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Dockerfile

@@ -1,16 +1,15 @@
 FROM python:3.9-slim
 
-# Set the working directory inside the container
-WORKDIR /app #Complete the code to mention the command in Docker to set the working directory
+# Set the working directory inside the container to /app
+WORKDIR /app
 
-# Copy all files from the current directory to the container's working directory
-COPY . . 
+# Copy all files from the current directory on the host to the container's /app 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 #Complete the code to mention the command in Docker to install dependencies
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -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: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

@@ -1,51 +1,38 @@
 
-# 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("Predict") 
-
-# Load the trained sales prediction model
-model = joblib.load("superkart.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 SuperKart sales prediction!" #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 sales 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
-if __name__ == '__main__':
-    superkart_api.run(debug=True)
+import streamlit as st
+import requests
+
+st.title("Superkart Prediction")
+
+# 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", "reg"])
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, max_value=1.0, value=0.027)
+Product_MRP = st.number_input("Product MRP", min_value=0.0, value=117.08)
+Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1900, max_value=2026, value=2009)
+Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
+Product_Type = st.selectbox("Product Type", ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Snack Foods', 'Soft Drinks', 'Household', 'Fruits and Vegetables', 'Meat', 'Hard Drinks', 'Breads', 'Breakfast', 'Starchy Foods', 'Seafood', 'Others'])
+
+product_data = {
+    "Product_Weight": Product_Weight,
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Product_Allocated_Area": Product_Allocated_Area,
+    "Product_MRP": Product_MRP,
+    "Store_Establishment_Year": Store_Establishment_Year,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Store_Type": Store_Type,
+    "Product_Type": Product_Type
+}
+
+if st.button("Predict", type='primary'):
+    # Replace 'ansarkar' with your Hugging Face username and 'superkart' with your backend space name
+    response = requests.post("https://ansarkar-superkart.hf.space/v1/predict", json=product_data)
+    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(f"Error in API request: {response.status_code} - {response.text}")

requirements.txt

@@ -1,13 +1,2 @@
-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
-joblib==1.4.2
-Werkzeug==2.2.2
-flask==2.2.2
-gunicorn==20.1.0
 requests==2.32.3
-uvicorn[standard]
-streamlit==1.43.2
+streamlit==1.45.0