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app.py

@@ -52,18 +52,35 @@ input_data = {
 # Make prediction when the "Predict" button is clicked
 if st.button("Predict Sales"):
     try:
-        response = requests.post("https://D009-SuperKartBackend.hf.space/v1/sales", json=input_data)
+        # Updated API endpoint
+        backend_url = "https://d009-superkartbackend.hf.space"  # Corrected URL
+        endpoint = f"{backend_url}/v1/sales"
+        
+        st.info(f"Connecting to: {endpoint}")  # Show the endpoint being called
+        
+        response = requests.post(endpoint, json=input_data, timeout=10)
+        
         if response.status_code == 200:
-            result = response.json()
-            st.success(f"Predicted Sales Total: ${result['predicted_sales']}")
-            
-            # Display features used
-            st.markdown("**Features Used**")
-            st.write(", ".join(result['features_used']))
+            try:
+                result = response.json()
+                st.success(f"Predicted Sales Total: ${result['predicted_sales']:.2f}")
+                
+                # Display features used
+                st.markdown("**Features Used**")
+                st.write(", ".join(result['features_used']))
+            except ValueError:
+                st.error("Could not decode JSON response from server")
+                st.text(f"Raw response: {response.text}")
         else:
-            st.error(f"Error making prediction: {response.json().get('error', 'Unknown error')}")
-    except Exception as e:
+            st.error(f"Error making prediction (Status {response.status_code})")
+            try:
+                error_details = response.json()
+                st.json(error_details)
+            except ValueError:
+                st.text(f"Raw response: {response.text}")
+    except requests.exceptions.RequestException as e:
         st.error(f"Connection error: {str(e)}")
+        st.info("Please check if the backend server is running and accessible")
 
 # Section for batch prediction
 st.subheader("Batch Prediction")
@@ -84,28 +101,46 @@ uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
 if uploaded_file is not None:
     if st.button("Predict Batch Sales"):
         try:
-            files = {'file': uploaded_file}
-            response = requests.post("https://D009-SuperKartBackend.hf.space/v1/salesbatch", files=files)
+            backend_url = "https://d009-superkartbackend.hf.space"  # Corrected URL
+            endpoint = f"{backend_url}/v1/salesbatch"
+            
+            st.info(f"Connecting to: {endpoint}")  # Show the endpoint being called
+            
+            files = {'file': (uploaded_file.name, uploaded_file, 'text/csv')}
+            response = requests.post(endpoint, files=files, timeout=30)
 
             if response.status_code == 200:
-                results = response.json()['predictions']
-                results_df = pd.DataFrame(results)
-
-                st.success("Batch predictions completed!")
-                st.dataframe(results_df)
-
-                # Download button for results
-                csv = results_df.to_csv(index=False)
-                st.download_button(
-                    label="Download predictions as CSV",
-                    data=csv,
-                    file_name='sales_predictions.csv',
-                    mime='text/csv'
-                )
+                try:
+                    results = response.json()
+                    if 'predictions' in results:
+                        results_df = pd.DataFrame(results['predictions'])
+                        st.success("Batch predictions completed!")
+                        st.dataframe(results_df)
+
+                        # Download button for results
+                        csv = results_df.to_csv(index=False)
+                        st.download_button(
+                            label="Download predictions as CSV",
+                            data=csv,
+                            file_name='sales_predictions.csv',
+                            mime='text/csv'
+                        )
+                    else:
+                        st.error("Unexpected response format from server")
+                        st.json(results)
+                except ValueError:
+                    st.error("Could not decode JSON response from server")
+                    st.text(f"Raw response: {response.text}")
             else:
-                st.error(f"Error making predictions: {response.json().get('error', 'Unknown error')}")
-        except Exception as e:
+                st.error(f"Error making predictions (Status {response.status_code})")
+                try:
+                    error_details = response.json()
+                    st.json(error_details)
+                except ValueError:
+                    st.text(f"Raw response: {response.text}")
+        except requests.exceptions.RequestException as e:
             st.error(f"Connection error: {str(e)}")
+            st.info("Please check if the backend server is running and accessible")
 
 # Add sample data section
 st.sidebar.markdown("### Sample Data")
@@ -123,4 +158,9 @@ if st.sidebar.button("Show Sample Input"):
     }
     
     st.sidebar.json(sample_data)
-
+    st.sidebar.download_button(
+        label="Download Sample CSV",
+        data=pd.DataFrame([sample_data]).to_csv(index=False),
+        file_name='sample_input.csv',
+        mime='text/csv'
+    )