Update app.py

app.py

@@ -1,5 +1,4 @@
-from streamlit_webrtc import webrtc_streamer, WebRtcMode
-from sentiment_analysis import analyze_sentiment
+from sentiment_analysis import analyze_sentiment, transcribe_with_chunks
 from product_recommender import ProductRecommender
 from objection_handler import ObjectionHandler
 from google_sheets import fetch_call_data, store_data_in_sheet
@@ -10,14 +9,10 @@ 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
-# Initialize components
-product_recommender = ProductRecommender("recommendations.csv")  # Relative path
-objection_handler = ObjectionHandler("objections.csv")  # Relative path
+objection_handler = ObjectionHandler("objections.csv")  # Use relative path
+product_recommender = ProductRecommender("recommendations.csv")  # Use relative path
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
 def generate_comprehensive_summary(chunks):
@@ -121,60 +116,29 @@ def handle_objection(text):
 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_bytes = audio_frame.to_ndarray().tobytes()
-
-        # Save audio bytes to a temporary WAV file
-        with BytesIO() as wav_buffer:
-            with wave.open(wav_buffer, 'wb') as wav_file:
-                wav_file.setnchannels(1)  # Mono audio
-                wav_file.setsampwidth(2)  # 2 bytes for int16
-                wav_file.setframerate(16000)  # Sample rate
-                wav_file.writeframes(audio_bytes)
-
-            # Transcribe the audio
-            text = transcribe_audio(wav_buffer.getvalue())
-            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 transcribe_audio(audio_bytes):
-    """Transcribe audio using a speech-to-text model or API."""
-    # Replace this with your actual speech-to-text implementation
-    # For now, we'll just return a dummy text
-    return "This is a placeholder transcription."
+    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 run_app():
     st.set_page_config(page_title="Sales Call Assistant", layout="wide")
@@ -185,7 +149,8 @@ def run_app():
 
     if app_mode == "Real-Time Call Analysis":
         st.header("Real-Time Sales Call Analysis")
-        real_time_analysis()
+        if st.button("Start Listening"):
+            real_time_analysis()
 
     elif app_mode == "Dashboard":
         st.header("Call Summaries and Sentiment Analysis")