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app.py

@@ -9,11 +9,11 @@ st.title("Superkart Price Prediction")
 st.subheader("Online Prediction")
 
 # Collect user input for property features
-product_weight = st.number_input("Product Weight", min_value=0.0, max_value=100.0, step=1.0, value=90.0)
+product_weight = st.number_input("Product Weight", min_value=0.0)
 product_sugar_content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
-product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, max_value=100.0, step=1.0, value=90.0)
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0)
 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'])
-product_mrp = st.number_input("Product MRP", min_value=0.0, max_value=100.0, step=1.0, value=90.0)
+product_mrp = st.number_input("Product MRP", min_value=0.0)
 store_size = st.selectbox("Store Size", ['Medium', 'High', 'Small'])
 store_location_city_type = st.selectbox("Store Location City Type", ['Tier 2', 'Tier 1', 'Tier 3'])
 age_category = st.selectbox("Age_Category", ['0to20', '21to30', '31to50'])
@@ -34,7 +34,7 @@ input_data = pd.DataFrame([{
 
 # Make prediction when the "Predict" button is clicked
 if st.button("Predict"):
-    response = requests.post("https://RedRooster99-projectfrontend.hf.space/v1/superkart", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    response = requests.post("https://RedRooster99-projectbackend.hf.space/v1/superkart", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
     if response.status_code == 200:
         prediction = response.json()['Predicted Price']
         st.success(f"Superkart Price: {prediction}")