Upload folder using huggingface_hub

app.py

@@ -37,37 +37,9 @@ input_data = {
     'store_type': store_type
 }
 
-# Convert user input into a DataFrame
-input_data_1 = pd.DataFrame([{
-    '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_Id': store_id,
-    'Store_Establishment_Year': store_establishment_year,
-    'Store_Size': store_size,
-    'Store_Location_City_Type': store_location_city_type,
-    'Store_Type': store_type
-}])
-
-input_data_2 = {
-"Product_Weight":5,
-"Product_Sugar_Content":"Low Sugar",
-"Product_Allocated_Area":0.01,
-"Product_Type":"Frozen Foods",
-"Product_MRP":50,
-"Store_Id":"OUT001",
-"Store_Establishment_Year":"1987",
-"Store_Size":"Small",
-"Store_Location_City_Type":"Tier 1",
-"Store_Type":"Supermarket Type1",
-}
-input_data_1 = input_data_1.to_dict(orient='records')[0]
-st.write("input_data_2:", input_data_2)
 # Make prediction when the "Predict" button is clicked
 if st.button("Predict"):
-    response = requests.post("https://karora1804-StoreTotalSalesPredictionBackend.hf.space/v1/storeSales", json=input_data_2)  # Send data to Flask API
+    response = requests.post("https://karora1804-StoreTotalSalesPredictionBackend.hf.space/v1/storeSales", json=input_data)  # Send data to Flask API
     if response.status_code == 200:
         prediction = response.json()['Predicted Total Sales:']
         st.success(f"Predicted Store Total Sales: {prediction}")