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Dockerfile

@@ -1,16 +1,24 @@
-# Use a minimal base image with Python 3.9 installed
 FROM python:3.9-slim
 
-# Set the working directory inside the container to /app
-WORKDIR /app
+# Set the working directory inside the container
+WORKDIR /app #Complete the code to mention the command in Docker to set the working directory
 
-# Copy all files from the current directory on the host to the container's /app directory
-COPY . .
+# Copy all files from the current directory to the container's working directory
+COPY . . #Complete the code to mention the command in Docker to copy the files from the current directory to the container's working directory
 
-# 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
+#   removed and replaced with below     RUN pip install --no-cache-dir --upgrade -r requirements.txt #Complete the code to mention the command in Docker to install dependencies
+RUN pip install --no-cache-dir --upgrade -r backend_files/requirements.txt
+
+
+# new scripts
+EXPOSE 7860
+
+# 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`)
+#  removed the following detail    CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:superkart_api"]
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "backend_files.app:superkart_api"]
 
-# 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"]
 
-# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -1,39 +1,55 @@
 
-import streamlit as st
-import requests
-
-st.title("Product Store Sales Prediction App") #Complete the code to define the title of the app.
-
-# Input fields for product and store data
-Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)
-Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
-Product_Allocated_Area = st.selectbox("Product Allocated Area", ["Small", "Medium", "Large"]) #Complete the code to define the UI element for Product_Allocated_Area
-Product_MRP = st.number_input("Product MRP", min_value=0.0, value=100.0) #Complete the code to define the UI element for Product_MRP
-Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])   #Complete the code to define the UI element for Store_Size
-Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"]) #Complete the code to define the UI element for Store_Location_City_Type
-Store_Type = st.selectbox("Store Type", ["Type 1", "Type 2", "Type 3", "Type 4"]) #Complete the code to define the UI element for Store_Type
-Product_Id_char = st.text_input("Product ID (char)", value="FDX") #Complete the code to define the UI element for Product_Id_char
-Store_Age_Years = st.number_input("Store Age (Years)", min_value=0, value=5) #Complete the code to define the UI element for Store_Age_Years
-Product_Type_Category = st.selectbox("Product Type Category", ["Food", "Non-Food", "Drinks"]) #Complete the code to define the UI element for Product_Type_Category
-
-product_data = {
-    "Product_Weight": Product_Weight,
-    "Product_Sugar_Content": Product_Sugar_Content,
-    "Product_Allocated_Area": Product_Allocated_Area,
-    "Product_MRP": Product_MRP,
-    "Store_Size": Store_Size,
-    "Store_Location_City_Type": Store_Location_City_Type,
-    "Store_Type": Store_Type,
-    "Product_Id_char": Product_Id_char,
-    "Store_Age_Years": Store_Age_Years,
-    "Product_Type_Category": Product_Type_Category
-}
-
-if st.button("Predict", type='primary'):
-    response = requests.post("https://Hunagypsy/my-docker-space.hf.space/v1/predict", json=product_data)    # Complete the code to enter user name and space name to correctly define the endpoint
-    if response.status_code == 200:
-        result = response.json()
-        predicted_sales = result["Sales"]
-        st.write(f"Predicted Product Store Sales Total: ₹{predicted_sales:.2f}")
-    else:
-        st.error("Error in API request")
+# 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 Flask app with a name
+superkart_api = Flask("superkart_api") #Complete the code to define the name of the app
+
+# Load the trained churn prediction model
+model = joblib.load("backend_files/tuned_gradient_boosting_regressor_model.joblib") #Complete the code to define the location of the serialized model
+
+# Define a route for the home page
+@superkart_api.get('/')
+def home():
+    return "Welcome to the Superkart Sales Prediction API!" #Complete the code to define a welcome message
+
+# Define an endpoint to predict churn for a single customer
+@superkart_api.post('/v1/predict')
+def predict_sales():
+    # Get JSON data from the request
+    data = request.get_json()
+
+    # Extract relevant customer features from the input data. The order of the column names matters.
+    sample = {
+        'Product_Weight': data['Product_Weight'],
+        'Product_Sugar_Content': data['Product_Sugar_Content'],
+        'Product_Allocated_Area': data['Product_Allocated_Area'],
+        'Product_MRP': data['Product_MRP'],
+        'Store_Size': data['Store_Size'],
+        'Store_Location_City_Type': data['Store_Location_City_Type'],
+        'Store_Type': data['Store_Type'],
+        'Product_Id_char': data['Product_Id_char'],
+        'Store_Age_Years': data['Store_Age_Years'],
+        'Product_Type_Category': data['Product_Type_Category']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make a churn prediction using the trained model
+    prediction = model.predict(input_data).tolist()[0]
+
+    # Return the prediction as a JSON response
+    return jsonify({'Sales': prediction})
+
+
+# Run the Flask app in debug mode
+#  changed following      if __name__ == '__main__':
+#  changed following        superkart_api.run(debug=True)
+if __name__ == '__main__':
+    import os
+    port = int(os.environ.get("PORT", 7860))
+    superkart_api.run(host="0.0.0.0", port=port)

requirements.txt

@@ -1,2 +1,12 @@
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+seaborn==0.13.2
+joblib==1.4.2
+xgboost==2.1.4
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
 requests==2.32.3
-streamlit==1.45.0
+uvicorn[standard]
+streamlit==1.45