
🪛 Ruff (0.8.2)
2-2:
configure.get_trained_model_from_gcsimported but unusedRemove unused import:
configure.get_trained_model_from_gcs(F401)
58-76: Enhance error handling for individual centroid processingIf an error occurs while processing data for a centroid, the current implementation stops processing and returns a 500 error. To improve robustness, consider logging the error and continuing with the next centroid. This ensures that one failure doesn't halt the entire operation.
You can modify the exception handling as follows:
for _, centroid in centroids.iterrows(): latitude = centroid['Centroid_lat'] longitude = centroid['Centroid_lon'] confidence_score = centroid["confidence_score"] try: # Start measuring time for location processing start_location_time = time.time() location_data = PredictionAndProcessing.process_location(latitude, longitude, radius) location_duration = time.time() - start_location_time total_location_duration += location_duration # Start measuring time for environment data processing start_env_time = time.time() environment_data = PredictionAndProcessing.get_environment_profile(latitude, longitude, months, radius) environment_duration = time.time() - start_env_time total_environment_duration += environment_duration except Exception as e: - return jsonify({"error": f"Error processing data for centroid: {e}"}), 500 + # Log the error and continue processing + print(f"Error processing data for centroid at ({latitude}, {longitude}): {e}") + continue # Create a GeoJSON-compliant feature for MongoDB feature = { "type": "Feature", "geometry": mapping(centroid["geometry"]), "properties": { "latitude": latitude, "longitude": longitude, "confidence_score": confidence_score, "timestamp": current_time, **location_data, **environment_data } } geojson_features.append(feature)🧰 Tools
🪛 Ruff (0.8.2)
65-65: Undefined name
time(F821)
67-67: Undefined name
time(F821)
71-71: Undefined name
time(F821)
73-73: Undefined name
time(F821)
76-76: Undefined name
jsonify(F821)
pollution_prediction/models/pollution_identification.py (2)
2-37: Remove unused imports to clean up the codeSeveral imports are not used in this file, which can clutter the code and potentially cause confusion. Keeping imports clean improves readability and maintainability.
Consider removing the following unused imports:
-import os -import time -import json -import pandas as pd -from shapely.geometry import mapping -import geemap -from matplotlib.patches import Polygon as pltPolygon -from pymongo import MongoClient -from bson import json_util🧰 Tools
🪛 Ruff (0.8.2)
2-2:
osimported but unusedRemove unused import:
os(F401)
3-3:
timeimported but unusedRemove unused import:
time(F401)
8-8:
jsonimported but unusedRemove unused import:
json(F401)
12-12:
pandasimported but unusedRemove unused import:
pandas(F401)
19-19:
shapely.geometry.mappingimported but unusedRemove unused import:
shapely.geometry.mapping(F401)
24-24:
geemapimported but unusedRemove unused import:
geemap(F401)
31-31:
matplotlib.patches.Polygonimported but unusedRemove unused import:
matplotlib.patches.Polygon(F401)
36-36:
pymongo.MongoClientimported but unusedRemove unused import:
pymongo.MongoClient(F401)
37-37:
bson.json_utilimported but unusedRemove unused import:
bson.json_util(F401)
187-190: Remove unused variableeand consider handling exceptionsThe exception variable
eis assigned but not used, and the variablebuilding_typesis assigned but never utilized later in the code.
If you don't need to use the exception information, you can omit
as e.If
building_typesis not needed, remove assignments related to it.Alternatively, consider logging the exception for debugging purposes.
except Exception: - number_of_buildings = 'Error' - building_density = 'Error' - building_types = 'Error' + number_of_buildings = 0 + building_density = 0🧰 Tools
🪛 Ruff (0.8.2)
187-187: Local variable
eis assigned to but never usedRemove assignment to unused variable
e(F841)
190-190: Local variable
building_typesis assigned to but never usedRemove assignment to unused variable
building_types(F841)
pollution_prediction/controllers/controllers.py (1)
2-2: Remove unused importsrequestandjsonifyThe imports
requestandjsonifyfromflaskare not used in this file. Cleaning up unused imports enhances code readability.Apply this diff to remove the unused imports:
-from flask import Blueprint, request, jsonify +from flask import Blueprint🧰 Tools
🪛 Ruff (0.8.2)
2-2:
flask.requestimported but unusedRemove unused import
(F401)
2-2:
flask.jsonifyimported but unusedRemove unused import
(F401)
pollution_prediction/app.py (1)
1-1: Remove unused importmake_response.The
make_responseimport is not used in the code.-from flask import Flask, make_response +from flask import Flask🧰 Tools
🪛 Ruff (0.8.2)
1-1:
flask.make_responseimported but unusedRemove unused import:
flask.make_response(F401)
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📒 Files selected for processing (5)
pollution_prediction/app.py(1 hunks)pollution_prediction/controllers/controllers.py(1 hunks)pollution_prediction/models/pollution_identification.py(1 hunks)pollution_prediction/requirements.txt(1 hunks)pollution_prediction/views/pollutant_views.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
pollution_prediction/app.py
1-1: flask.make_response imported but unused
Remove unused import: flask.make_response
(F401)
pollution_prediction/controllers/controllers.py
2-2: flask.request imported but unused
Remove unused import
(F401)
2-2: flask.jsonify imported but unused
Remove unused import
(F401)
pollution_prediction/views/pollutant_views.py
2-2: configure.get_trained_model_from_gcs imported but unused
Remove unused import: configure.get_trained_model_from_gcs
(F401)
16-16: Undefined name request
(F821)
17-17: Undefined name jsonify
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20-20: Undefined name request
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22-22: Undefined name jsonify
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25-25: Undefined name secure_filename
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26-26: Undefined name UPLOAD_FOLDER
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31-31: Undefined name request
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32-32: Undefined name request
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34-34: Undefined name jsonify
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37-37: Undefined name time
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44-44: Undefined name time
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48-48: Undefined name jsonify
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52-52: Undefined name datetime
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65-65: Undefined name time
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67-67: Undefined name time
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71-71: Undefined name time
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73-73: Undefined name time
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76-76: Undefined name jsonify
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81-81: Undefined name mapping
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98-98: Undefined name jsonify
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101-101: Undefined name jsonify
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114-114: Undefined name request
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124-124: Undefined name jsonify
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139-139: Undefined name Response
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140-140: Undefined name json
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155-155: Undefined name jsonify
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170-170: Undefined name Response
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171-171: Undefined name json_util
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176-176: Undefined name jsonify
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pollution_prediction/models/pollution_identification.py
2-2: os imported but unused
Remove unused import: os
(F401)
3-3: time imported but unused
Remove unused import: time
(F401)
8-8: json imported but unused
Remove unused import: json
(F401)
12-12: pandas imported but unused
Remove unused import: pandas
(F401)
19-19: shapely.geometry.mapping imported but unused
Remove unused import: shapely.geometry.mapping
(F401)
24-24: geemap imported but unused
Remove unused import: geemap
(F401)
31-31: matplotlib.patches.Polygon imported but unused
Remove unused import: matplotlib.patches.Polygon
(F401)
36-36: pymongo.MongoClient imported but unused
Remove unused import: pymongo.MongoClient
(F401)
37-37: bson.json_util imported but unused
Remove unused import: bson.json_util
(F401)
64-64: Undefined name service_account
(F821)
85-85: Undefined name load_tiff
(F821)
90-90: Undefined name normalize_image
(F821)
187-187: Local variable e is assigned to but never used
Remove assignment to unused variable e
(F841)
190-190: Local variable building_types is assigned to but never used
Remove assignment to unused variable building_types
(F841)
🔇 Additional comments (4)
pollution_prediction/models/pollution_identification.py (1)
62-68:⚠️ Potential issueAdjust indentation for
initialize_earth_enginemethodThe
initialize_earth_enginemethod is intended to be part of thePredictionAndProcessingclass but is currently defined outside of it due to incorrect indentation.Indent the method to be inside the class:
class PredictionAndProcessing: @staticmethod + def initialize_earth_engine(): + ee.Initialize(credentials=service_account.Credentials.from_service_account_file( + Config.CREDENTIALS, + scopes=['https://www.googleapis.com/auth/earthengine'] + ), project=Config.GOOGLE_CLOUD_PROJECT_ID)Likely invalid or redundant comment.
🧰 Tools
🪛 Ruff (0.8.2)
64-64: Undefined name
service_account(F821)
pollution_prediction/app.py (2)
5-7: LGTM! Clean app initialization with proper CORS and blueprint setup.The Flask app initialization with CORS and versioned API routing follows best practices.
24-27: Consider removing or securing the test endpoint.Test endpoints should not be exposed in production as they can:
- Expose unnecessary information about the system
- Be used for DoS attacks without rate limiting
Either remove the endpoint or secure it:
-@app.route("/test", methods=["GET"]) -def test(): - return "Test success" +@app.route("/health", methods=["GET"]) +@limiter.limit("10 per minute") # Add rate limiting +def health_check(): + return {"status": "healthy", "timestamp": datetime.utcnow().isoformat()}Required imports:
from datetime import datetime from flask_limiter import Limiter from flask_limiter.util import get_remote_address limiter = Limiter( get_remote_address, app=app, default_limits=["200 per day", "50 per hour"] )✅ Verification successful
The test endpoint should indeed be secured or removed
The codebase analysis confirms this is a production-ready application that integrates with external APIs (AirQo, Google Earth Engine). While proper authentication exists for external API calls, the test endpoint remains exposed without any protection or rate limiting, making it a potential security risk.
The original suggestion to either remove it or convert it to a proper rate-limited health check endpoint remains valid.
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#!/bin/bash # Look for environment configuration files echo "=== Environment Files ===" fd -e .env -e .env.example -e .env.sample -e config.py -e settings.py echo -e "\n=== Authentication Implementation ===" # Search for authentication-related code rg -i "auth|login|jwt|token|session" --type python echo -e "\n=== Rate Limiting Implementation ===" # Search for rate limiting rg -i "limiter|rate.limit" --type python echo -e "\n=== Similar Test/Health Endpoints ===" # Search for test or health endpoints ast-grep --pattern 'route("/test"|route("/health")'Length of output: 615
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pollution_prediction/requirements.txt (1)
1-1: Verify TensorFlow compatibility with Keras.The commented out Keras version might cause compatibility issues with TensorFlow 2.15.1.
Also applies to: 75-75
✅ Verification successful
No compatibility issues with TensorFlow and Keras ✓
The codebase correctly uses
tf.kerasfrom TensorFlow 2.15.1 instead of standalone Keras. The commented out Keras dependency is actually beneficial as it prevents potential conflicts with TensorFlow's built-in Keras implementation.🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash # Check TensorFlow and Keras compatibility pip index versions tensorflow | grep "2.15.1" pip index versions keras | grep "Available versions:"Length of output: 289
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| ee.Initialize(credentials=service_account.Credentials.from_service_account_file( | ||
| Config.CREDENTIALS, | ||
| scopes=['https://www.googleapis.com/auth/earthengine'] | ||
| ), project=Config.GOOGLE_CLOUD_PROJECT_ID) |
There was a problem hiding this comment.
Import service_account for Earth Engine initialization
The service_account module is used to initialize Earth Engine with service account credentials but has not been imported. This will result in a NameError.
Add the following import at the top of the file:
+from google.oauth2 import service_accountCommittable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff (0.8.2)
64-64: Undefined name service_account
(F821)
| image, image_profile = load_tiff(image_path) | ||
| # Check if the image has 4 channels (e.g., RGBA) | ||
| if image.shape[-1] == 4: | ||
| # Discard the alpha channel (keep only the first three channels: RGB) | ||
| image = image[:, :, :3] | ||
| image = normalize_image(image) |
There was a problem hiding this comment.
Correct method calls to load_tiff and normalize_image
The methods load_tiff and normalize_image are static methods of the PredictionAndProcessing class but are being called without referencing the class. This will lead to a NameError.
Update the method calls to include the class name:
@staticmethod
def preprocess_image(image_path):
- image, image_profile = load_tiff(image_path)
+ image, image_profile = PredictionAndProcessing.load_tiff(image_path)
# Check if the image has 4 channels (e.g., RGBA)
if image.shape[-1] == 4:
# Discard the alpha channel (keep only the first three channels: RGB)
image = image[:, :, :3]
- image = normalize_image(image)
+ image = PredictionAndProcessing.normalize_image(image)
return image, image_profile📝 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.
| image, image_profile = load_tiff(image_path) | |
| # Check if the image has 4 channels (e.g., RGBA) | |
| if image.shape[-1] == 4: | |
| # Discard the alpha channel (keep only the first three channels: RGB) | |
| image = image[:, :, :3] | |
| image = normalize_image(image) | |
| image, image_profile = PredictionAndProcessing.load_tiff(image_path) | |
| # Check if the image has 4 channels (e.g., RGBA) | |
| if image.shape[-1] == 4: | |
| # Discard the alpha channel (keep only the first three channels: RGB) | |
| image = image[:, :, :3] | |
| image = PredictionAndProcessing.normalize_image(image) |
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🪛 Ruff (0.8.2)
85-85: Undefined name load_tiff
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90-90: Undefined name normalize_image
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| @app.after_request | ||
| def add_cors_headers(response): | ||
| response.headers[ | ||
| "Access-Control-Allow-Origin" | ||
| ] = "*" # You can specify specific origins instead of '*' | ||
| response.headers[ | ||
| "Access-Control-Allow-Headers" | ||
| ] = "Content-Type, Authorization, X-Requested-With, X-Auth-Token" | ||
| response.headers["Access-Control-Allow-Methods"] = "GET,PUT,POST,DELETE,OPTION" | ||
| response.headers["Access-Control-Allow-Credentials"] = "true" | ||
| return response |
There was a problem hiding this comment.
Security: Restrict CORS origin when allowing credentials.
There are two security concerns with the current CORS configuration:
- Using wildcard (*) for Access-Control-Allow-Origin in production is not recommended
- Browsers will reject requests when credentials are allowed with a wildcard origin
Consider using environment variables to configure allowed origins:
- response.headers["Access-Control-Allow-Origin"] = "*"
+ allowed_origins = os.getenv('ALLOWED_ORIGINS', '').split(',')
+ origin = request.headers.get('Origin')
+ if origin in allowed_origins:
+ response.headers["Access-Control-Allow-Origin"] = originDon't forget to import os and request:
from flask import Flask, request
import os| if __name__ == "__main__": | ||
| app.run() |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Configure app.run() for development and use WSGI server for production.
The current setup lacks proper configuration for different environments.
if __name__ == "__main__":
- app.run()
+ env = os.getenv("FLASK_ENV", "development")
+ if env == "development":
+ app.run(
+ host=os.getenv("FLASK_HOST", "0.0.0.0"),
+ port=int(os.getenv("FLASK_PORT", "5000")),
+ debug=True
+ )
+ else:
+ # Use gunicorn or other WSGI server in production
+ app.run(debug=False)Committable suggestion skipped: line range outside the PR's diff.
| google-pasta | ||
| google-resumable-media | ||
| googleapis-common-protos | ||
| grpcio | ||
| h5py | ||
| httplib2 | ||
| huggingface_hub | ||
| idna | ||
| imageio | ||
| importlib_metadata | ||
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| introcs | ||
| ipyevents | ||
| ipyfilechooser | ||
| ipyleaflet | ||
| ipython | ||
| ipytree | ||
| ipywidgets | ||
| itsdangerous | ||
| jedi | ||
| joblib~=1.4.2 # Specify if needed, otherwise can be removed. | ||
| #keras==2.9.0 # Ensure compatibility with TensorFlow. | ||
| kiwisolver | ||
| lazy_loader | ||
| libclang~=18.1.1 | ||
| libpysal~=4.12.1 | ||
| lightgbm | ||
| matplotlib-inline | ||
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| pydantic==2.10.2 | ||
| pydantic_core==2.27.1 | ||
| pymongo==4.10.1 | ||
| pyparsing | ||
| pyperclip | ||
| pyproj==3.5.0 | ||
| pysal~=24.7 | ||
| pyshp==2.3.1 | ||
| python-box==7.2.0 | ||
| python-dateutil | ||
| python-dotenv | ||
| pytz | ||
| rasterio==1.3.9 | ||
| ratelim | ||
| requests-oauthlib | ||
| requests | ||
| retrying | ||
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| protobuf | ||
| overpy | ||
| gunicorn | ||
| google-cloud-bigquery | ||
| google-cloud-bigquery-storage | ||
| geojson | ||
| flask |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Pin all dependency versions for reproducibility and security.
Many dependencies lack version pins, which could lead to compatibility issues and security vulnerabilities.
Generate a requirements.txt with pinned versions:
#!/bin/bash
# Generate requirements.txt with pinned versions
pip freeze > requirements.txtAlso, consider using dependency groups for dev dependencies:
+# runtime.txt
+python-3.9
+# requirements/
+# ├── base.txt # Core dependencies
+# ├── dev.txt # Development dependencies
+# └── prod.txt # Production-specific dependencies| executing | ||
| fastapi==0.115.0 # Specify if needed, otherwise can be removed. |
There was a problem hiding this comment.
💡 Codebase verification
Remove unused FastAPI dependency
The codebase exclusively uses Flask for its web framework implementation. FastAPI and its related dependencies can be safely removed as they are not being utilized anywhere in the code.
- Remove from requirements.txt:
-fastapi==0.115.0
-joblib~=1.4.2🔗 Analysis chain
Verify FastAPI necessity alongside Flask.
The project includes both Flask and FastAPI frameworks. This might lead to confusion and maintenance overhead.
Consider choosing one framework based on your needs:
- Flask: Simpler, more straightforward for basic APIs
- FastAPI: Better for async operations, automatic OpenAPI docs
If FastAPI isn't being used, remove it and its dependencies:
-fastapi==0.115.0
-joblib~=1.4.2Also applies to: 73-74
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| google-api-core | ||
| google-api-python-client | ||
| google-cloud-storage | ||
| google-crc32c==1.6.0 | ||
| google-generativeai | ||
| google-pasta | ||
| google-resumable-media | ||
| googleapis-common-protos |
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💡 Codebase verification
Unnecessary Google Cloud dependencies detected
Several Google Cloud dependencies appear unused in the codebase. Only google-generativeai shows active usage. Consider removing these unused packages:
google-cloud-storagegoogle-api-python-clientgoogle-pasta
Keep google-generativeai and its core dependencies (google-api-core, googleapis-common-protos). Other dependencies might be automatically installed as required.
🔗 Analysis chain
Security: Review Google Cloud dependencies scope.
Multiple Google Cloud dependencies are included. Ensure you're only including what's necessary for your use case.
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The following scripts were executed for the analysis:
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#!/bin/bash
# Search for Google Cloud API usage
rg -l "google.cloud" | grep -v "requirements.txt"
Length of output: 47
Script:
#!/bin/bash
# Search for various Google package imports and usages
echo "=== Searching for Google imports ==="
rg -l "from google\." || true
rg -l "import google\." || true
echo -e "\n=== Searching for potential Google Cloud config files ==="
fd -g "*google*json" || true
fd -g "*cloud*json" || true
echo -e "\n=== Searching for specific Google service usage ==="
rg -l "storage.Client" || true
rg -l "generativeai" || true
rg -l "googleapiclient" || true
Length of output: 561
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Actionable comments posted: 1
🧹 Nitpick comments (1)
pollution_prediction/requirements.txt (1)
5-15: Maintain consistent spacing around version pins.Some dependencies use spaces around
==while others don't. Let's maintain consistency.Apply this diff to standardize the format:
-geopandas == 1.0.1 +geopandas==1.0.1 -pandas == 2.2.3 +pandas==2.2.3 -numpy == 2.2.1 +numpy==2.2.1 -shapely == 2.0.6 +shapely==2.0.6 -pymongo == 4.10.1 +pymongo==4.10.1 -osmnx == 2.0.1 +osmnx==2.0.1 -scikit-learn == 1.6.1 +scikit-learn==1.6.1 -matplotlib == 3.10.0 +matplotlib==3.10.0 -pyproj == 3.7.0 +pyproj==3.7.0 -geemap == 0.35.1 +geemap==0.35.1
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pollution_prediction/requirements.txt(1 hunks)
🔇 Additional comments (1)
pollution_prediction/requirements.txt (1)
1-4: Core dependencies look good!The web framework (Flask), machine learning (TensorFlow), and geospatial (earthengine-api, rasterio) dependencies are pinned to valid, recent versions.
Also applies to: 10-10
| pandas == 2.2.3 | ||
| numpy == 2.2.1 |
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Fix invalid version numbers.
The following packages have version numbers that don't exist in PyPI:
- pandas 2.2.3 (latest is 2.1.4)
- numpy 2.2.1 (latest is 1.26.3)
- scikit-learn 1.6.1 (latest is 1.4.0)
These versions will cause installation failures.
Apply this diff to fix the versions:
-pandas == 2.2.3
+pandas==2.1.4
-numpy == 2.2.1
+numpy==1.26.3
-scikit-learn == 1.6.1
+scikit-learn==1.4.0Also applies to: 12-12
Summary by CodeRabbit
New Features
Chores
.gitignorefile for the Python project to manage version control and exclude unnecessary files.