Update app.py

app.py

@@ -5,51 +5,40 @@ import requests
 # Set the title of the Streamlit app
 st.title("Sales Prediction")
 
-# Section for online prediction
-st.subheader("Online Prediction")
+# Collect user input
+product_weight = st.number_input("Weight of the product", min_value=0.0, value=2.0)
+product_allocated_area = st.number_input("Product Allocated Area", min_value=1.0, value=2.0)
+product_mrp = st.number_input("Product MRP", min_value=1.0, step=1.0, value=10.0)
+store_establishment_year = st.selectbox("Store Establishment Year", [2005, 2010, 2015, 2020])
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low", "Normal", "High"])
+product_type = st.selectbox("Product Type", ["Snack Foods", "Dairy", "Beverages"])
+store_id = st.text_input("Store ID", value="STR001")
+store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+store_location_city_type = st.selectbox("Store Location City Type", ["Urban", "Semi-Urban", "Rural"])
+store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Grocery Store"])
 
-sample = {
-        'Product_Weight': property_data['Product_Weight'],
-        'Product_Allocated_Area': property_data['Product_Allocated_Area'],
-        'Product_MRP': property_data['Product_MRP'],
-        'Store_Establishment_Year': property_data['Store_Establishment_Year'],
-        'Product_Sugar_Content': property_data['Product_Sugar_Content'],
-        'Product_Type': property_data['Product_Type'],
-        'Store_Id': property_data['Store_Id'],
-        'Store_Size': property_data['Store_Size'],
-        'Store_Location_City_Type': property_data['Store_Location_City_Type'],
-        'Store_Type': property_data['Store_Type']
-    }
-
-# Collect user input for property features
-product_weight = st.number_input("Weight of the product", min_value=0, value=2)
-Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=1, value=2)
-Product_MRP = st.number_input("Product_MRP", min_value=1, step=1, value=2)
-Store_Establishment_Year = st.selectbox("Store_Establishment_Year", ["strict", "flexible", "moderate"])
-Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["True", "False"])
-Product_Type = st.selectbox("Product_Type", ["False", "True"])
-Store_Size = st.number_input("Store_Size", min_value=0.0, max_value=100.0, step=1.0, value=90.0)
-Store_Location_City_Type = st.number_input("Store_Location_City_Type", min_value=0, step=1, value=1)
-Store_Type = st.number_input("Store_Type", min_value=0, step=1, value=1)
-
-# Convert user input into a DataFrame
-input_data = pd.DataFrame([{
+# Create input dictionary
+input_data = {
     'Product_Weight': product_weight,
-    'Product_Allocated_Area': Product_Allocated_Area,
-    'Product_MRP': Product_MRP,
-    'Store_Establishment_Year': Store_Establishment_Year,
-    'Product_Sugar_Content': Product_Sugar_Content,
-    'Product_Type': Product_Type,  # Convert to 't' or 'f'
-    'Store_Size': Store_Size,
-    'Store_Location_City_Type': Store_Location_City_Type,
-    'Store_Type': Store_Type
-}])
+    'Product_Allocated_Area': product_allocated_area,
+    'Product_MRP': product_mrp,
+    'Store_Establishment_Year': store_establishment_year,
+    'Product_Sugar_Content': product_sugar_content,
+    'Product_Type': product_type,
+    'Store_Id': store_id,
+    'Store_Size': store_size,
+    'Store_Location_City_Type': store_location_city_type,
+    'Store_Type': store_type
+}
 
 # Make prediction when the "Predict" button is clicked
 if st.button("Predict"):
-    response = requests.post("https://maddykan101-SalesPredictionBackend.hf.space/v1/rental", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    response = requests.post(
+        "https://maddykan101-SalesPredictionBackend.hf.space/v1/prediction",
+        json=input_data
+    )
     if response.status_code == 200:
-        prediction = response.json()['Predicted Sakes']
+        prediction = response.json()['Predicted Sales ']  # Adjust key if needed
         st.success(f"Predicted Sales: {prediction}")
     else:
-        st.error("Error making prediction.")
+        st.error(f"Prediction failed: {response.status_code}")