Update app.py

fully modified code

app.py

@@ -250,92 +250,90 @@ def main():
         # Prediction button
         if st.button("🔍 Identify Bird Species", type="primary", use_container_width=True):
             with st.spinner("🔄 Processing audio and making prediction..."):
-                # Process button
-                # Process button
-                if st.button("🔍 Identify Bird Species", type="primary", use_container_width=True):
-                with st.spinner("🔄 Processing audio and making prediction..."):
                 try:
-                # Get the uploaded file bytes
-                    audio_bytes = uploaded_file.getvalue()
+                    # Create temporary file with proper handling
+                    with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
+                        # Write the uploaded file data
+                        tmp_file.write(uploaded_file.getvalue())
+                        tmp_file.flush()  # Ensure data is written
+                        tmp_file_path = tmp_file.name
                     
-                    # Save to a specific location
-                    temp_dir = "/tmp"
-                    temp_filename = f"uploaded_audio_{hash(audio_bytes) % 10000}.wav"
-                    temp_path = os.path.join(temp_dir, temp_filename)
+                    # Verify file was created successfully
+                    if not os.path.exists(tmp_file_path):
+                        st.error("❌ Failed to create temporary file")
+                        return
                     
-                    # Write file
-                    with open(temp_path, 'wb') as f:
-                        f.write(audio_bytes)
+                    file_size = os.path.getsize(tmp_file_path)
+                    if file_size == 0:
+                        st.error("❌ Temporary file is empty")
+                        return
                     
-                    # Verify file exists
-                    if os.path.exists(temp_path) and os.path.getsize(temp_path) > 0:
-                        st.write(f"✅ Audio file saved: {os.path.getsize(temp_path)} bytes")
-                        
-                        # Process audio
-                        spectrogram = preprocess_audio(temp_path)
-                        
-                        # Rest of your processing code...
-                        
-                    else:
-                        st.error("❌ Failed to save audio file")
-                        
-                except Exception as e:
-                    st.error(f"❌ Error: {str(e)}")
-
-
-                
-                # Process and predict
-                spectrogram = preprocess_audio(tmp_file_path)
-                
-                if spectrogram is not None:
-                    predicted_species, confidence, top3_predictions = predict_bird_species(
-                        model, spectrogram, label_encoder, device
-                    )
+                    st.write(f"✅ Temporary file created: {file_size} bytes")
                     
-                    # Clean up
-                    os.unlink(tmp_file_path)
+                    # Process audio
+                    spectrogram = preprocess_audio(tmp_file_path)
                     
-                    if predicted_species is not None:
-                        # Display results
-                        st.success("🎉 Prediction Complete!")
+                    if spectrogram is not None:
+                        predicted_species, confidence, top3_predictions = predict_bird_species(
+                            model, spectrogram, label_encoder, device
+                        )
                         
-                        # Main prediction
-                        st.subheader("🏆 Primary Prediction")
-                        clean_species = predicted_species.replace("_sound", "").replace("_", " ")
+                        # Clean up temp file
+                        try:
+                            os.unlink(tmp_file_path)
+                        except:
+                            pass  # Ignore cleanup errors
                         
-                        col1, col2 = st.columns([2, 1])
-                        with col1:
-                            st.metric(
-                                label="Predicted Species",
-                                value=clean_species,
-                                delta=f"{confidence:.1%} confidence"
+                        # Display results
+                        if predicted_species is not None:
+                            st.success("🎉 Prediction Complete!")
+                            
+                            # Main prediction
+                            st.subheader("🏆 Primary Prediction")
+                            clean_species = predicted_species.replace("_sound", "").replace("_", " ")
+                            
+                            col1, col2 = st.columns([2, 1])
+                            with col1:
+                                st.metric(
+                                    label="Predicted Species",
+                                    value=clean_species,
+                                    delta=f"{confidence:.1%} confidence"
+                                )
+                            
+                            with col2:
+                                if confidence > 0.8:
+                                    st.success("🎯 High Confidence")
+                                elif confidence > 0.6:
+                                    st.warning("⚠️ Moderate Confidence")
+                                else:
+                                    st.info("💭 Low Confidence")
+                            
+                            # Top 3 predictions
+                            st.subheader("📊 Alternative Predictions")
+                            for i, (species, prob) in enumerate(top3_predictions):
+                                clean_name = species.replace("_sound", "").replace("_", " ")
+                                st.write(f"**{i+1}.** {clean_name}")
+                                st.progress(prob)
+                                st.caption(f"Confidence: {prob:.1%}")
+                            
+                            # Conservation note
+                            st.subheader("🌿 Conservation Impact")
+                            st.info(
+                                f"Identifying '{clean_species}' helps with biodiversity monitoring "
+                                "and conservation efforts in national parks and protected areas."
                             )
-                        
-                        with col2:
-                            if confidence > 0.8:
-                                st.success("🎯 High Confidence")
-                            elif confidence > 0.6:
-                                st.warning("⚠️ Moderate Confidence")
-                            else:
-                                st.info("💭 Low Confidence")
-                        
-                        # Top 3 predictions
-                        st.subheader("📊 Alternative Predictions")
-                        for i, (species, prob) in enumerate(top3_predictions):
-                            clean_name = species.replace("_sound", "").replace("_", " ")
-                            st.write(f"**{i+1}.** {clean_name}")
-                            st.progress(prob)
-                            st.caption(f"Confidence: {prob:.1%}")
-                        
-                        # Conservation note
-                        st.subheader("🌿 Conservation Impact")
-                        st.info(
-                            f"Identifying '{clean_species}' helps with biodiversity monitoring "
-                            "and conservation efforts in national parks and protected areas."
-                        )
-                
-                else:
-                    st.error("❌ Failed to process audio file.")
+                    
+                    else:
+                        st.error("❌ Failed to process audio file.")
+                    
+                except Exception as e:
+                    st.error(f"❌ Error processing audio: {str(e)}")
+                    # Clean up on error
+                    try:
+                        if 'tmp_file_path' in locals():
+                            os.unlink(tmp_file_path)
+                    except:
+                        pass
     
     # Footer
     st.markdown("---")