Upload Streamlit frontend files

Dockerfile

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+# 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
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -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"]
+
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

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+
+import streamlit as st
+import requests
+import json
+import pandas as pd
+
+# --- Page Configuration ---
+st.set_page_config(page_title='SuperKart Sales Revenue Forecaster', layout='wide')
+st.title('SuperKart Sales Revenue Forecaster')
+
+# --- Backend API URL ---
+# Make sure to update this with your deployed Flask API URL
+# During local development, it might be 'http://localhost:5000/forecast_revenue'
+# For Hugging Face Space, it will be the URL of your deployed backend space, e.g., 'https://<your-space-id>.hf.space/forecast_revenue'
+BACKEND_URL = 'https://sagarathf-superkart.hf.space/v1/forecastrevenue' # API URL for POST
+
+st.markdown("""
+    This application predicts the total sales revenue for a product in a given store.
+    Please fill in the details below to get a sales forecast.
+""")
+
+# --- Input Widgets for Features ---
+
+st.subheader('Product Details')
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    product_weight = st.number_input('Product Weight (kg)', min_value=0.1, max_value=50.0, value=10.0, step=0.1)
+    product_sugar_content = st.selectbox(
+        'Product Sugar Content',
+        ['Low Sugar', 'Regular', 'No Sugar', 'Others']
+    )
+
+with col2:
+    product_allocated_area = st.number_input('Product Allocated Area Ratio', min_value=0.001, max_value=0.5, value=0.05, step=0.001, format="%.3f")
+    product_type = st.selectbox(
+        'Product Type',
+        ['Dairy', 'Soft Drinks', 'Meat', 'Fruits and Vegetables', 'Household', 
+         'Baking Goods', 'Snack Foods', 'Frozen Foods', 'Breakfast', 
+         'Health and Hygiene', 'Hard Drinks', 'Canned', 'Breads', 
+         'Starchy Foods', 'Others', 'Seafood']
+    )
+
+with col3:
+    product_mrp = st.number_input('Product MRP (Max. Retail Price)', min_value=10.0, max_value=500.0, value=150.0, step=1.0)
+
+st.subheader('Store Details')
+col4, col5, col6 = st.columns(3)
+
+with col4:
+    store_id = st.selectbox(
+        'Store ID',
+        ['OUT003', 'OUT002', 'OUT001', 'OUT004']
+    )
+    store_establishment_year = st.number_input('Store Establishment Year', min_value=1950, max_value=2024, value=2000, step=1)
+
+with col5:
+    store_size = st.selectbox(
+        'Store Size',
+        ['Medium', 'High', 'Small']
+    )
+    store_location_city_type = st.selectbox(
+        'Store Location City Type',
+        ['Tier 1', 'Tier 2', 'Tier 3']
+    )
+
+with col6:
+    store_type = st.selectbox(
+        'Store Type',
+        ['Departmental Store', 'Supermarket Type1', 'Food Mart', 'Supermarket Type2']
+    )
+
+
+# --- Prediction Button and Logic ---
+if st.button('Predict Sales Revenue'):
+    # Collect input data into a dictionary
+    input_data = {
+        "Product_Weight": product_weight,
+        "Product_Sugar_Content": product_sugar_content,
+        "Product_Allocated_Area": product_allocated_area,
+        "Product_Type": product_type,
+        "Product_MRP": product_mrp,
+        "Store_Id": store_id,
+        "Store_Establishment_Year": store_establishment_year,
+        "Store_Size": store_size,
+        "Store_Location_City_Type": store_location_city_type,
+        "Store_Type": store_type
+    }
+
+    # Display collected data (for debugging purposes)
+    st.json(input_data)
+
+    try:
+        # Send POST request to the backend API
+        response = requests.post(BACKEND_URL, json=input_data)
+
+        # Check if the request was successful
+        if response.status_code == 200:
+            prediction_result = response.json()
+            predicted_sales = prediction_result.get('predicted_sales')
+            if predicted_sales is not None:
+                st.success(f"Predicted Sales Revenue: &#8377;{predicted_sales:,.2f}")
+            else:
+                st.error("Prediction result not found in the API response.")
+        else:
+            st.error(f"Error from backend API: {response.status_code} - {response.text}")
+
+    except requests.exceptions.ConnectionError:
+        st.error("Could not connect to the backend API. Please ensure the API is running and the URL is correct.")
+    except Exception as e:
+        st.error(f"An unexpected error occurred: {e}")

requirements.txt

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+streamlit==1.43.2
+requests==2.32.3
+pandas==2.2.2