Update app.py

app.py

@@ -12,6 +12,7 @@ import plotly.express as px
 import streamlit as st
 import numpy as np
 import queue
+import threading
 
 # Initialize components
 objection_handler = ObjectionHandler("objections.csv")  # Use relative path
@@ -53,7 +54,11 @@ class AudioProcessor(AudioProcessorBase):
     def recv(self, frame):
         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:
@@ -82,32 +87,116 @@ def real_time_analysis():
         media_stream_constraints={"audio": True, "video": False},
     )
 
-    # Display transcribed text from the queue
-    while not transcription_queue.empty():
-        text = transcription_queue.get()
-        st.write(f"*Recognized Text:* {text}")
+    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})")
 
-        # 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)
 
-        if recommendations:
-            st.write("*Product Recommendations:*")
-            for rec in recommendations:
-                st.write(rec)
+def fetch_data_and_display():
+    try:
+        st.header("Call Summaries and Sentiment Analysis")
+        data = fetch_call_data(config["google_sheet_id"])
+        
+        # Debugging: Log fetched data
+        print(f"Fetched data: {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")
+                fig_pie = px.pie(
+                    values=sentiment_counts.values, 
+                    names=sentiment_counts.index, 
+                    title='Call Sentiment Breakdown',
+                    color_discrete_map={
+                        'POSITIVE': 'green', 
+                        'NEGATIVE': 'red', 
+                        'NEUTRAL': 'blue'
+                    }
+                )
+                st.plotly_chart(fig_pie)
+
+            # Bar Chart
+            with col2:
+                st.subheader("Sentiment Counts")
+                fig_bar = px.bar(
+                    x=sentiment_counts.index, 
+                    y=sentiment_counts.values, 
+                    title='Number of Calls by Sentiment',
+                    labels={'x': 'Sentiment', 'y': 'Number of Calls'},
+                    color=sentiment_counts.index,
+                    color_discrete_map={
+                        'POSITIVE': 'green', 
+                        'NEGATIVE': 'red', 
+                        'NEUTRAL': 'blue'
+                    }
+                )
+                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
+            else:
+                st.error("No details available for the selected Call ID.")
+    except Exception as e:
+        st.error(f"Error loading dashboard: {e}")
 
 def run_app():
     st.set_page_config(page_title="Sales Call Assistant", layout="wide")
@@ -121,83 +210,7 @@ def run_app():
         real_time_analysis()
 
     elif app_mode == "Dashboard":
-        st.header("Call Summaries and Sentiment Analysis")
-        try:
-            data = fetch_call_data(config["google_sheet_id"])
-            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")
-                    fig_pie = px.pie(
-                        values=sentiment_counts.values, 
-                        names=sentiment_counts.index, 
-                        title='Call Sentiment Breakdown',
-                        color_discrete_map={
-                            'POSITIVE': 'green', 
-                            'NEGATIVE': 'red', 
-                            'NEUTRAL': 'blue'
-                        }
-                    )
-                    st.plotly_chart(fig_pie)
-
-                # Bar Chart
-                with col2:
-                    st.subheader("Sentiment Counts")
-                    fig_bar = px.bar(
-                        x=sentiment_counts.index, 
-                        y=sentiment_counts.values, 
-                        title='Number of Calls by Sentiment',
-                        labels={'x': 'Sentiment', 'y': 'Number of Calls'},
-                        color=sentiment_counts.index,
-                        color_discrete_map={
-                            'POSITIVE': 'green', 
-                            'NEGATIVE': 'red', 
-                            'NEUTRAL': 'blue'
-                        }
-                    )
-                    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
-                else:
-                    st.error("No details available for the selected Call ID.")
-        except Exception as e:
-            st.error(f"Error loading dashboard: {e}")
+        fetch_data_and_display()
 
 if __name__ == "__main__":
     run_app()