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 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") # Use relative path product_recommender = ProductRecommender("recommendations.csv") # Use 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 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.") 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") 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()