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app.py

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+
+import streamlit as st
+import pandas as pd
+import requests
+
+st.title('SuperKart Sales Forecast')
+
+# Input fields with refined ranges based on df.describe()
+product_weight = st.number_input('Product Weight', min_value=4.555, max_value=21.35, value=12.8289)
+sugar_content = st.selectbox('Sugar Content', ['Low Sugar', 'Regular', 'No Sugar', 'reg'])
+allocated_area = st.number_input('Allocated Area', min_value=0.013, max_value=0.295, value=0.1393)
+product_type = st.selectbox('Product Type', ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Snack Foods', 'Meat', 'Household', 'Hard Drinks', 'Fruits and Vegetables', 'Breads', 'Soft Drinks', 'Breakfast', 'Others', 'Starchy Foods', 'Seafood'])
+mrp = st.number_input('Product MRP', min_value=31.29, max_value=266.89, value=141.15)
+est_year = st.number_input('Store Establishment Year', min_value=1985, max_value=2015, value=1998.81)
+store_size = st.selectbox('Store Size', ['Medium', 'High', 'Small'])
+city_type = st.selectbox('City Type', ['Tier 1', 'Tier 2', 'Tier 3'])
+store_type = st.selectbox('Store Type', ['Supermarket Type1', 'Supermarket Type2', 'Departmental Store', 'Food Mart'])
+
+if st.button('Predict'):
+    data = {
+        'Product_Weight': product_weight,
+        'Product_Sugar_Content': sugar_content,
+        'Product_Allocated_Area': allocated_area,
+        'Product_Type': product_type,
+        'Product_MRP': mrp,
+        'Store_Establishment_Year': est_year,
+        'Store_Size': store_size,
+        'Store_Location_City_Type': city_type,
+        'Store_Type': store_type,
+        'Store_Age': 2025 - est_year
+    }
+    try:
+        response = requests.post('https://saibsund-superkart-flask-api.hf.space/predict', json=data)
+        response.raise_for_status()
+        prediction = response.json()['prediction']
+        st.write(f'Predicted Sales: ${prediction:.2f}')
+    except requests.exceptions.RequestException as e:
+        st.write(f"Error: {e}")