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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:rental_price_predictor_api"]

app.py

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+# Import necessary libraries
+import joblib  # For loading the serialized model
+import pandas as pd  # For data manipulation
+from flask import Flask, request, jsonify  # For creating the Flask API
+
+# Initialize the Flask application
+superkart_sales_api = Flask("SuperKart Sales Forecast API")
+
+# Load the trained machine learning pipeline (preprocessor + model)
+# Make sure this file is present next to app.py in your backend folder
+model = joblib.load("sales_forecast_model_v1_0.joblib")
+
+# Expected feature names (order doesn't matter for DataFrame, kept for clarity)
+EXPECTED_FEATURES = [
+    "Product_Weight",
+    "Product_Allocated_Area",
+    "Product_MRP",
+    "Store_Age",
+    "Product_Sugar_Content",
+    "Product_Type",
+    "Store_Size",
+    "Store_Location_City_Type",
+    "Store_Type",
+]
+
+# Define a route for the home page (GET request)
+@superkart_sales_api.get("/")
+def home():
+    """
+    Handles GET requests to the root URL ('/').
+    Returns a simple welcome message and the expected schema.
+    """
+    return "Welcome to the SuperKart Sales Forecast API!"
+    
+  #  jsonify({
+   #     "message": ,
+    #    "expected_payload": {
+     #       "Product_Weight": "float",
+      #      "Product_Allocated_Area": "float (0-1)",
+       #     "Product_MRP": "float",
+        #    "Store_Age": "int",
+         #   "Product_Sugar_Content": "str (e.g., 'Regular', 'Low Sugar', 'No Sugar')",
+          #  "Product_Type": "str (e.g., 'Snack Foods', 'Dairy', ...)",
+           # "Store_Size": "str (e.g., 'Small', 'Medium', 'High')",
+            #"Store_Location_City_Type": "str (e.g., 'Tier 1', 'Tier 2', 'Tier 3')",
+            #"Store_Type": "str (e.g., 'Supermarket Type 2', 'Departmental Store', ...)"
+        #}
+   #})
+
+# Define an endpoint for single sales prediction (POST request)
+@superkart_sales_api.post("/v1/sales")
+def predict_sales():
+    """
+    Handles POST requests to the '/v1/sales' endpoint.
+    Expects a JSON payload with SuperKart product & store features and
+    returns the predicted Product_Store_Sales_Total as JSON.
+
+    Example payload:
+    {
+      "Product_Weight": 12.5,
+      "Product_Allocated_Area": 0.06,
+      "Product_MRP": 150,
+      "Store_Age": 16,
+      "Product_Sugar_Content": "Regular",
+      "Product_Type": "Snack Foods",
+      "Store_Size": "Medium",
+      "Store_Location_City_Type": "Tier 2",
+      "Store_Type": "Supermarket Type 2"
+    }
+    """
+    try:
+        payload = request.get_json()
+
+        # Basic validation: ensure all required features are present
+        missing = [f for f in EXPECTED_FEATURES if f not in payload]
+        if missing:
+            return jsonify({
+                "error": "Missing required feature(s).",
+                "missing": missing,
+                "expected_features": EXPECTED_FEATURES
+            }), 400
+
+        # Build a single-row DataFrame in the expected feature order
+        sample = {f: payload[f] for f in EXPECTED_FEATURES}
+        input_df = pd.DataFrame([sample])
+
+        # Predict sales (model outputs actual sales; no log transform)
+        pred = model.predict(input_df)[0]
+        pred = round(float(pred), 2)  # ensure JSON-serializable and nicely rounded
+
+        return jsonify({"Predicted Product_Store_Sales_Total": pred})
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == "__main__":
+    superkart_sales_api.run(debug=True)

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.28.1
+uvicorn[standard]
+streamlit==1.43.2

sales_forecast_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0dc4282180e8e3994e569450d1c09b35ce2825e6116917d752c350f91295fd0a
+size 63809427