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Dockerfile

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+# Use Python 3.9 as base image
+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and install
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the Streamlit app
+COPY app.py .
+
+# Expose port 8501 (Streamlit's default)
+EXPOSE 8501
+
+# Run Streamlit
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

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+import streamlit as st
+import requests
+import json
+
+# Page configuration
+st.set_page_config(
+    page_title="SuperKart Sales Predictor",
+    page_icon="🛒",
+    layout="centered"
+)
+
+# Debug: Print to logs
+print("Streamlit app starting...")
+
+# Title and description
+st.title("🛒 SuperKart Sales Predictor")
+st.markdown("Predict product sales using your tuned Random Forest model. Enter details below!")
+
+# Input fields matching SuperKart dataset
+col1, col2 = st.columns(2)
+
+with col1:
+    st.subheader("Product Information")
+    product_weight = st.number_input("Product Weight", min_value=0.0, max_value=50.0, value=12.0, step=0.1)
+    product_mrp = st.number_input("Product MRP ($)", min_value=0.0, max_value=10000.0, value=150.0, step=0.01)
+    product_sugar = st.selectbox("Product Sugar Content", ['Low Fat', 'Regular', 'Low Sugar', 'LF'])
+    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'])
+
+with col2:
+    st.subheader("Store Information")
+    store_size = st.selectbox("Store Size", ['Small', 'Medium', 'High'])
+    store_location = st.selectbox("Store Location Type", ['Tier 1', 'Tier 2', 'Tier 3'])
+    store_type = st.selectbox("Store Type", 
+                             ['Grocery Store', 'Supermarket Type1', 'Supermarket Type2', 'Supermarket Type3'])
+
+# Prediction button
+if st.button("Predict Sales"):
+    # Prepare data for your backend API
+    data = {
+        "Product_Weight": product_weight,
+        "Product_MRP": product_mrp,
+        "Product_Sugar_Content": product_sugar,
+        "Product_Type": product_type,
+        "Store_Size": store_size,
+        "Store_Location_City_Type": store_location,
+        "Store_Type": store_type
+    }
+
+    # Debug: Print data being sent
+    print(f"Sending data: {data}")
+
+    # Call your deployed backend API
+    # REPLACE YOUR_USERNAME with your actual Hugging Face username
+    api_url = "https://toddmattingly-superkart-backend.hf.space/predict"
+    
+    try:
+        response = requests.post(api_url, json=data, timeout=10)
+        print(f"API response status: {response.status_code}")
+        
+        if response.status_code == 200:
+            # API returns a list directly (based on your testing)
+            predictions = response.json()
+            prediction = predictions[0] if isinstance(predictions, list) and len(predictions) > 0 else 0
+            
+            st.success(f"🎯 Predicted Sales Total: ${prediction:,.2f}")
+            st.info(f"📊 Based on: {product_type} at ${product_mrp:,.2f} MRP in a {store_type}")
+        else:
+            st.error(f"API Error: {response.status_code} - {response.text}")
+            print(f"API Error: {response.status_code} - {response.text}")
+            
+    except requests.exceptions.RequestException as e:
+        st.error(f"Connection Error: {str(e)}")
+        print(f"Connection Error: {str(e)}")
+    except Exception as e:
+        st.error(f"Unexpected Error: {str(e)}")
+        print(f"Unexpected Error: {str(e)}")
+
+# Footer
+st.markdown("---")
+st.markdown("*Powered by Streamlit & Hugging Face Spaces*")
+st.markdown("*Using your tuned Random Forest model*")
+
+print("Streamlit app loaded successfully.")

requirements.txt

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+streamlit
+requests