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Dockerfile

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+
+# 1. Base Image: Use a lightweight Python 3.9 image
+FROM python:3.9-slim-buster
+
+# 2. Set the working directory inside the container
+WORKDIR /app
+
+# 3. Copy and install requirements first (for better caching)
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 4. Copy the rest of the application files
+# This copies app.py, model.joblib, and preprocessor.joblib
+COPY . .
+
+# 5. Expose the port the app runs on
+EXPOSE 5000
+
+# 6. Define the command to run the application
+# We use gunicorn to serve the Flask app (app:superkart_api)
+CMD ["gunicorn", "--bind", "0.0.0.0:5000", "app:superkart_api"]
+
+print("✅ Dockerfile created in /content/drive/MyDrive/SuperKart/backend_files/")

app.py

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+
+# Import necessary libraries
+import numpy as np
+import joblib  # For loading the serialized artifacts
+import pandas as pd  # For data manipulation
+from flask import Flask, request, jsonify  # For creating the Flask API
+import os
+
+# Define the directory where the app and models are
+# This is good practice for finding the files
+APP_DIR = os.path.dirname(os.path.abspath(__file__))
+
+# Initialize Flask app
+superkart_api = Flask("superkart_predictor")
+
+# --- Load the preprocessor AND the model ---
+try:
+    preprocessor_path = os.path.join(APP_DIR, "preprocessor.joblib")
+    model_path = os.path.join(APP_DIR, "model.joblib")
+    
+    preprocessor = joblib.load(preprocessor_path)
+    model = joblib.load(model_path)
+    print("✅ Preprocessor and model loaded successfully.")
+except Exception as e:
+    print(f"❌ Error loading artifacts: {e}")
+    preprocessor = None
+    model = None
+
+# Define a route for the home page
+@superkart_api.get('/')
+def home():
+    # A simple welcome message
+    return "Welcome to the SuperKart Sales Prediction API!"
+
+# Define an endpoint to predict sales
+@superkart_api.post('/v1/predict')
+def predict_sales():
+    
+    if preprocessor is None or model is None:
+        return jsonify({"error": "Model or preprocessor not loaded"}), 500
+
+    # Get JSON data from the request
+    data = request.get_json()
+
+    # --- Extract features from the input data ---
+    # The keys (e.g., 'Product_Weight') MUST match the JSON sent
+    # The columns MUST match what the preprocessor was trained on.
+    try:
+        sample = {
+            '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_Size': data['Store_Size'],
+            'Store_Location_City_Type': data['Store_Location_City_Type'],
+            'Store_Type': data['Store_Type'],
+            'Store_Age': data['Store_Age']
+        }
+    except KeyError as e:
+        return jsonify({"error": f"Missing key in JSON payload: {e}"}), 400
+
+    # Convert the extracted data into a 1-row DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # --- Make a prediction ---
+    try:
+        # 1. Transform the raw input data using the preprocessor
+        processed_data = preprocessor.transform(input_data)
+        
+        # 2. Make a prediction using the trained model
+        prediction = model.predict(processed_data).tolist()[0]
+
+        # Return the prediction as a JSON response
+        # We also round the prediction to 2 decimal places
+        return jsonify({'predicted_sales': round(prediction, 2)})
+    
+    except Exception as e:
+        return jsonify({"error": f"Error during prediction: {str(e)}"}), 500
+
+
+# Run the Flask app
+if __name__ == '__main__':
+    # Use 0.0.0.0 to make it accessible outside the container/notebook
+    superkart_api.run(host='0.0.0.0', port=5000, debug=True)

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:54aeda2ba7d11b49c070bf6e15f0a4dfdd1c4dace2a4a1a1c6805edab5a2bcf4
+size 472809

preprocessor.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6cc6ae7565037569fd6c94e73a4f64b865e0a29d078fb06f17603aed40112b2d
+size 3583

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+joblib==1.4.2
+flask==2.2.2
+gunicorn==20.1.0
+
+print("✅ requirements.txt file created in /content/drive/MyDrive/SuperKart/backend_files/")