Update app.py

app.py

@@ -140,43 +140,43 @@ def real_time_analysis():
     st.info("Listening... Say 'stop' to end the process.")
 
     def audio_frame_callback(audio_frame):
-        # Convert audio frame to bytes
-        audio_data = audio_frame.to_ndarray()
-        print(f"Audio data shape: {audio_data.shape}")  # Debug: Check audio data shape
-        print(f"Audio data sample: {audio_data[:10]}")  # Debug: Check first 10 samples
+    # Convert audio frame to bytes
+    audio_data = audio_frame.to_ndarray()
+    print(f"Audio data shape: {audio_data.shape}")  # Debug: Check audio data shape
+    print(f"Audio data sample: {audio_data[:10]}")  # Debug: Check first 10 samples
 
-        audio_bytes = (audio_data * 32767).astype(np.int16).tobytes()  # Convert to int16 format
+    audio_bytes = (audio_data * 32767).astype(np.int16).tobytes()  # Convert to int16 format
 
-        # Transcribe the audio
-        text = transcribe_audio(audio_bytes)
-        if text:
-            st.write(f"*Recognized Text:* {text}")
+    # Transcribe the audio
+    text = transcribe_audio(audio_bytes)
+    if text:
+        st.write(f"*Recognized Text:* {text}")
 
-            # Analyze sentiment
-            sentiment, score = analyze_sentiment(text)
-            st.write(f"*Sentiment:* {sentiment} (Score: {score})")
+        # Analyze sentiment
+        sentiment, score = analyze_sentiment(text)
+        st.write(f"*Sentiment:* {sentiment} (Score: {score})")
 
-            # Handle objection
-            objection_response = handle_objection(text)
-            st.write(f"*Objection Response:* {objection_response}")
+        # Handle objection
+        objection_response = handle_objection(text)
+        st.write(f"*Objection Response:* {objection_response}")
 
-            # Get product recommendation
-            recommendations = []
-            if is_valid_input(text) and is_relevant_sentiment(score):
-                query_embedding = model.encode([text])
-                distances, indices = product_recommender.index.search(query_embedding, 1)
+        # Get product recommendation
+        recommendations = []
+        if is_valid_input(text) and is_relevant_sentiment(score):
+            query_embedding = model.encode([text])
+            distances, indices = product_recommender.index.search(query_embedding, 1)
 
-                if distances[0][0] < 1.5:  # Similarity threshold
-                    recommendations = product_recommender.get_recommendations(text)
+            if distances[0][0] < 1.5:  # Similarity threshold
+                recommendations = product_recommender.get_recommendations(text)
 
-            if recommendations:
-                st.write("*Product Recommendations:*")
-                for rec in recommendations:
-                    st.write(rec)
-        else:
-            st.error("No transcription returned.")  # Debug: Check if transcription fails
+        if recommendations:
+            st.write("*Product Recommendations:*")
+            for rec in recommendations:
+                st.write(rec)
+    else:
+        st.error("No transcription returned.")  # Debug: Check if transcription fails
 
-        return audio_frame
+    return audio_frame
 
     # Start WebRTC audio stream
     webrtc_ctx = webrtc_streamer(