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app.py

@@ -40,26 +40,26 @@ input_data = pd.DataFrame([{
     'Store_Establishment_Year': store_establishment_year
 }])
 
-# Make prediction when the "Predict" button is clicked
-# if st.button("Predict"):
-#     response = requests.post("https://Swetha2031-SuperKartSalesPredictionBackend.hf.space/v1/salestotal", json=input_data.to_dict(orient='records')[0])
-#     if response.status_code == 200:
-#         prediction = response.json()['Predicted Sales (₹)']
-#         st.success(f"Predicted Product Sales: ₹{prediction}")
-#     else:
-#         st.error("Error making prediction.")
+Make prediction when the "Predict" button is clicked
 if st.button("Predict"):
     response = requests.post("https://Swetha2031-SuperKartSalesPredictionBackend.hf.space/v1/salestotal", json=input_data.to_dict(orient='records')[0])
     if response.status_code == 200:
-        try:
-            result = response.json()
-            st.write("Raw response:", result)  # Optional: for debugging
-            prediction = result.get('Predicted Sales (₹)', 'No prediction returned')
-            st.success(f"Predicted Product Sales: ₹{prediction}")
-        except Exception as e:
-            st.error(f"Error parsing prediction: {e}")
+        prediction = response.json()['Predicted Sales (₹)']
+        st.success(f"Predicted Product Sales: ₹{prediction}")
     else:
         st.error("Error making prediction.")
+# if st.button("Predict"):
+#     response = requests.post("https://Swetha2031-SuperKartSalesPredictionBackend.hf.space/v1/salestotal", json=input_data.to_dict(orient='records')[0])
+#     if response.status_code == 200:
+#         try:
+#             result = response.json()
+#             st.write("Raw response:", result)  # Optional: for debugging
+#             prediction = result.get('Predicted Sales (₹)', 'No prediction returned')
+#             st.success(f"Predicted Product Sales: ₹{prediction}")
+#         except Exception as e:
+#             st.error(f"Error parsing prediction: {e}")
+#     else:
+#         st.error("Error making prediction.")
 
 
 # Section for batch prediction
@@ -78,3 +78,26 @@ if uploaded_file is not None:
             st.write(predictions)
         else:
             st.error("Error making batch prediction.")
+
+# if uploaded_file is not None:
+#     if st.button("Predict Batch"):
+#         response = requests.post("https://Swetha2031-SuperKartSalesPredictionBackend.hf.space/v1/salesbatch",files={"file": uploaded_file})
+
+#         if response.status_code == 200:
+#             predictions = response.json()
+
+#             # Convert to DataFrame for inspection
+#             df_predictions = pd.DataFrame(predictions)
+
+#             # Check for overflow or invalid values
+#             if df_predictions.isin([float('inf'), float('nan')]).any().any():
+#                 st.warning("Some predictions returned 'inf' or 'NaN' due to overflow. Please check input values.")
+#             elif (df_predictions.select_dtypes(include='number') > 1e10).any().any():
+#                 st.warning("Some predictions are extremely large. This may indicate overflow or input scaling issues.")
+
+#             st.success("Batch predictions completed!")
+#             st.write(df_predictions)
+
+#         else:
+#             st.error("Error making batch prediction.")
+