Update app.py

app.py

@@ -15,75 +15,78 @@ from io import BytesIO
 import wave
 
 # Initialize components
-objection_handler = ObjectionHandler('objections.csv')
-product_recommender = ProductRecommender('recommendations.csv')
+product_recommender = ProductRecommender(r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet2.csv")
+objection_handler = ObjectionHandler(r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet3.csv")
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
-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."
-
-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
-        responses = objection_handler.handle_objection(text)
-        return "\n".join(responses) if responses else "No objection response found."
-    return "No objection response found."
+def generate_comprehensive_summary(chunks):
+    """
+    Generate a comprehensive summary from conversation chunks
+    """
+    # Extract full text from chunks
+    full_text = " ".join([chunk[0] for chunk in chunks])
+    
+    # Perform basic analysis
+    total_chunks = len(chunks)
+    sentiments = [chunk[1] for chunk in chunks]
+    
+    # Determine overall conversation context
+    context_keywords = {
+        'product_inquiry': ['dress', 'product', 'price', 'stock'],
+        'pricing': ['cost', 'price', 'budget'],
+        'negotiation': ['installment', 'payment', 'manage']
+    }
+    
+    # Detect conversation themes
+    themes = []
+    for keyword_type, keywords in context_keywords.items():
+        if any(keyword.lower() in full_text.lower() for keyword in keywords):
+            themes.append(keyword_type)
+    
+    # Basic sentiment analysis
+    positive_count = sentiments.count('POSITIVE')
+    negative_count = sentiments.count('NEGATIVE')
+    neutral_count = sentiments.count('NEUTRAL')
+    
+    # Key interaction highlights
+    key_interactions = []
+    for chunk in chunks:
+        if any(keyword.lower() in chunk[0].lower() for keyword in ['price', 'dress', 'stock', 'installment']):
+            key_interactions.append(chunk[0])
+    
+    # Construct summary
+    summary = f"Conversation Summary:\n"
+    
+    # Context and themes
+    if 'product_inquiry' in themes:
+        summary += "• Customer initiated a product inquiry about items.\n"
+    
+    if 'pricing' in themes:
+        summary += "• Price and budget considerations were discussed.\n"
+    
+    if 'negotiation' in themes:
+        summary += "• Customer and seller explored flexible payment options.\n"
+    
+    # Sentiment insights
+    summary += f"\nConversation Sentiment:\n"
+    summary += f"• Positive Interactions: {positive_count}\n"
+    summary += f"• Negative Interactions: {negative_count}\n"
+    summary += f"• Neutral Interactions: {neutral_count}\n"
+    
+    # Key highlights
+    summary += "\nKey Conversation Points:\n"
+    for interaction in key_interactions[:3]:  # Limit to top 3 key points
+        summary += f"• {interaction}\n"
+    
+    # Conversation outcome
+    if positive_count > negative_count:
+        summary += "\nOutcome: Constructive and potentially successful interaction."
+    elif negative_count > positive_count:
+        summary += "\nOutcome: Interaction may require further follow-up."
+    else:
+        summary += "\nOutcome: Neutral interaction with potential for future engagement."
+    
+    return summary
 
 def is_valid_input(text):
     text = text.strip().lower()
@@ -94,109 +97,25 @@ def is_valid_input(text):
 def is_relevant_sentiment(sentiment_score):
     return sentiment_score > 0.4
 
-def run_app():
-    st.set_page_config(page_title="Sales Call Assistant", layout="wide")
-    st.title("AI Sales Call Assistant")
-
-    st.sidebar.title("Navigation")
-    app_mode = st.sidebar.radio("Choose a mode:", ["Real-Time Call Analysis", "Dashboard"])
-
-    if app_mode == "Real-Time Call Analysis":
-        st.header("Real-Time Sales Call Analysis")
-        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_counts = data['Sentiment'].value_counts()
-
-                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)
-
-                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)
-
-                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']])
+def calculate_overall_sentiment(sentiment_scores):
+    if sentiment_scores:
+        average_sentiment = sum(sentiment_scores) / len(sentiment_scores)
+        overall_sentiment = (
+            "POSITIVE" if average_sentiment > 0 else
+            "NEGATIVE" if average_sentiment < 0 else
+            "NEUTRAL"
+        )
+    else:
+        overall_sentiment = "NEUTRAL"
+    return overall_sentiment
 
-                unique_call_ids = data[data['Call ID'] != '']['Call ID'].unique()
-                call_id = st.selectbox("Select a Call ID to view details:", unique_call_ids)
-
-                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']}")
-
-                    st.subheader("Full Call Summary")
-                    st.text_area("Summary:",
-                                 value=call_details.iloc[0]['Summary'],
-                                 height=200,
-                                 disabled=True)
-
-                    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("---")
-                else:
-                    st.error("No details available for the selected Call ID.")
-        except Exception as e:
-            st.error(f"Error loading dashboard: {e}")
-
-if __name__ == "__main__":
-    run_app()from streamlit_webrtc import webrtc_streamer, WebRtcMode
-from sentiment_analysis import analyze_sentiment
-from product_recommender import ProductRecommender
-from objection_handler import ObjectionHandler
-from google_sheets import fetch_call_data, store_data_in_sheet
-from sentence_transformers import SentenceTransformer
-from env_setup import config
-import re
-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')
-product_recommender = ProductRecommender('recommendations.csv')
-model = SentenceTransformer('all-MiniLM-L6-v2')
+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
+        responses = objection_handler.handle_objection(text)
+        return "\n".join(responses) if responses else "No objection response found."
+    return "No objection response found."
 
 def real_time_analysis():
     st.info("Listening... Say 'stop' to end the process.")
@@ -256,23 +175,6 @@ def transcribe_audio(audio_bytes):
     # For now, we'll just return a dummy text
     return "This is a placeholder transcription."
 
-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
-        responses = objection_handler.handle_objection(text)
-        return "\n".join(responses) if responses else "No objection response found."
-    return "No objection response found."
-
-def is_valid_input(text):
-    text = text.strip().lower()
-    if len(text) < 3 or re.match(r'^[a-zA-Z\s]*$', text) is None:
-        return False
-    return True
-
-def is_relevant_sentiment(sentiment_score):
-    return sentiment_score > 0.4
-
 def run_app():
     st.set_page_config(page_title="Sales Call Assistant", layout="wide")
     st.title("AI Sales Call Assistant")
@@ -291,65 +193,73 @@ def run_app():
             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,
+                        values=sentiment_counts.values, 
+                        names=sentiment_counts.index, 
                         title='Call Sentiment Breakdown',
                         color_discrete_map={
-                            'POSITIVE': 'green',
-                            'NEGATIVE': 'red',
+                            '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,
+                        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',
+                            '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,
+                    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']):
+                        if pd.notna(row['Chunk']):  
                             st.write(f"**Chunk:** {row['Chunk']}")
                             st.write(f"**Sentiment:** {row['Sentiment']}")
-                            st.write("---")
+                            st.write("---")  # Separator between chunks
                 else:
                     st.error("No details available for the selected Call ID.")
         except Exception as e: