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My updates for link front end to back end

streamlit_app.py

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+# streamlit_app.py
+
+import streamlit as st
+import pandas as pd
+import requests
+
+# ----------------- CONFIGURATION -----------------
+# CRITICAL: REPLACE THIS PLACEHOLDER URL with your actual deployed Docker Space URL
+API_URL = "https://aibtus.ut.aiml.hf.space/predict" 
+
+# Define static categorical options (MUST match preprocessing categories)
+CATEGORIES = {
+    'Product_Sugar_Content': ['no sugar', 'low sugar', 'regular'],
+    'Store_Size': ['Low', 'Medium', 'High'],
+    'Store_Location_City_Type': ['Tier 1', 'Tier 2', 'Tier 3'],
+    'Store_Type': ['Supermarket Type 1', 'Supermarket Type 2', 'Supermarket Type 3', 'Food Mart'],
+    'Product_Type': ['Snack Foods', 'Dairy', 'Baking Goods', 'Health and Hygiene', 'Frozen Foods', 'Others', 
+                     'Soft Drinks', 'Household', 'Meat', 'Fruits and Vegetables', 'Breads', 'Breakfast', 
+                     'Hard Drinks', 'Starchy Foods', 'Seafood', 'Canned'],
+    'Product_Category_Simplified': ['FD', 'DR', 'NC']
+}
+
+FEATURE_COLS = [
+    'Product_Weight', 'Product_Sugar_Content', 'Product_Allocated_Area',
+    'Product_Type', 'Product_MRP', 'Store_Size',
+    'Store_Location_City_Type', 'Store_Type', 'Store_Age',
+    'Product_Category_Simplified'
+]
+
+# ----------------- APP INTERFACE -----------------
+
+st.set_page_config(page_title="SuperKart Sales Forecaster", layout="wide")
+st.title(" SuperKart Sales Forecasting Tool")
+
+with st.form("sales_prediction_form"):
+    st.header("Product Attributes")
+    
+    col1, col2, col3 = st.columns(3)
+    
+    product_weight = col1.number_input("Product Weight (kg)", min_value=1.0, max_value=20.0, value=10.0, step=0.1)
+    product_mrp = col2.number_input("Product MRP (₹)", min_value=10.0, max_value=300.0, value=150.0, step=1.0)
+    product_allocated_area = col3.number_input("Product Allocated Area", min_value=0.0, max_value=1.0, value=0.07, step=0.01)
+
+    product_type = col1.selectbox("Product Type", options=CATEGORIES['Product_Type'])
+    product_sugar_content = col2.selectbox("Product Sugar Content", options=CATEGORIES['Product_Sugar_Content'])
+    product_category_simplified = col3.selectbox("Product Category (FD/DR/NC)", options=CATEGORIES['Product_Category_Simplified'])
+    
+    st.header("Store Attributes")
+    
+    col4, col5, col6 = st.columns(3)
+    
+    store_type = col4.selectbox("Store Type", options=CATEGORIES['Store_Type'])
+    store_location_city_type = col5.selectbox("Store Location City Type", options=CATEGORIES['Store_Location_City_Type'])
+    store_size = col6.selectbox("Store Size", options=CATEGORIES['Store_Size'])
+    
+    store_age = st.slider("Store Age (Years)", min_value=5, max_value=35, value=10)
+
+
+    submitted = st.form_submit_button("Forecast Sales")
+
+if submitted:
+    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_Size': store_size,
+        'Store_Location_City_Type': store_location_city_type,
+        'Store_Type': store_type,
+        'Store_Age': float(store_age), 
+        'Product_Category_Simplified': product_category_simplified
+    }
+    
+    payload = [input_data]
+
+    try:
+        st.info(f"Sending request to API at: {API_URL}...")
+        response = requests.post(API_URL, json=payload, timeout=15) 
+        
+        if response.status_code == 200:
+            result = response.json()
+            if result['status'] == 'success':
+                st.success(f" **Forecast Complete!**")
+                st.metric(label="Predicted Sales Revenue", value=f"₹{result['predicted_sales_revenue']:,.2f}")
+                st.balloons()
+            else:
+                st.error(f"Prediction API Error: {result.get('error', 'Unknown error')}")
+        else:
+            st.error(f"**API Connection Error** (Status {response.status_code}): Check API URL and Docker logs.")
+
+    except requests.exceptions.Timeout:
+        st.error("**Connection Failed:** Request timed out (15s). The Docker API might be slow or unreachable.")
+    except Exception as e:
+        st.error(f"An unexpected error occurred: {e}")

streamlit_requirements.txt

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+streamlit==1.36.0
+pandas==2.2.2
+requests==2.32.3