Update app.py

app.py

@@ -4,11 +4,13 @@ from transformers import pipeline
 import librosa
 import numpy as np
 import plotly.graph_objects as go
+import tempfile
+import os
 
 # Set page config
 st.set_page_config(page_title="🎵 Music Genre Classifier", layout="wide")
 
-# Custom CSS
+# Custom CSS (unchanged)
 st.markdown("""
 <style>
     .main-title {
@@ -51,12 +53,22 @@ pipe = load_model()
 
 def classify_audio(audio_file):
     start_time = time.time()
-    y, sr = librosa.load(audio_file, sr=None)
-    preds = pipe(y)
-    outputs = {p["label"]: p["score"] for p in preds}
-    end_time = time.time()
-    prediction_time = end_time - start_time
-    return outputs, prediction_time
+    
+    # Save the uploaded file to a temporary file
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
+        tmp_file.write(audio_file.getvalue())
+        tmp_file_path = tmp_file.name
+    
+    try:
+        y, sr = librosa.load(tmp_file_path, sr=None)
+        preds = pipe(y)
+        outputs = {p["label"]: p["score"] for p in preds}
+        end_time = time.time()
+        prediction_time = end_time - start_time
+        return outputs, prediction_time, y, sr
+    finally:
+        # Make sure to remove the temporary file
+        os.unlink(tmp_file_path)
 
 st.markdown("<h1 class='main-title'>🎵 Music Genre Classifier</h1>", unsafe_allow_html=True)
 st.markdown("<p class='sub-title'>Upload a music file and let AI detect its genre!</p>", unsafe_allow_html=True)
@@ -75,41 +87,45 @@ if uploaded_file is not None:
     
     if st.button("Classify Genre"):
         with st.spinner("Analyzing the music... 🎧"):
-            results, pred_time = classify_audio(uploaded_file)
-            
-            # Get top genre
-            top_genre = max(results, key=results.get)
-            
-            st.markdown(f"<h2 class='genre-result'>Detected Genre: {top_genre.capitalize()}</h2>", unsafe_allow_html=True)
-            st.markdown(f"<p class='prediction-time'>Prediction Time: {pred_time:.2f} seconds</p>", unsafe_allow_html=True)
-            
-            # Create a bar chart using Plotly
-            fig = go.Figure(data=[go.Bar(
-                x=list(results.keys()),
-                y=list(results.values()),
-                marker_color='#1DB954'
-            )])
-            fig.update_layout(
-                title="Genre Probabilities",
-                xaxis_title="Genre",
-                yaxis_title="Probability",
-                paper_bgcolor='rgba(0,0,0,0)',
-                plot_bgcolor='rgba(0,0,0,0)'
-            )
-            st.plotly_chart(fig, use_container_width=True)
+            try:
+                results, pred_time, y, sr = classify_audio(uploaded_file)
+                
+                # Get top genre
+                top_genre = max(results, key=results.get)
+                
+                st.markdown(f"<h2 class='genre-result'>Detected Genre: {top_genre.capitalize()}</h2>", unsafe_allow_html=True)
+                st.markdown(f"<p class='prediction-time'>Prediction Time: {pred_time:.2f} seconds</p>", unsafe_allow_html=True)
+                
+                # Create a bar chart using Plotly
+                fig = go.Figure(data=[go.Bar(
+                    x=list(results.keys()),
+                    y=list(results.values()),
+                    marker_color='#1DB954'
+                )])
+                fig.update_layout(
+                    title="Genre Probabilities",
+                    xaxis_title="Genre",
+                    yaxis_title="Probability",
+                    paper_bgcolor='rgba(0,0,0,0)',
+                    plot_bgcolor='rgba(0,0,0,0)'
+                )
+                st.plotly_chart(fig, use_container_width=True)
 
-            # Display waveform
-            st.subheader("Audio Waveform")
-            y, sr = librosa.load(uploaded_file, sr=None)
-            fig_waveform = go.Figure(data=[go.Scatter(y=y, mode='lines', line=dict(color='#1DB954'))])
-            fig_waveform.update_layout(
-                title="Audio Waveform",
-                xaxis_title="Time",
-                yaxis_title="Amplitude",
-                paper_bgcolor='rgba(0,0,0,0)',
-                plot_bgcolor='rgba(0,0,0,0)'
-            )
-            st.plotly_chart(fig_waveform, use_container_width=True)
+                # Display waveform
+                st.subheader("Audio Waveform")
+                fig_waveform = go.Figure(data=[go.Scatter(y=y, mode='lines', line=dict(color='#1DB954'))])
+                fig_waveform.update_layout(
+                    title="Audio Waveform",
+                    xaxis_title="Time",
+                    yaxis_title="Amplitude",
+                    paper_bgcolor='rgba(0,0,0,0)',
+                    plot_bgcolor='rgba(0,0,0,0)'
+                )
+                st.plotly_chart(fig_waveform, use_container_width=True)
+            
+            except Exception as e:
+                st.error(f"An error occurred while processing the audio: {str(e)}")
+                st.info("Please try uploading the file again or use a different audio file.")
 
 st.markdown("""
 <div style='text-align: center; margin-top: 2rem;'>