Update app.py

app.py

@@ -15,8 +15,8 @@ import queue
 import threading
 
 # Initialize components
-objection_handler = ObjectionHandler("objections.csv")  # Use relative path
-product_recommender = ProductRecommender("recommendations.csv")  # Use relative path
+objection_handler = ObjectionHandler("objections.csv")
+product_recommender = ProductRecommender("recommendations.csv")
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
 # Queue to hold transcribed text
@@ -41,7 +41,7 @@ def calculate_overall_sentiment(sentiment_scores):
 def handle_objection(text):
     query_embedding = model.encode([text])
     distances, indices = objection_handler.index.search(query_embedding, 1)
-    if distances[0][0] < 1.5:  # Adjust similarity threshold as needed
+    if distances[0][0] < 1.5:
         responses = objection_handler.handle_objection(text)
         return "\n".join(responses) if responses else "No objection response found."
     return "No objection response found."
@@ -55,23 +55,20 @@ class AudioProcessor(AudioProcessorBase):
         audio_data = frame.to_ndarray()
         audio_bytes = (audio_data * 32767).astype(np.int16).tobytes()  # Convert to int16 format
         
-        # Debugging: Check audio data
         print(f"Audio data shape: {audio_data.shape}")
         print(f"Audio data sample: {audio_data[:10]}")
         
-        # Transcribe the audio
         text = self.transcribe_audio(audio_bytes)
         if text:
-            self.q.put(text)  # Add transcribed text to the queue
+            self.q.put(text)
 
         return frame
 
     def transcribe_audio(self, audio_bytes):
         try:
-            # Use the transcribe_with_chunks function from sentiment_analysis.py
-            chunks = transcribe_with_chunks({})  # Pass an empty objections_dict for now
+            chunks = transcribe_with_chunks({})
             if chunks:
-                return chunks[-1][0]  # Return the latest transcribed text
+                return chunks[-1][0]
         except Exception as e:
             print(f"Error transcribing audio: {e}")
         return None
@@ -79,7 +76,6 @@ class AudioProcessor(AudioProcessorBase):
 def real_time_analysis():
     st.info("Listening... Say 'stop' to end the process.")
 
-    # Start WebRTC audio stream
     webrtc_ctx = webrtc_streamer(
         key="real-time-audio",
         mode=WebRtcMode.SENDONLY,
@@ -88,26 +84,22 @@ def real_time_analysis():
     )
 
     if webrtc_ctx.state.playing:
-        # Display transcribed text from the queue
         while not transcription_queue.empty():
             text = transcription_queue.get()
             st.write(f"*Recognized Text:* {text}")
 
-            # 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}")
 
-            # 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
+                if distances[0][0] < 1.5:
                     recommendations = product_recommender.get_recommendations(text)
 
             if recommendations:
@@ -120,16 +112,13 @@ def fetch_data_and_display():
         st.header("Call Summaries and Sentiment Analysis")
         data = fetch_call_data(config["google_sheet_id"])
         
-        # Debugging: Log fetched data
-        print(f"Fetched data: {data}")
+        print(f"Fetched data: {data}")  # Log fetched data
         
         if data.empty:
             st.warning("No data available in the Google Sheet.")
         else:
-            # Sentiment Visualizations
             sentiment_counts = data['Sentiment'].value_counts()
             
-            # Pie Chart
             col1, col2 = st.columns(2)
             with col1:
                 st.subheader("Sentiment Distribution")
@@ -145,7 +134,6 @@ def fetch_data_and_display():
                 )
                 st.plotly_chart(fig_pie)
 
-            # Bar Chart
             with col2:
                 st.subheader("Sentiment Counts")
                 fig_bar = px.bar(
@@ -162,37 +150,32 @@ def fetch_data_and_display():
                 )
                 st.plotly_chart(fig_bar)
 
-            # Existing Call Details Section
             st.subheader("All Calls")
             display_data = data.copy()
             display_data['Summary Preview'] = display_data['Summary'].str[:100] + '...'
             st.dataframe(display_data[['Call ID', 'Chunk', 'Sentiment', 'Summary Preview', 'Overall Sentiment']])
 
-            # Dropdown to select Call ID
             unique_call_ids = data[data['Call ID'] != '']['Call ID'].unique()
             call_id = st.selectbox("Select a Call ID to view details:", unique_call_ids)
 
-            # Display selected Call ID details
             call_details = data[data['Call ID'] == call_id]
             if not call_details.empty:
                 st.subheader("Detailed Call Information")
                 st.write(f"**Call ID:** {call_id}")
                 st.write(f"**Overall Sentiment:** {call_details.iloc[0]['Overall Sentiment']}")
 
-                # Expand summary section
                 st.subheader("Full Call Summary")
                 st.text_area("Summary:", 
                              value=call_details.iloc[0]['Summary'], 
                              height=200, 
                              disabled=True)
 
-                # Show all chunks for the selected call
                 st.subheader("Conversation Chunks")
                 for _, row in call_details.iterrows():
                     if pd.notna(row['Chunk']):  
                         st.write(f"**Chunk:** {row['Chunk']}")
                         st.write(f"**Sentiment:** {row['Sentiment']}")
-                        st.write("---")  # Separator between chunks
+                        st.write("---")
             else:
                 st.error("No details available for the selected Call ID.")
     except Exception as e: