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Dockerfile

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+FROM python:3.9-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 --upgrade -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:house_price_api"]

app.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app
+sales_api = Flask("Product Store Sales Predictor")
+
+# Load the trained sales prediction model
+# model =  joblib.load("deployment_files/sales_prediction_model_v1_0.pkl")
+model = saved_model
+
+# Define a route for the home page
+@sales_api.get('/')
+def home():
+    return "Welcome to the Product Store Sales Prediction API!"
+
+# ==============================
+# Single record prediction endpoint
+# ==============================
+@sales_api.post('/v1/sales')
+def predict_sales():
+    try:
+        # Get JSON data from the request
+        data = request.get_json()
+
+        # Extract all required product/store features from the input data
+        sample = {
+            'Product_Id': data['Product_Id'],
+            'Product_Weight': data['Product_Weight'],
+            'Product_Sugar_Content': data['Product_Sugar_Content'],
+            'Product_Allocated_Area': data['Product_Allocated_Area'],
+            'Product_Type': data['Product_Type'],
+            'Product_MRP': data['Product_MRP'],
+            'Store_Id': data['Store_Id'],
+            'Store_Establishment_Year': data['Store_Establishment_Year'],
+            'Store_Size': data['Store_Size'],
+            'Store_Location_City_Type': data['Store_Location_City_Type'],
+            'Store_Type': data['Store_Type']
+        }
+
+        # Convert dictionary to DataFrame
+        input_df = pd.DataFrame([sample])
+
+        # Make prediction using the trained model
+        prediction = model.predict(input_df).tolist()[0]
+
+        # Return JSON response
+        return jsonify({'Predicted_Product_Store_Sales_Total': prediction})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+# ==============================
+# Batch prediction endpoint
+# ==============================
+@sales_api.post('/v1/salesbatch')
+def predict_sales_batch():
+    try:
+        # Get uploaded CSV file from the request
+        file = request.files['file']
+
+        # Read CSV file into DataFrame
+        input_data = pd.read_csv(file)
+
+        # Make predictions on the batch
+        predictions = model.predict(input_data).tolist()
+
+        # Add predictions as new column
+        input_data['Predicted_Product_Store_Sales_Total'] = predictions
+
+        # Convert DataFrame to list of dicts for JSON output
+        result = input_data.to_dict(orient='records')
+
+        return jsonify(result)
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+# ==============================
+# Run Flask app
+# ==============================
+if __name__ == '__main__':
+    sales_api.run(debug=True)

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.28.1
+uvicorn[standard]

sales_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4657187149681115b7fe62798bb261be5dcfaa2831d4028d46d7da0afc9da490
+size 647912