app.py

import sounddevice as sd
import numpy as np
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

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

def real_time_analysis():
    st.info("Listening... Say 'stop' to end the process.")

    samplerate = 16000  # Sample rate for audio capture
    duration = 5  # Duration of each audio chunk in seconds
    sentiment_scores = []
    transcribed_chunks = []
    total_text = ""

    try:
        while True:
            # Capture audio
            audio_data = sd.rec(int(samplerate * duration), samplerate=samplerate, channels=1, dtype='float32')
            sd.wait()  # Wait for the recording to finish

            # Convert audio data to bytes for processing
            audio_bytes = (audio_data * 32767).astype(np.int16).tobytes()

            # Analyze the audio
            text = analyze_audio(audio_bytes, samplerate)
            if not text:
                continue

            st.write(f"*Recognized Text:* {text}")

            if 'stop' in text.lower():
                st.write("Stopping real-time analysis...")
                break

            # Append to the total conversation
            total_text += text + " "
            sentiment, score = analyze_sentiment(text)
            sentiment_scores.append(score)

            # Handle objection
            objection_response = handle_objection(text)

            # 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)

            transcribed_chunks.append((text, sentiment, score))

            st.write(f"*Sentiment:* {sentiment} (Score: {score})")
            st.write(f"*Objection Response:* {objection_response}")

            if recommendations:
                st.write("*Product Recommendations:*")
                for rec in recommendations:
                    st.write(rec)

        # After conversation ends, calculate and display overall sentiment and summary
        overall_sentiment = calculate_overall_sentiment(sentiment_scores)
        call_summary = generate_comprehensive_summary(transcribed_chunks)

        st.subheader("Conversation Summary:")
        st.write(total_text.strip())
        st.subheader("Overall Sentiment:")
        st.write(overall_sentiment)

        # Store data in Google Sheets
        store_data_in_sheet(
            config["google_sheet_id"],
            transcribed_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 analyze_audio(audio_bytes, samplerate):
    """Analyze audio data and return transcribed text."""
    try:
        # Use a speech-to-text model or API to transcribe the audio
        # For simplicity, we'll use a placeholder function
        text = transcribe_audio(audio_bytes, samplerate)
        return text
    except Exception as e:
        st.error(f"Error analyzing audio: {e}")
        return None

def transcribe_audio(audio_bytes, samplerate):
    """Placeholder function for transcribing audio."""
    # 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 generate_comprehensive_summary(chunks):
    """Generate a comprehensive summary from conversation chunks."""
    full_text = " ".join([chunk[0] for chunk in chunks])
    total_chunks = len(chunks)
    sentiments = [chunk[1] for chunk in chunks]

    context_keywords = {
        'product_inquiry': ['dress', 'product', 'price', 'stock'],
        'pricing': ['cost', 'price', 'budget'],
        'negotiation': ['installment', 'payment', 'manage']
    }

    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)

    positive_count = sentiments.count('POSITIVE')
    negative_count = sentiments.count('NEGATIVE')
    neutral_count = sentiments.count('NEUTRAL')

    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])

    summary = f"Conversation Summary:\n"
    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"

    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"

    summary += "\nKey Conversation Points:\n"
    for interaction in key_interactions[:3]:
        summary += f"• {interaction}\n"

    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 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")
        if st.button("Start Listening"):
            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']])

                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()