app.py

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
# Initialize components
product_recommender = ProductRecommender("recommendations.csv")  # Relative path
objection_handler = ObjectionHandler("objections.csv")  # Relative path
model = SentenceTransformer('all-MiniLM-L6-v2')

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()
    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 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

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.")

    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 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 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}")

if __name__ == "__main__":
    run_app()