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Dockerfile

@@ -6,9 +6,11 @@ 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 
-RUN pip install --no-cache-dir -r requirements.txt
+# 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
-
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:app"]
+# - `-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

@@ -2,70 +2,66 @@ import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
 
-# Initialize Flask app with a name
-app = Flask("Telecom Customer Churn Predictor")
+# Initialize Flask app
+super_kart_api = Flask("Super Kart Product Sales Predictor")
 
-# Load the trained churn prediction model
-model = joblib.load("churn_prediction_model_v1_0.joblib")
+# Load the trained model
+model = joblib.load("super_kart_model_v1_0.joblib")
 
 # Define a route for the home page
-@app.get('/')
+@super_kart_api.get('/')
 def home():
-    return "Welcome to the Telecom Customer Churn Prediction API"
+    return "Welcome to the Super Kart Product Sales Predictor API!"
 
-# Define an endpoint to predict churn for a single customer
-@app.post('/v1/customer')
-def predict_churn():
+# Define an endpoint to predict price for a single product
+@super_kart_api.post('/v1/product')
+def predict_product_sales_price():
     # Get JSON data from the request
-    customer_data = request.get_json()
+    product_data = request.get_json()
 
-    # Extract relevant customer features from the input data
+    # Extract relevant house features from the input data
     sample = {
-        'SeniorCitizen': customer_data['SeniorCitizen'],
-        'Partner': customer_data['Partner'],
-        'Dependents': customer_data['Dependents'],
-        'tenure': customer_data['tenure'],
-        'PhoneService': customer_data['PhoneService'],
-        'InternetService': customer_data['InternetService'],
-        'Contract': customer_data['Contract'],
-        'PaymentMethod': customer_data['PaymentMethod'],
-        'MonthlyCharges': customer_data['MonthlyCharges'],
-        'TotalCharges': customer_data['TotalCharges']
+        'Product_Weight': product_data['Product_Weight'],
+        'Product_Sugar_Content': product_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
+        'Product_Type': product_data['Product_Type'],
+        'Product_MRP': product_data['Product_MRP'],
+        'Store_Id': product_data['Store_Id'],
+        'Store_Establishment_Year': product_data['Store_Establishment_Year'],
+        'Store_Size': product_data['Store_Size'],
+        'Store_Location_City_Type': product_data['Store_Location_City_Type'],
+        'Store_Type': product_data['Store_Type']
     }
 
     # Convert the extracted data into a DataFrame
     input_data = pd.DataFrame([sample])
 
-    # Make a churn prediction using the trained model
+    # Make a prediction using the trained model
     prediction = model.predict(input_data).tolist()[0]
 
-    # Map prediction result to a human-readable label
-    prediction_label = "churn" if prediction == 1 else "not churn"
-
     # Return the prediction as a JSON response
-    return jsonify({'Prediction': prediction_label})
+    return jsonify({'Predicted_Product_Sales': prediction})
 
-# Define an endpoint to predict churn for a batch of customers
-@app.post('/v1/customerbatch')
-def predict_churn_batch():
+# Define an endpoint to predict product sales price for a batch of product
+@super_kart_api.post('/v1/productbatch')
+def predict_house_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)
 
-    # Make predictions for the batch data and convert raw predictions into a readable format
-    predictions = [
-        'Churn' if x == 1
-        else "Not Churn"
-        for x in model.predict(input_data.drop("customerID",axis=1)).tolist()
-    ]
+    # Make predictions for the batch data
+    predictions = model.predict(input_data).tolist()
+
+    # Add predictions to the DataFrame
+    input_data['Predicted_Product_Sales'] = predictions
 
-    cust_id_list = input_data.customerID.values.tolist()
-    output_dict = dict(zip(cust_id_list, predictions))
+    # Convert results to dictionary
+    result = input_data.to_dict(orient="records")
 
-    return output_dict
+    return jsonify(result)
 
 # Run the Flask app in debug mode
 if __name__ == '__main__':
-    app.run(debug=True)
+    house_price_api.run(debug=True)