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Dockerfile

@@ -1,14 +1,16 @@
-# Use a minimal base image with Python 3.9 installed
 FROM python:3.9-slim
 
-# Set     the working directory inside the container to /app
+# Set the working directory inside the container
 WORKDIR /app
 
-# Copy all files from the current directory on the host to the container's /app directory
+# Copy all files from the current directory to the container's working directory
 COPY . .
 
-# Install Python dependencies listed in requirements.txt
-RUN pip3 install -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 run the Streamlit app on port 8501 and make it accessible externally
-CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
+# 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

@@ -1,52 +1,64 @@
+# Import necessary libraries
+import numpy as np
+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
+rental_price_predictor_api = Flask("Super Kart Sales Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("super_kart_prediction_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@rental_price_predictor_api.get('/')
+def home():
+    """
+    This function handles GET requests to the root URL ('/') of the API.
+    It returns a simple welcome message.
+    """
+    return "Welcome to the Super Kart Sales Predictor API!"
+
+# Define an endpoint for single property prediction (POST request)
+@rental_price_predictor_api.post('/v1/superkart')
+def predict_rental_price():
+    """
+    This function handles POST requests to the '/v1/superkart' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted rental price as a JSON response.
+    """
+    # Get the JSON data from the request body
+    product_data = request.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+      '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 Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get log_price)
+    predicted_log_price = model.predict(input_data)[0]
+
+    # Calculate actual price
+    predicted_price = np.exp(predicted_log_price)
+
+    # Convert predicted_price to Python float
+    predicted_price = round(float(predicted_price), 2)
+    # The conversion above is needed as we convert the model prediction (log price) to actual price using np.exp, which returns predictions as NumPy float32 values.
+    # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error
+
+    # Return the actual price
+    return jsonify({'Predicted Sales Total': predicted_price})
+
 
-import streamlit as st
-import pandas as pd
-import joblib
-
-# Load the trained model
-def load_model():
-    return joblib.load("churn_prediction_model_v1_0.joblib")
-
-model = load_model()
-
-# Streamlit UI for Customer Churn Prediction
-st.title("Customer Churn Prediction App")
-st.write("This tool predicts customer churn risk based on their details. Enter the required information below.")
-
-# Collect user input based on dataset columns
-Partner = st.selectbox("Does the customer have a partner?", ["Yes", "No"])
-SeniorCitizen = st.selectbox("SeniorCitizen ?", ["Yes", "No"])
-Dependents = st.selectbox("Does the customer have dependents?", ["Yes", "No"])
-PhoneService = st.selectbox("Does the customer have phone service?", ["Yes", "No"])
-InternetService = st.selectbox("Type of Internet Service", ["DSL", "Fiber optic", "No"])
-Contract = st.selectbox("Type of Contract", ["Month-to-month", "One year", "Two year"])
-PaymentMethod = st.selectbox("Payment Method", ["Electronic check", "Mailed check", "Bank transfer", "Credit card"])
-tenure = st.number_input("Tenure (Months with the company)", min_value=0, value=12)
-MonthlyCharges = st.number_input("Monthly Charges", min_value=0.0, value=50.0)
-TotalCharges = st.number_input("Total Charges", min_value=0.0, value=600.0)
-
-
-# Convert categorical inputs to match model training
-input_data = pd.DataFrame([{
-    'Partner': Partner,
-    'Dependents': Dependents,
-    'PhoneService': PhoneService,
-    'InternetService': InternetService,
-    'Contract': Contract,
-    'PaymentMethod': PaymentMethod,
-    'tenure': tenure,
-    'MonthlyCharges': MonthlyCharges,
-    'TotalCharges': TotalCharges,
-    'SeniorCitizen': 1 if SeniorCitizen == "Yes" else 0
-}])
-
-# Set classification threshold
-classification_threshold = 0.5
-
-# Predict button
-if st.button("Predict"):
-    prediction_proba = model.predict_proba(input_data)[0, 1]
-    prediction = (prediction_proba >= classification_threshold).astype(int)
-    result = "churn" if prediction == 1 else "not churn"
-    st.write(f"Prediction: The customer is likely to **{result}**.")
-    st.write(f"Churn Probability: {prediction_proba:.2f}")

requirements.txt

@@ -3,4 +3,9 @@ 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]
 streamlit==1.43.2

super_kart_prediction_model_v1_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be7fa0ee5de13393a6fc55f1974f724c550a97d299c4a1cf0410a4d68474730e
+size 63812499