Implementaion for stationary sources of pollution #3945
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Co-Authored-By: Noble Mutabazi <[email protected]>
📝 Walkthrough📝 WalkthroughWalkthroughThis pull request introduces significant updates to the application, including the addition of new environment variables Changes
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## staging #3945 +/- ##
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- Coverage 11.73% 11.60% -0.13%
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Files 114 115 +1
Lines 15318 15680 +362
Branches 318 318
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+ Hits 1797 1820 +23
- Misses 13521 13860 +339 |
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Actionable comments posted: 20
🧹 Outside diff range and nitpick comments (11)
src/spatial/controllers/controllers.py (2)
84-94: Consider adding request validation middlewareGiven that these endpoints are part of a critical pollution monitoring system, consider adding request validation middleware to ensure:
- Valid image formats and size limits for
/upload-image- Proper confidence score range validation for
/get-data-by-confidenceWould you like me to provide an example implementation of the validation middleware?
84-94: Document API endpointsConsider adding OpenAPI/Swagger documentation for these new endpoints to help API consumers understand:
- Expected request/response formats
- Valid parameter ranges
- Error responses
Example documentation format:
@controller_bp.route('/upload-image', methods=['POST']) def upload_image_for_prediction(): """ Upload an image for pollutant prediction. --- parameters: - name: image in: formData type: file required: true description: Image file to analyze - name: duration in: query type: integer description: Duration of interest in minutes - name: radius in: query type: number description: Radius of area of interest in meters responses: description: Successful prediction schema: properties: geometry: object confidence_score: number location_profile: object """ return PollutantApis.upload_image()src/spatial/requirements.txt (1)
1-156: Consider organizing dependencies by categoryThe requirements file contains a comprehensive set of dependencies that align well with the PR objectives for handling stationary sources of pollution. However, to improve maintainability, consider organizing the dependencies into logical categories with comments:
# Web Framework Flask~=3.0.3 Flask-Cors~=5.0.0 # Machine Learning tensorflow==<version> torch==<version> scikit-learn~=1.5.2 # Geospatial Processing geopandas==0.13.2 shapely~=2.0.6 # etc...src/spatial/configure.py (1)
31-31: Consider integrating satellite data for pollution source validationGiven the extensive satellite data collections available in this configuration (particularly the SO2, NO2, and CO data from Copernicus), consider leveraging this data to validate or enrich the stationary pollution source detections.
You could enhance the pollution source detection by:
- Cross-referencing detected locations with satellite data
- Using satellite measurements to validate pollution levels
- Incorporating atmospheric conditions from the satellite data
Would you like me to provide a code example demonstrating this integration?
src/spatial/views/pollutant_views.py (2)
113-113: Use Logging Instead of Print StatementsUsing
Replace
- print(f"Querying with min_confidence: {min_confidence}") + app.logger.info(f"Querying with min_confidence: {min_confidence}")For the commented-out print statement:
- # print(f"Inserted documents with IDs: {insert_result.inserted_ids}") + app.logger.info(f"Inserted documents with IDs: {insert_result.inserted_ids}")Also applies to: 94-94
124-135: Refactor to Reduce Code DuplicationThe process of converting retrieved documents to a GeoJSON FeatureCollection is repeated in both
get_data_by_confidenceandget_all_data. Refactoring this into a helper function will enhance maintainability.Consider creating a helper function:
def create_geojson_feature_collection(data): return { "type": "FeatureCollection", "features": [ { "type": "Feature", "geometry": doc["geometry"], "properties": doc["properties"] } for doc in data ] }Then, use this function in both methods:
geojson_data = create_geojson_feature_collection(data)Also applies to: 155-166
src/spatial/models/pollutant_identification.py (5)
19-19: Eliminate unused importmappingfromshapely.geometryThe function
mappingimported on line 19 is not used in the code.🧰 Tools
🪛 Ruff (0.8.0)
19-19:
shapely.geometry.mappingimported but unusedRemove unused import:
shapely.geometry.mapping(F401)
24-24: Discard unused importgeemapThe module
geemapimported on line 24 is not utilized.🧰 Tools
🪛 Ruff (0.8.0)
24-24:
geemapimported but unusedRemove unused import:
geemap(F401)
31-31: Remove unused import frommatplotlib.patchesThe
Polygonclass imported aspltPolygonon line 31 is not used in the code.🧰 Tools
🪛 Ruff (0.8.0)
31-31:
matplotlib.patches.Polygonimported but unusedRemove unused import:
matplotlib.patches.Polygon(F401)
36-37: Eliminate unused MongoDB importsThe following imports are not used and can be removed:
- Line 36:
from pymongo import MongoClient- Line 37:
from bson import json_util🧰 Tools
🪛 Ruff (0.8.0)
36-36:
pymongo.MongoClientimported but unusedRemove unused import:
pymongo.MongoClient(F401)
37-37:
bson.json_utilimported but unusedRemove unused import:
bson.json_util(F401)
185-188: Review exception handling and variable usageWithin the exception block starting at line 185:
- The exception
eis assigned but not used.- The variable
building_typesis assigned a value but not utilized elsewhere.Consider utilizing the exception
efor logging or debugging purposes, or remove it if unnecessary. Also, ifbuilding_typesis not needed, you can remove its assignment to streamline the code.🧰 Tools
🪛 Ruff (0.8.0)
185-185: Local variable
eis assigned to but never usedRemove assignment to unused variable
e(F841)
188-188: Local variable
building_typesis assigned to but never usedRemove assignment to unused variable
building_types(F841)
📜 Review details
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Review profile: CHILL
📒 Files selected for processing (5)
src/spatial/configure.py(1 hunks)src/spatial/controllers/controllers.py(2 hunks)src/spatial/models/pollutant_identification.py(1 hunks)src/spatial/requirements.txt(1 hunks)src/spatial/views/pollutant_views.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.0)
src/spatial/models/pollutant_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)
13-13: geopandas imported but unused
Remove unused import: geopandas
(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)
48-48: Undefined name get_trained_model_from_gcs
(F821)
49-49: Undefined name Config
(F821)
50-50: Undefined name Config
(F821)
62-62: Undefined name service_account
(F821)
63-63: Undefined name Config
(F821)
65-65: Undefined name Config
(F821)
83-83: Undefined name load_tiff
(F821)
88-88: Undefined name normalize_image
(F821)
117-117: Undefined name gp
(F821)
175-175: Undefined name flatten_highway
(F821)
185-185: Local variable e is assigned to but never used
Remove assignment to unused variable e
(F841)
188-188: Local variable building_types is assigned to but never used
Remove assignment to unused variable building_types
(F841)
src/spatial/views/pollutant_views.py
108-108: SyntaxError: unindent does not match any outer indentation level
109-109: SyntaxError: Expected class, function definition or async function definition after decorator
145-145: SyntaxError: unindent does not match any outer indentation level
146-146: SyntaxError: Expected class, function definition or async function definition after decorator
🔇 Additional comments (3)
src/spatial/controllers/controllers.py (1)
16-16: LGTM: Clean import statement
The import of PollutantApis is appropriately placed with other view imports.
src/spatial/requirements.txt (1)
51-51:
Add version specifications for all dependencies
Several critical dependencies are missing version specifications, which could lead to breaking changes and reproducibility issues:
google-cloud-storage
google-generativeai
huggingface_hub
openai
tensorflow
transformers
torch
gunicornFor machine learning projects, it's particularly important to pin versions of ML libraries to ensure consistent behavior.
Also applies to: 53-53, 60-60, 87-87, 132-132, 146-146, 147-147, 151-151
✅ Verification successful
Add version specifications to ensure dependency stability
Based on the latest stable versions from PyPI, here are the recommended version specifications:
google-cloud-storage==2.18.2google-generativeai==0.8.3huggingface_hub==0.26.3openai==1.55.2tensorflow==2.18.0transformers==4.46.3torch==2.5.1gunicorn==23.0.0
Using these pinned versions will ensure reproducible builds and prevent unexpected breaking changes from dependency updates.
Also applies to: 53-53, 60-60, 87-87, 132-132, 146-146, 147-147, 151-151
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Get the latest stable versions of these packages from PyPI
for pkg in google-cloud-storage google-generativeai huggingface_hub openai tensorflow transformers torch gunicorn
do
echo "Package: $pkg"
curl -s "https://pypi.org/pypi/$pkg/json" | jq -r '.info.version'
done
Length of output: 1978
src/spatial/configure.py (1)
31-31: Verify architectural alignment with existing systems
The introduction of MongoDB for storing pollutant data represents a significant architectural decision. Given that this PR is focused on stationary sources of pollution, we should ensure this aligns with the broader system architecture.
Let's verify the existing data storage patterns:
✅ Verification successful
MongoDB is already well-established in the system architecture
Based on the codebase analysis, MongoDB is extensively used across multiple services including:
- Device Registry (
src/device-registry) - Device Monitoring (
src/device-monitoring) - Analytics (
src/analytics) - Data Management (
src/data-mgt) - Predictions (
src/predict) - Authentication (
src/auth-service)
The introduction of MongoDB for storing pollutant data in the spatial service aligns perfectly with the existing architectural patterns, as MongoDB is already the primary database across the platform's microservices architecture.
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash
# Search for existing database connections and patterns
rg -g '!node_modules' -g '!*.pyc' -A 3 'mongodb://' .
rg -g '!node_modules' -g '!*.pyc' -A 3 'MONGO_' .
# Look for related configuration patterns
rg -g '!node_modules' -g '!*.pyc' -A 3 'database.*config' .
Length of output: 50533
| def get_data_by_confidence(): | ||
| return PollutantApis.get_data_by_confidencee() | ||
|
|
There was a problem hiding this comment.
Fix typo in method name
There's a typo in the method name get_data_by_confidencee() (extra 'e' at the end).
Apply this fix:
- return PollutantApis.get_data_by_confidencee()
+ return PollutantApis.get_data_by_confidence()📝 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.
| @controller_bp.route('/get-data-by-confidence', methods=['GET']) | |
| def get_data_by_confidence(): | |
| return PollutantApis.get_data_by_confidencee() | |
| @controller_bp.route('/get-data-by-confidence', methods=['GET']) | |
| def get_data_by_confidence(): | |
| return PollutantApis.get_data_by_confidence() |
| @controller_bp.route('/upload-image', methods=['POST']) | ||
| def upload_image_for_prediction(): | ||
| return PollutantApis.upload_image() | ||
|
|
There was a problem hiding this comment.
💡 Codebase verification
Error handling improvements needed for image upload endpoint
The current implementation has basic error handling but lacks several critical safeguards for file uploads:
- No file type validation (ALLOWED_EXTENSIONS not defined)
- No file size limits (MAX_CONTENT_LENGTH not configured)
- No error handling for image processing failures (preprocess_image and predict_and_get_centroids don't have try-catch blocks)
- No cleanup of temporary files in case of processing errors
Recommended improvements:
- Add file type validation for accepted image formats
- Configure maximum file size limit
- Wrap image processing operations in try-catch blocks
- Implement proper cleanup of uploaded files
- Add error handling for model prediction failures
🔗 Analysis chain
Verify error handling for image upload endpoint
The implementation looks clean, but given that this endpoint handles file uploads, we should ensure proper error handling is in place.
Let's check the implementation of the upload handler:
The previous pattern didn't yield results, so I'm:
- Searching for the class implementation with the method
- Using ripgrep as a backup to find any occurrences of the method name with context
This will help us verify the error handling implementation in the upload endpoint.
I can see the beginning of the error handling in the implementation, but let's get the complete method implementation to verify all error cases are properly handled, including file type validation, size limits, and processing errors.
I can see basic error handling for file presence and empty filename, but I need to verify:
- The complete method implementation to see how it handles processing errors
- If there are file type restrictions defined and enforced
I can see error handling for:
- Missing file and empty filename
- Invalid form parameters
- Need to verify error handling in the preprocessing and prediction methods
- Check if there's any file size limit configured
This will help ensure all potential error cases are covered.
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash
# Search for the upload_image implementation to verify error handling
ast-grep --pattern 'def upload_image():
$$$
}'
Length of output: 50
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#!/bin/bash
# Let's try searching for the class and method implementation
ast-grep --pattern 'class PollutantApis:
$$$
def upload_image($$$):
$$$'
# Also search for any similar method names
rg "upload_image" -A 5
Length of output: 1051
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#!/bin/bash
# Let's examine the complete implementation in pollutant_views.py
rg -A 15 "def upload_image" src/spatial/views/pollutant_views.py
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#!/bin/bash
# Let's get more context to see the complete method implementation
rg -A 30 "def upload_image" src/spatial/views/pollutant_views.py
# Also check for file type validation constants
rg "ALLOWED_EXTENSIONS|UPLOAD_FOLDER" src/spatial/views/pollutant_views.py
Length of output: 1615
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#!/bin/bash
# Let's check the preprocess_image and predict_and_get_centroids methods for error handling
rg -A 10 "def preprocess_image|def predict_and_get_centroids"
# Also check if there's any file size limit configuration
rg "MAX_CONTENT_LENGTH|app\.config\['MAX_CONTENT_LENGTH'\]"
Length of output: 2006
src/spatial/requirements.txt
Outdated
| google-cloud-bigquery[pandas] | ||
| gunicorn | ||
| flask-cors~=5.0.0 | ||
| Flask-Cors==5.0.0 |
There was a problem hiding this comment.
Remove duplicate Flask-Cors entry
The package Flask-Cors appears twice with different version specifiers:
- Line 1:
Flask-Cors==5.0.0 - Line 39:
flask-cors~=5.0.0
This could lead to installation conflicts. Keep only one entry, preferably using the compatible release operator ~=.
-Flask-Cors==5.0.0Also applies to: 39-39
src/spatial/requirements.txt
Outdated
| @@ -17,18 +1,156 @@ | |||
| Flask-Cors==5.0.0 | |||
| Flask==3.0.3 | |||
There was a problem hiding this comment.
Remove duplicate Flask entry
The package Flask appears twice with different cases and version specifiers:
- Line 2:
Flask==3.0.3 - Line 156:
flask~=3.0.3
Maintain consistency in package naming and avoid duplicates.
-flask~=3.0.3Also applies to: 156-156
src/spatial/requirements.txt
Outdated
| opt_einsum==3.4.0 | ||
| osmnx==1.9.3 | ||
| packaging==24.2 | ||
| pandas-gbq |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consolidate BigQuery dependencies
There are multiple overlapping BigQuery-related packages:
pandas-gbq
google-cloud-bigquery
google-cloud-bigquery-storage
google-cloud-bigquery[pandas]The google-cloud-bigquery[pandas] extras already include pandas integration, making pandas-gbq redundant.
-pandas-gbqAlso applies to: 152-152, 153-153, 154-154
| 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.
Ensure service_account and Config are defined for Earth Engine initialization
In the initialize_earth_engine method, service_account and Config are not defined:
- Line 62:
service_account - Lines 63 and 65:
Config
To resolve this issue, import service_account from the appropriate module (e.g., from google.oauth2 import service_account) and define or import Config with the necessary credentials and project information.
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62-62: Undefined name service_account
(F821)
63-63: Undefined name Config
(F821)
65-65: Undefined name Config
(F821)
| def get_environment_profile(latitude, longitude, months, radius): | ||
| today = datetime.date.today() | ||
| start_date = ee.Date.fromYMD(today.year, today.month, today.day).advance(-months, 'month') | ||
| end_date = ee.Date.fromYMD(today.year, today.month, today.day) | ||
| point = ee.Geometry.Point(longitude, latitude) | ||
|
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||
| def get_mean(collection, band): | ||
| collection = collection.filterDate(start_date, end_date).filterBounds(point).select(band) | ||
| mean_value = collection.mean().reduceRegion(ee.Reducer.mean(), point.buffer(radius), scale=1000).get(band) | ||
| return mean_value.getInfo() if mean_value else None | ||
|
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||
| return { | ||
| 'mean_AOD': get_mean(ee.ImageCollection('MODIS/061/MCD19A2_GRANULES'), 'Optical_Depth_047'), | ||
| 'mean_CO': get_mean(ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO'), 'CO_column_number_density'), | ||
| 'mean_NO2': get_mean(ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2'), 'tropospheric_NO2_column_number_density') | ||
| } |
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🛠️ Refactor suggestion
Add error handling for Earth Engine data retrieval
In the get_environment_profile method, external API calls are made without error handling. Failures in these calls could lead to unhandled exceptions.
Consider wrapping the Earth Engine API calls with try-except blocks to handle potential errors gracefully. This will enhance the robustness of your application.
| road_type_lengths = defaultdict(float) | ||
| # Calculate the length for each road type | ||
| for _, edge in edges.iterrows(): | ||
| for road_type in flatten_highway(edge['highway']): |
There was a problem hiding this comment.
Correct method call to flatten_highway
The method flatten_highway is called without specifying the class name:
- Line 175:
for road_type in flatten_highway(edge['highway']):
Since flatten_highway is a static method of the PredictionAndProcessing class, it should be called using the class name:
- for road_type in flatten_highway(edge['highway']):
+ for road_type in PredictionAndProcessing.flatten_highway(edge['highway']):📝 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 road_type in flatten_highway(edge['highway']): | |
| for road_type in PredictionAndProcessing.flatten_highway(edge['highway']): |
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175-175: Undefined name flatten_highway
(F821)
| geom['properties']['confidence_score'] = mean_confidence | ||
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| # Create a GeoDataFrame from the geometries | ||
| gpd_polygonized_raster = gp.GeoDataFrame.from_features(geoms, crs='epsg:3857') |
There was a problem hiding this comment.
Fix typo: Undefined name gp should be gpd
On line 117, gp.GeoDataFrame.from_features is used, but gp is not defined. This appears to be a typo.
Replace gp with gpd to use the GeoPandas library correctly:
- gpd_polygonized_raster = gp.GeoDataFrame.from_features(geoms, crs='epsg:3857')
+ gpd_polygonized_raster = gpd.GeoDataFrame.from_features(geoms, crs='epsg:3857')📝 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.
| gpd_polygonized_raster = gp.GeoDataFrame.from_features(geoms, crs='epsg:3857') | |
| gpd_polygonized_raster = gpd.GeoDataFrame.from_features(geoms, crs='epsg:3857') |
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117-117: Undefined name gp
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| @staticmethod | ||
| def load_tiff(file_path): | ||
| with rasterio.open(file_path) as src: | ||
| image = src.read() | ||
| profile = src.profile | ||
| return reshape_as_image(image), profile | ||
|
|
||
| # Function to normalize the image | ||
| @staticmethod | ||
| def normalize_image(image): | ||
| return image / np.max(image) | ||
|
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| # Function to preprocess the image for prediction | ||
| @staticmethod | ||
| def preprocess_image(image_path): | ||
| 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) | ||
| return image, image_profile | ||
| @staticmethod |
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Correct static method calls within the class
Static methods are being called without specifying the class name:
- Line 83:
image, image_profile = load_tiff(image_path) - Line 88:
image = normalize_image(image)
Since these are static methods within the PredictionAndProcessing class, they should be called using the class name:
- image, image_profile = load_tiff(image_path)
+ image, image_profile = PredictionAndProcessing.load_tiff(image_path)- image = normalize_image(image)
+ image = PredictionAndProcessing.normalize_image(image)Alternatively, if you prefer cleaner code, consider changing these methods to class methods or instance methods.
📝 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 load_tiff(file_path): | |
| with rasterio.open(file_path) as src: | |
| image = src.read() | |
| profile = src.profile | |
| return reshape_as_image(image), profile | |
| # Function to normalize the image | |
| @staticmethod | |
| def normalize_image(image): | |
| return image / np.max(image) | |
| # Function to preprocess the image for prediction | |
| @staticmethod | |
| def preprocess_image(image_path): | |
| 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) | |
| return image, image_profile | |
| @staticmethod | |
| @staticmethod | |
| def load_tiff(file_path): | |
| with rasterio.open(file_path) as src: | |
| image = src.read() | |
| profile = src.profile | |
| return reshape_as_image(image), profile | |
| # Function to normalize the image | |
| @staticmethod | |
| def normalize_image(image): | |
| return image / np.max(image) | |
| # Function to preprocess the image for prediction | |
| @staticmethod | |
| def preprocess_image(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 = PredictionAndProcessing.normalize_image(image) | |
| return image, image_profile | |
| @staticmethod |
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83-83: Undefined name load_tiff
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88-88: Undefined name normalize_image
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Thanks. @pronapro for this. The future is indeed bright.
- I can see that the deploy preview for the PR is failing. Please investigate this.
- Can you please reach out (individually) to @wabinyai and @Mnoble-19 to thoroughly review this PR?
Thanks again.
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Actionable comments posted: 11
🧹 Outside diff range and nitpick comments (7)
src/spatial/Dockerfile (3)
4-12: Consider enhancing the system dependencies installationThe GDAL dependencies installation looks good, but could be improved for better reliability and efficiency.
Consider these improvements:
RUN apt-get update && apt-get install -y --no-install-recommends \ gcc \ build-essential \ libgomp1 \ gdal-bin \ libgdal-dev \ libproj-dev \ proj-bin \ + python3-dev \ + && apt-get clean \ && rm -rf /var/lib/apt/lists/*Additionally, consider pinning specific versions of critical packages for reproducible builds.
17-19: Enhance GDAL configuration with additional environment variablesWhile the basic GDAL include paths are set, consider adding more configuration for optimal GDAL operation.
Consider adding these additional environment variables:
ENV CPLUS_INCLUDE_PATH=/usr/include/gdal ENV C_INCLUDE_PATH=/usr/include/gdal +ENV GDAL_VERSION=$(gdal-config --version) +ENV PROJ_LIB=/usr/share/proj
Line range hint
1-43: Consider optimizing for geospatial processing workloadsGiven that this service handles geospatial data processing:
- Consider using a more specialized base image like
gdal/gdalfor better performance- Memory and CPU requirements might need adjustment for processing large datasets
- Consider adding health checks for GDAL functionality
Example health check addition:
FROM base as production ENV FLASK_ENV=production +HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ + CMD gdal-config --version || exit 1 CMD gunicorn --bind=0.0.0.0:5000 app:appsrc/spatial/requirements.txt (1)
37-37: Clarify web framework strategy and fix versionsThe presence of both FastAPI and Flask could indicate:
- A transition in progress
- Unnecessary complexity maintaining two web frameworks
Consider:
- Choosing one framework (FastAPI is more modern and async-first)
- If both are needed, document the reason
- Specify versions for stability:
-fastapi==0.115.0 # Specify if needed, otherwise can be removed. -flask-cors -flask +fastapi==0.115.0 +flask-cors==5.0.0 +flask==3.0.3Also applies to: 39-39, 153-153
src/spatial/controllers/controllers.py (2)
84-87: Enhance function name and add API documentationThe function name
upload_image_for_predictioncould be more specific to its purpose with stationary pollution sources. Additionally, add docstring documentation for API consumers.@controller_bp.route('/upload-image', methods=['POST']) -def upload_image_for_prediction(): +def upload_stationary_pollution_source(): + """Upload and analyze an image of a stationary pollution source. + + Returns: + JSON response containing geometry, confidence score, and environmental profiles + """ return PollutantApis.upload_image()
92-94: Add pagination for get-all-data endpointThe endpoint retrieving all data should implement pagination to prevent performance issues with large datasets.
@controller_bp.route('/get-all-data', methods=['GET']) def get_all_data(): + """Retrieve all pollution source data with pagination. + + Query Parameters: + page (int): Page number (default: 1) + per_page (int): Items per page (default: 20) + + Returns: + JSON response containing paginated pollution source data + """ + page = request.args.get('page', 1, type=int) + per_page = request.args.get('per_page', 20, type=int) + if page < 1 or per_page < 1: + return jsonify({'error': 'Invalid pagination parameters'}), 400 return PollutantApis.get_all_data()src/spatial/views/pollutant_views.py (1)
6-11: Enhance MongoDB connection handlingThe MongoDB connection setup could be more robust with connection error handling and connection pooling.
Consider enhancing the connection setup:
def connect_mongo(): + try: + # Enable connection pooling + client = MongoClient(Config.MONGO_URI, maxPoolSize=50) - client = MongoClient(Config.MONGO_URI) db = client[Config.DB_NAME] + # Verify connection + client.server_info() return db + except Exception as e: + raise Exception(f"Failed to connect to MongoDB: {e}") db = connect_mongo() collection = db["pollutant_data"]
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (6)
src/spatial/Dockerfile(1 hunks)src/spatial/configure.py(1 hunks)src/spatial/controllers/controllers.py(3 hunks)src/spatial/models/pollutant_identification.py(1 hunks)src/spatial/requirements.txt(1 hunks)src/spatial/views/pollutant_views.py(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- src/spatial/configure.py
🧰 Additional context used
🪛 Ruff (0.8.0)
src/spatial/models/pollutant_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)
62-62: Undefined name service_account
(F821)
83-83: Undefined name load_tiff
(F821)
88-88: Undefined name normalize_image
(F821)
185-185: Local variable e is assigned to but never used
Remove assignment to unused variable e
(F841)
188-188: Local variable building_types is assigned to but never used
Remove assignment to unused variable building_types
(F841)
src/spatial/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
(F821)
20-20: Undefined name request
(F821)
22-22: Undefined name jsonify
(F821)
25-25: Undefined name secure_filename
(F821)
26-26: Undefined name UPLOAD_FOLDER
(F821)
31-31: Undefined name request
(F821)
32-32: Undefined name request
(F821)
34-34: Undefined name jsonify
(F821)
37-37: Undefined name time
(F821)
44-44: Undefined name time
(F821)
48-48: Undefined name jsonify
(F821)
52-52: Undefined name datetime
(F821)
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)
81-81: Undefined name mapping
(F821)
98-98: Undefined name jsonify
(F821)
101-101: Undefined name jsonify
(F821)
114-114: Undefined name request
(F821)
124-124: Undefined name jsonify
(F821)
139-139: Undefined name Response
(F821)
140-140: Undefined name json
(F821)
140-140: Undefined name json_util
(F821)
145-145: Undefined name jsonify
(F821)
155-155: Undefined name jsonify
(F821)
170-170: Undefined name Response
(F821)
171-171: Undefined name json
(F821)
171-171: Undefined name json_util
(F821)
176-176: Undefined name jsonify
(F821)
🔇 Additional comments (9)
src/spatial/requirements.txt (1)
91-91: Consolidate Google Cloud dependencies
The BigQuery-related packages have overlapping functionality:
pandas-gbqgoogle-cloud-bigquerygoogle-cloud-bigquery-storage
Keep only the necessary ones to avoid conflicts.
Also applies to: 150-151
src/spatial/controllers/controllers.py (3)
16-16: LGTM: Clean import statement
The import follows Python conventions and is logically placed with other view imports.
84-87:
Add request validation for image upload endpoint
The current implementation delegates all validation to the PollutantApis class. Consider adding basic request validation at the controller level.
@controller_bp.route('/upload-image', methods=['POST'])
def upload_image_for_prediction():
+ if 'image' not in request.files:
+ return jsonify({'error': 'No image file provided'}), 400
+ if not request.form.get('duration'):
+ return jsonify({'error': 'Duration parameter is required'}), 400
+ if not request.form.get('radius'):
+ return jsonify({'error': 'Radius parameter is required'}), 400
return PollutantApis.upload_image()84-94: Verify error handling implementation in PollutantApis
Let's verify the error handling implementation in the PollutantApis class methods.
✅ Verification successful
Error handling is well-implemented in PollutantApis
The verification shows comprehensive error handling across all endpoints:
- Input validation with proper 400 responses for invalid requests
- Not found conditions returning 404 status
- Server errors properly caught with 500 status codes
- Detailed error messages in JSON format for all error cases
Specific error cases handled:
- File upload validation
- Parameter validation for months and radius
- MongoDB operations
- Data processing for centroids
- Empty result sets
- Generic exception handling
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check error handling in PollutantApis implementation
# Search for try-except blocks in the implementation
echo "Checking error handling in PollutantApis methods..."
rg -A 10 "try:|except:|finally:" src/spatial/views/pollutant_views.py
# Check for response error handling
echo "Checking response formatting..."
rg "jsonify.*error|abort.*[45][0-9]{2}" src/spatial/views/pollutant_views.py
Length of output: 4241
src/spatial/views/pollutant_views.py (2)
75-77: 🛠️ Refactor suggestion
Improve error handling granularity
The current error handling is too broad and exposes internal error details.
Implement more specific error handling:
- except Exception as e:
- return jsonify({"error": f"Error processing data for centroid: {e}"}), 500
+ except ValueError as e:
+ return jsonify({"error": "Invalid coordinates provided"}), 400
+ except TimeoutError:
+ return jsonify({"error": "Processing timeout occurred"}), 504
+ except Exception as e:
+ # Log the actual error for debugging
+ current_app.logger.error(f"Error processing centroid: {e}")
+ return jsonify({"error": "An error occurred while processing the data"}), 500Likely invalid or redundant comment.
🧰 Tools
🪛 Ruff (0.8.0)
76-76: Undefined name jsonify
(F821)
16-27:
Enhance file upload validation and security
The current file validation is basic and could be more secure.
Add comprehensive file validation:
+ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}
+def allowed_file(filename):
+ return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
if 'file' not in request.files:
return jsonify({"error": "No file part in the request"}), 400
file = request.files['file']
if file.filename == '':
return jsonify({"error": "No selected file"}), 400
+if not allowed_file(file.filename):
+ return jsonify({"error": "Invalid file type. Allowed types: png, jpg, jpeg, gif"}), 400
+# Generate unique filename to prevent overwrites
+timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
+filename = f"{timestamp}_{secure_filename(file.filename)}"
-filename = secure_filename(file.filename)
file_path = os.path.join(UPLOAD_FOLDER, filename)
+
+# Ensure upload directory exists
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
file.save(file_path)Likely invalid or redundant comment.
🧰 Tools
🪛 Ruff (0.8.0)
16-16: Undefined name request
(F821)
17-17: Undefined name jsonify
(F821)
20-20: Undefined name request
(F821)
22-22: Undefined name jsonify
(F821)
25-25: Undefined name secure_filename
(F821)
26-26: Undefined name UPLOAD_FOLDER
(F821)
src/spatial/models/pollutant_identification.py (3)
39-41: Ensure get_trained_model_from_gcs and Config are correctly imported
While get_trained_model_from_gcs and Config are imported from configure, please verify that:
- The
configuremodule exists and is accessible. Configcontains the required attributes (GOOGLE_CLOUD_PROJECT_ID,PROJECT_BUCKET).get_trained_model_from_gcsfunctions as expected.
Would you like assistance in verifying these imports and configurations?
Also applies to: 48-51
62-62:
Import the service_account module
The service_account module is used but not imported, leading to a potential NameError. Please add the following import statement:
+ from google.oauth2 import service_accountLikely invalid or redundant comment.
🧰 Tools
🪛 Ruff (0.8.0)
62-62: Undefined name service_account
(F821)
60-65:
Move initialize_earth_engine method inside the class
The @staticmethod decorator is applied to initialize_earth_engine, but it's defined outside of the PredictionAndProcessing class. To maintain proper structure, move this method inside the class:
- @staticmethod
- def initialize_earth_engine():
+ class PredictionAndProcessing:
+ @staticmethod
+ def initialize_earth_engine():Likely invalid or redundant comment.
🧰 Tools
🪛 Ruff (0.8.0)
62-62: Undefined name service_account
(F821)
| #install tensorflow | ||
| #RUN pip install tensorflow==2.11.1 --no-deps |
There was a problem hiding this comment.
💡 Codebase verification
TensorFlow dependency is actively used and should not be removed
The TensorFlow installation in the Dockerfile should not be commented out as it's actively used in src/spatial/models/pollutant_identification.py for:
- Loading a pre-trained model using
tf.keras.models.load_model - Making predictions with the loaded model
- Processing image data for pollutant identification
The code shows critical dependencies on TensorFlow that are essential for the application's machine learning functionality. Please uncomment and restore the TensorFlow installation in the Dockerfile.
🔗 Analysis chain
Verify the removal of TensorFlow dependency
The TensorFlow installation has been commented out without explanation. This could impact the application's functionality, especially for machine learning operations.
Let's check if TensorFlow is still required:
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check if TensorFlow is still referenced in the codebase
echo "Checking for TensorFlow imports and usage..."
rg -l "import tensorflow" || echo "No direct TensorFlow imports found"
rg -l "from tensorflow" || echo "No direct TensorFlow imports found"
# Check if it's in requirements.txt
echo "Checking requirements.txt for TensorFlow..."
grep -i "tensorflow" requirements.txt || echo "TensorFlow not found in requirements.txt"
Length of output: 554
Script:
#!/bin/bash
# Check the content of the file that imports tensorflow
echo "Content of pollutant_identification.py:"
cat src/spatial/models/pollutant_identification.py
# Check if there are any other potential ML/DL frameworks being used
echo -e "\nChecking for other ML frameworks:"
rg -l "import torch|from torch|import keras|from keras"
# Check for any dependency files
echo -e "\nLooking for dependency files:"
fd -g "*requirements*.txt" -g "setup.py" -g "pyproject.toml"
Length of output: 9325
| google-cloud-bigquery[pandas] | ||
| gunicorn | ||
| flask-cors~=5.0.0 | ||
| tensorflow==2.15.1 |
There was a problem hiding this comment.
Specify versions for core ML dependencies
The ML stack requires careful version management to ensure compatibility:
- TensorFlow 2.15.1 requires specific numpy versions
- torch and transformers should have pinned versions for reproducibility
Apply these changes:
tensorflow==2.15.1
-#keras==2.9.0 # Ensure compatibility with TensorFlow.
-numpy
+numpy>=1.26.0,<2.1.0 # Required by TensorFlow 2.15.1
-transformers
-torch
+transformers==4.36.0
+torch==2.1.1Also applies to: 75-75, 85-85, 144-145
| contourpy | ||
| cycler | ||
| decorator | ||
| dnspython | ||
| earthengine-api~=1.1.1 |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Standardize geospatial dependency versions
The geospatial stack shows inconsistent version specifications:
- Some use
~=(earthengine-api, esda) - Others use
==(fiona, geopandas)
Standardize version specifications:
-earthengine-api~=1.1.1
-esda~=2.6.0
-fiona==1.10.1
-geopandas==0.13.2
-pyproj==3.5.0
-shapely~=2.0.6
+earthengine-api==1.1.1
+esda==2.6.0
+fiona==1.10.1
+geopandas==0.13.2
+pyproj==3.5.0
+shapely==2.0.6Also applies to: 34-34, 38-38, 46-47, 108-108, 125-125
| @@ -17,18 +1,153 @@ | |||
| tensorflow==2.15.1 | |||
| wrapt>=1.11.0,<1.15 | |||
| Jinja2 | |||
There was a problem hiding this comment.
Pin versions for reproducible builds
Many dependencies lack version specifications, which could lead to unexpected breaks when new versions are released.
At minimum, pin versions for core dependencies:
-Jinja2
-Markdown
-MarkupSafe
-Werkzeug
-anyio
+Jinja2==3.1.3
+Markdown==3.5.1
+MarkupSafe==2.1.3
+Werkzeug==3.0.1
+anyio==4.2.0Also applies to: 5-9, 13-31
| def get_data_by_confidence(): | ||
| return PollutantApis.get_data_by_confidence() | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add parameter validation and documentation for confidence endpoint
The confidence score endpoint needs input validation and documentation for API consumers.
@controller_bp.route('/get-data-by-confidence', methods=['GET'])
def get_data_by_confidence():
+ """Retrieve pollution source data filtered by confidence score.
+
+ Query Parameters:
+ min_confidence (float): Minimum confidence score (0.0 to 1.0)
+
+ Returns:
+ JSON response containing filtered pollution source data
+ """
+ min_confidence = request.args.get('min_confidence', type=float)
+ if min_confidence is None or not 0 <= min_confidence <= 1:
+ return jsonify({'error': 'Invalid min_confidence parameter'}), 400
return PollutantApis.get_data_by_confidence()Committable suggestion skipped: line range outside the PR's diff.
| def get_data_by_confidence(): | ||
| try: | ||
| # Parse the minimum confidence score from query parameters | ||
| min_confidence = float(request.args.get('min_confidence', 0)) | ||
| print(f"Querying with min_confidence: {min_confidence}") | ||
|
|
||
| # Query documents where confidence_score is greater than or equal to min_confidence | ||
| data = list(collection.find({ | ||
| "properties.confidence_score": {"$gte": min_confidence} | ||
| })) | ||
|
|
||
| # If no documents match, return an error message | ||
| if not data: | ||
| return jsonify({"error": "No data found for the given confidence score."}), 404 |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add pagination and remove debug print
The endpoint could benefit from pagination for large datasets, and contains a debug print statement.
Implement pagination and clean up debugging:
def get_data_by_confidence():
try:
# Parse the minimum confidence score from query parameters
min_confidence = float(request.args.get('min_confidence', 0))
- print(f"Querying with min_confidence: {min_confidence}")
+
+ # Add pagination parameters
+ page = int(request.args.get('page', 1))
+ per_page = int(request.args.get('per_page', 10))
+ skip = (page - 1) * per_page
# Query documents where confidence_score is greater than or equal to min_confidence
data = list(collection.find({
"properties.confidence_score": {"$gte": min_confidence}
- }))
+ }).skip(skip).limit(per_page))Committable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff (0.8.0)
114-114: Undefined name request
(F821)
124-124: Undefined name jsonify
(F821)
| def get_all_data(): | ||
| try: | ||
| # Retrieve all documents from the collection | ||
| data = list(collection.find({})) | ||
|
|
||
| # If no documents are found, return an error message | ||
| if not data: | ||
| return jsonify({"error": "No data found in the database."}), 404 | ||
|
|
||
| # Convert the retrieved documents to a GeoJSON FeatureCollection | ||
| geojson_data = { | ||
| "type": "FeatureCollection", | ||
| "features": [ | ||
| { | ||
| "type": "Feature", | ||
| "geometry": doc["geometry"], | ||
| "properties": doc["properties"] | ||
| } for doc in data | ||
| ] | ||
| } | ||
|
|
||
| # Return the GeoJSON data | ||
| return Response( | ||
| json.dumps(geojson_data, default=json_util.default), | ||
| mimetype='application/geo+json' | ||
| ) | ||
|
|
||
| except Exception as e: | ||
| return jsonify({"error": f"Failed to fetch data: {e}"}), 500 |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Optimize performance for large datasets
The current implementation could face performance issues with large datasets.
Consider implementing these optimizations:
@staticmethod
def get_all_data():
try:
+ # Add pagination parameters
+ page = int(request.args.get('page', 1))
+ per_page = int(request.args.get('per_page', 10))
+ skip = (page - 1) * per_page
+
+ # Get total count for pagination metadata
+ total_count = collection.count_documents({})
+
# Retrieve all documents from the collection
- data = list(collection.find({}))
+ data = list(collection.find({}).skip(skip).limit(per_page))
# If no documents are found, return an error message
if not data:
return jsonify({"error": "No data found in the database."}), 404
# Convert the retrieved documents to a GeoJSON FeatureCollection
geojson_data = {
"type": "FeatureCollection",
- "features": [
+ "features": [
{
"type": "Feature",
"geometry": doc["geometry"],
"properties": doc["properties"]
} for doc in data
- ]
+ ],
+ "metadata": {
+ "page": page,
+ "per_page": per_page,
+ "total_count": total_count,
+ "total_pages": (total_count + per_page - 1) // per_page
+ }
}Committable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff (0.8.0)
155-155: Undefined name jsonify
(F821)
170-170: Undefined name Response
(F821)
171-171: Undefined name json
(F821)
171-171: Undefined name json_util
(F821)
176-176: Undefined name jsonify
(F821)
| import os | ||
| import time |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Remove unused imports to enhance code clarity
The following imports are unused and can be safely removed:
- Line 2:
import os - Line 3:
import time - Line 8:
import json - Line 12:
import pandas as pd - Line 19:
from shapely.geometry import mapping - Line 24:
import geemap - Line 31:
from matplotlib.patches import Polygon as pltPolygon - Line 36:
from pymongo import MongoClient - Line 37:
from bson import json_util
Cleaning up unused imports helps maintain readable and efficient code.
Also applies to: 8-8, 12-12, 19-19, 24-24, 31-31, 36-37
🧰 Tools
🪛 Ruff (0.8.0)
2-2: os imported but unused
Remove unused import: os
(F401)
3-3: time imported but unused
Remove unused import: time
(F401)
| # Function to preprocess the image for prediction | ||
| @staticmethod | ||
| def preprocess_image(image_path): | ||
| image, image_profile = load_tiff(image_path) |
There was a problem hiding this comment.
Correct static method calls within the class
Static methods should be called using the class name or by fully qualifying the method. Update the method calls as follows:
- Line 83: Replace
load_tiff(image_path)withPredictionAndProcessing.load_tiff(image_path) - Line 88: Replace
normalize_image(image)withPredictionAndProcessing.normalize_image(image)
This ensures clarity and prevents potential errors.
Also applies to: 88-88
🧰 Tools
🪛 Ruff (0.8.0)
83-83: Undefined name load_tiff
(F821)
| except Exception as e: | ||
| number_of_buildings = 'Error' | ||
| building_density = 'Error' | ||
| building_types = 'Error' |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Handle unused variables e and building_types
The variables e and building_types are assigned but never used:
- Line 185: Exception variable
eis assigned but not utilized. - Line 188:
building_typesis assigned but not used afterward.
Consider removing these variables or using them appropriately to avoid confusion.
🧰 Tools
🪛 Ruff (0.8.0)
185-185: Local variable e is assigned to but never used
Remove assignment to unused variable e
(F841)
188-188: Local variable building_types is assigned to but never used
Remove assignment to unused variable building_types
(F841)
Description
[Implementation for API endpoints for stationary sources of pollution ]
Changes Made
Testing
Affected Services
Summary by CodeRabbit
Release Notes
New Features
Bug Fixes
Documentation
These updates enhance the application's ability to process geospatial data and improve user interaction with pollutant data.