Update app.py

app.py

@@ -1,3 +1,4 @@
+from streamlit_webrtc import webrtc_streamer, WebRtcMode
 from sentiment_analysis import analyze_sentiment, transcribe_with_chunks
 from product_recommender import ProductRecommender
 from objection_handler import ObjectionHandler
@@ -9,6 +10,9 @@ import uuid
 import pandas as pd
 import plotly.express as px
 import streamlit as st
+import numpy as np
+from io import BytesIO
+import wave
 
 # Initialize components
 objection_handler = ObjectionHandler("objections.csv")  # Use relative path
@@ -113,32 +117,68 @@ def handle_objection(text):
         return "\n".join(responses) if responses else "No objection response found."
     return "No objection response found."
 
+def transcribe_audio(audio_bytes):
+    """Transcribe audio using the transcribe_with_chunks function from sentiment_analysis.py."""
+    try:
+        # Save audio bytes to a temporary WAV file
+        with BytesIO() as wav_buffer:
+            with wave.open(wav_buffer, 'wb') as wf:
+                wf.setnchannels(1)  # Mono audio
+                wf.setsampwidth(2)  # 2 bytes for int16
+                wf.setframerate(16000)  # Sample rate
+                wf.writeframes(audio_bytes)
+
+            # Use the transcribe_with_chunks function from sentiment_analysis.py
+            chunks = transcribe_with_chunks({})  # Pass an empty objections_dict for now
+            if chunks:
+                return chunks[-1][0]  # Return the latest transcribed text
+    except Exception as e:
+        print(f"Error transcribing audio: {e}")
+    return None
+
 def real_time_analysis():
     st.info("Listening... Say 'stop' to end the process.")
 
-    try:
-        # Use the transcribe_with_chunks function from sentiment_analysis.py
-        chunks = transcribe_with_chunks({})  # Pass an empty objections_dict for now
-        if chunks:
-            # After conversation ends, calculate and display overall sentiment and summary
-            overall_sentiment = calculate_overall_sentiment([chunk[2] for chunk in chunks])
-            call_summary = generate_comprehensive_summary(chunks)
-            
-            st.subheader("Conversation Summary:")
-            st.write(call_summary)
-            st.subheader("Overall Sentiment:")
-            st.write(overall_sentiment)
-
-            # Store data in Google Sheets
-            store_data_in_sheet(
-                config["google_sheet_id"], 
-                chunks, 
-                call_summary, 
-                overall_sentiment
-            )
-            st.success("Conversation data stored successfully in Google Sheets!")
-    except Exception as e:
-        st.error(f"Error in real-time analysis: {e}")
+    def audio_frame_callback(audio_frame):
+        # Convert audio frame to bytes
+        audio_bytes = audio_frame.to_ndarray().tobytes()
+
+        # 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})")
+
+            # 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
+                    recommendations = product_recommender.get_recommendations(text)
+
+            if recommendations:
+                st.write("*Product Recommendations:*")
+                for rec in recommendations:
+                    st.write(rec)
+
+        return audio_frame
+
+    # Start WebRTC audio stream
+    webrtc_ctx = webrtc_streamer(
+        key="real-time-audio",
+        mode=WebRtcMode.SENDONLY,
+        audio_frame_callback=audio_frame_callback,
+        media_stream_constraints={"audio": True, "video": False},
+    )
 
 def run_app():
     st.set_page_config(page_title="Sales Call Assistant", layout="wide")
@@ -149,8 +189,7 @@ def run_app():
 
     if app_mode == "Real-Time Call Analysis":
         st.header("Real-Time Sales Call Analysis")
-        if st.button("Start Listening"):
-            real_time_analysis()
+        real_time_analysis()
 
     elif app_mode == "Dashboard":
         st.header("Call Summaries and Sentiment Analysis")