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Dockerfile

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+# FROM python:3.9-slim
+FROM python:3.11-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy all files from the current directory to the container's working directory
+COPY . .
+
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Define the command to start the application using Gunicorn with 4 worker processes
+# - `-w 4`: Uses 4 worker processes for handling requests
+# - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
+# - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:app"]

app.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app with a name
+app = Flask("SuperKart product sales revenue Prediction")
+
+# Load the trained SuperKart sales revenue prediction model
+model_superkart = joblib.load("superkart_revenue_prediction_model_v1_0.joblib")
+
+# Define a route for the home page
+@app.get('/')
+def home():
+    return "Welcome to the SuperKart Revenue Prediction API, created by Vrundav Gamit"
+
+# Define an endpoint to predict revenue for single data point
+# POST endpoint
+# v1 in the url is a industry practise for versioning of endpoint urls
+# For example v1 urls can be used to testing, v2 urls can be used for validation testing
+@app.post('/v1/predictrevenue')
+# @app.route('/v1/predictrevenue', methods=['POST'])
+def predict_revenue():
+    # Get JSON data from the request
+    sales_data = request.get_json()
+
+    # Extract relevant store and product features from the input data
+    payload = {
+        'Product_Weight': sales_data['Product_Weight'],
+        'Product_Allocated_Area': sales_data['Product_Allocated_Area'],
+        'Product_MRP': sales_data['Product_MRP'],
+        'Store_Size': sales_data['Store_Size'],
+        'Store_Location_City_Type': sales_data['Store_Location_City_Type'],
+        'Store_Type': sales_data['Store_Type'],
+        'Product_Sugar_Content': sales_data['Product_Sugar_Content'],
+        'Product_Type': sales_data['Product_Type']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([payload])
+
+    # Make a sales revenue prediction using the trained model
+    prediction = model_superkart.predict(input_data).tolist()[0]
+
+    # Return the prediction as a JSON response
+    return jsonify({'Prediction': prediction})
+
+# Define an endpoint to predict revenue for a batch of store and product data
+@app.post('/v1/predictrevenuebatch')
+def predict_revenue_batch():
+    # Get the uploaded CSV file from the request
+    file = request.files['file']
+
+    # Read the file into a DataFrame
+    input_data = pd.read_csv(file)
+
+    # Drop Product_Id from the input before performing a predict
+    # As Product_Id is not one of the input features of the model
+    predictions = model_superkart.predict(input_data.drop("Product_Id",axis=1)).tolist()
+    
+    product_id_list = input_data.product_Id.values.tolist()
+    output_dict = dict(zip(product_id_list, predictions))
+
+    return output_dict
+    
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

@@ -0,0 +1,194 @@
+absl-py==2.3.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.12.0
+aiosignal==1.3.2
+altair==5.5.0
+annotated-types==0.7.0
+anyio==4.9.0
+asgiref==3.8.1
+asttokens==3.0.0
+attrs==25.3.0
+backoff==2.2.1
+bcrypt==4.3.0
+blinker==1.9.0
+build==1.2.2.post1
+cachetools==5.5.2
+certifi==2025.4.26
+charset-normalizer==3.4.2
+chromadb==1.0.10
+click==8.1.8
+colorama==0.4.6
+coloredlogs==15.0.1
+comm==0.2.2
+contourpy==1.3.2
+cycler==0.12.1
+dataclasses-json==0.6.7
+debugpy==1.8.14
+decorator==5.2.1
+Deprecated==1.2.18
+diskcache==5.6.3
+distro==1.9.0
+durationpy==0.10
+executing==2.2.0
+fastapi==0.115.9
+filelock==3.18.0
+Flask==3.1.1
+flatbuffers==25.2.10
+fonttools==4.58.2
+frozenlist==1.6.0
+fsspec==2025.5.1
+gitdb==4.0.12
+GitPython==3.1.44
+google-auth==2.40.2
+googleapis-common-protos==1.70.0
+greenlet==3.2.2
+grpcio==1.71.0
+h11==0.16.0
+h5py==3.14.0
+httpcore==1.0.9
+httptools==0.6.4
+httpx==0.28.1
+httpx-sse==0.4.0
+huggingface-hub==0.32.0
+humanfriendly==10.0
+idna==3.10
+importlib_metadata==8.6.1
+importlib_resources==6.5.2
+ipykernel==6.29.5
+ipython==9.2.0
+ipython_pygments_lexers==1.1.1
+itsdangerous==2.2.0
+jedi==0.19.2
+Jinja2==3.1.6
+joblib==1.5.1
+jsonpatch==1.33
+jsonpointer==3.0.0
+jsonschema==4.23.0
+jsonschema-specifications==2025.4.1
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+keras==3.10.0
+kiwisolver==1.4.8
+kubernetes==32.0.1
+langchain==0.3.25
+langchain-chroma==0.2.4
+langchain-community==0.3.24
+langchain-core==0.3.61
+langchain-huggingface==0.2.0
+langchain-text-splitters==0.3.8
+langsmith==0.3.42
+llama_cpp_python==0.1.85
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+marshmallow==3.26.1
+matplotlib==3.10.3
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+ml_dtypes==0.5.1
+mmh3==5.1.0
+mpmath==1.3.0
+multidict==6.4.4
+mypy_extensions==1.1.0
+namex==0.1.0
+narwhals==1.46.0
+nest-asyncio==1.6.0
+networkx==3.4.2
+numpy==2.3.0
+oauthlib==3.2.2
+onnxruntime==1.22.0
+opencv-python==4.11.0.86
+opentelemetry-api==1.33.1
+opentelemetry-exporter-otlp-proto-common==1.33.1
+opentelemetry-exporter-otlp-proto-grpc==1.33.1
+opentelemetry-instrumentation==0.54b1
+opentelemetry-instrumentation-asgi==0.54b1
+opentelemetry-instrumentation-fastapi==0.54b1
+opentelemetry-proto==1.33.1
+opentelemetry-sdk==1.33.1
+opentelemetry-semantic-conventions==0.54b1
+opentelemetry-util-http==0.54b1
+optree==0.16.0
+orjson==3.10.18
+overrides==7.7.0
+packaging==24.2
+pandas==2.2.3
+parso==0.8.4
+pillow==11.2.1
+platformdirs==4.3.8
+posthog==4.2.0
+prompt_toolkit==3.0.51
+propcache==0.3.1
+protobuf==5.29.4
+psutil==7.0.0
+pure_eval==0.2.3
+pyarrow==20.0.0
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pydantic==2.11.5
+pydantic-settings==2.9.1
+pydantic_core==2.33.2
+pydeck==0.9.1
+pyenv-win==3.1.1
+Pygments==2.19.1
+PyMuPDF==1.26.0
+pyparsing==3.2.3
+PyPika==0.48.9
+pyproject_hooks==1.2.0
+pyreadline3==3.5.4
+python-dateutil==2.9.0.post0
+python-dotenv==1.1.0
+pytz==2025.2
+pywin32==310
+PyYAML==6.0.2
+pyzmq==26.4.0
+referencing==0.36.2
+regex==2024.11.6
+requests==2.32.3
+requests-oauthlib==2.0.0
+requests-toolbelt==1.0.0
+rich==14.0.0
+rpds-py==0.25.1
+rsa==4.9.1
+safetensors==0.5.3
+scikit-learn==1.6.1
+scipy==1.15.3
+seaborn==0.13.2
+sentence-transformers==4.1.0
+setuptools==80.8.0
+shellingham==1.5.4
+six==1.17.0
+smmap==5.0.2
+sniffio==1.3.1
+SQLAlchemy==2.0.41
+stack-data==0.6.3
+starlette==0.45.3
+streamlit==1.46.1
+sympy==1.14.0
+tenacity==9.1.2
+threadpoolctl==3.6.0
+tiktoken==0.9.0
+tokenizers==0.21.1
+toml==0.10.2
+torch==2.7.0
+tornado==6.5.1
+tqdm==4.67.1
+traitlets==5.14.3
+transformers==4.52.3
+typer==0.15.4
+typing-inspect==0.9.0
+typing-inspection==0.4.1
+typing_extensions==4.13.2
+tzdata==2025.2
+urllib3==2.4.0
+uvicorn==0.34.2
+watchdog==6.0.0
+watchfiles==1.0.5
+wcwidth==0.2.13
+websocket-client==1.8.0
+websockets==15.0.1
+Werkzeug==3.1.3
+wrapt==1.17.2
+xgboost==3.0.2
+yarl==1.20.0
+zipp==3.21.0
+zstandard==0.23.0

superkart_revenue_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d15263fed5f116b2a3272497b48656c15c668caaef28a98839f9be4425a8a5ae
+size 311137