sentiment_analysis.py

import os
import json
import time
from speech_recognition import Recognizer, Microphone, AudioData, UnknownValueError, RequestError
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
from huggingface_hub import login
from product_recommender import ProductRecommender
from objection_handler import load_objections, check_objections  # Ensure check_objections is imported
from objection_handler import ObjectionHandler
from env_setup import config
from sentence_transformers import SentenceTransformer
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Hugging Face API setup
huggingface_api_key = config["huggingface_api_key"]
login(token=huggingface_api_key)

# Sentiment Analysis Model
model_name = "tabularisai/multilingual-sentiment-analysis"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
sentiment_analyzer = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

# Speech Recognition Setup
recognizer = Recognizer()

# Function to analyze sentiment
def preprocess_text(text):
    """Preprocess text for better sentiment analysis."""
    return text.strip().lower()

def analyze_sentiment(text):
    """Analyze sentiment of the text using Hugging Face model."""
    try:
        if not text.strip():
            return "NEUTRAL", 0.0
        
        processed_text = preprocess_text(text)
        result = sentiment_analyzer(processed_text)[0]
        
        print(f"Sentiment Analysis Result: {result}")
        
        # Map raw labels to sentiments
        sentiment_map = {
            'Very Negative': "NEGATIVE",
            'Negative': "NEGATIVE",
            'Neutral': "NEUTRAL",
            'Positive': "POSITIVE",
            'Very Positive': "POSITIVE"
        }
        
        sentiment = sentiment_map.get(result['label'], "NEUTRAL")
        return sentiment, result['score']
        
    except Exception as e:
        print(f"Error in sentiment analysis: {e}")
        return "NEUTRAL", 0.5

def transcribe_with_chunks(objections_dict):
    """Perform real-time transcription with sentiment analysis."""
    print("Say 'start listening' to begin transcription. Say 'stop listening' to stop.")
    is_listening = False
    chunks = []
    current_chunk = []
    chunk_start_time = time.time()

    # Initialize handlers with semantic search capabilities
    objection_handler = ObjectionHandler(r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet3.csv")
    product_recommender = ProductRecommender(r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet2.csv")

    # Load the embeddings model once
    model = SentenceTransformer('all-MiniLM-L6-v2')

    try:
        with Microphone() as source:
            recognizer.adjust_for_ambient_noise(source)
            print("Microphone calibrated. Please speak.")
            
            while True:
                print("Listening for speech...")
                try:
                    audio_data = recognizer.listen(source, timeout=5)
                    text = recognizer.recognize_google(audio_data)

                    if "start listening" in text.lower():
                        is_listening = True
                        print("Listening started. Speak into the microphone.")
                        continue
                    elif "stop listening" in text.lower():
                        is_listening = False
                        print("Listening stopped.")
                        if current_chunk:
                            chunk_text = " ".join(current_chunk)
                            sentiment, score = analyze_sentiment(chunk_text)
                            chunks.append((chunk_text, sentiment, score))
                            current_chunk = []
                        continue

                    if is_listening and text.strip():
                        print(f"Transcription: {text}")
                        current_chunk.append(text)

                        if time.time() - chunk_start_time > 3:
                            if current_chunk:
                                chunk_text = " ".join(current_chunk)
                                
                                # Always process sentiment
                                sentiment, score = analyze_sentiment(chunk_text)
                                chunks.append((chunk_text, sentiment, score))

                                # Get objection responses and check similarity score
                                query_embedding = model.encode([chunk_text])
                                distances, indices = objection_handler.index.search(query_embedding, 1)
                                
                                # If similarity is high enough, show objection response
                                if distances[0][0] < 1.5:  # Threshold for similarity
                                    responses = objection_handler.handle_objection(chunk_text)
                                    if responses:
                                        print("\nSuggested Response:")
                                        for response in responses:
                                            print(f"→ {response}")
                                
                                # Get product recommendations and check similarity score
                                distances, indices = product_recommender.index.search(query_embedding, 1)
                                
                                # If similarity is high enough, show recommendations
                                if distances[0][0] < 1.5:  # Threshold for similarity
                                    recommendations = product_recommender.get_recommendations(chunk_text)
                                    if recommendations:
                                        print(f"\nRecommendations for this response:")
                                        for idx, rec in enumerate(recommendations, 1):
                                            print(f"{idx}. {rec}")
                                
                                print("\n")
                                current_chunk = []
                                chunk_start_time = time.time()
                except UnknownValueError:
                    print("Could not understand the audio.")
                except RequestError as e:
                    print(f"Could not request results from Google Speech Recognition service; {e}")

    except KeyboardInterrupt:
        print("\nExiting...")
        return chunks

if __name__ == "__main__":
    objections_file_path = r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet3.csv"
    objections_dict = load_objections(objections_file_path)
    transcribed_chunks = transcribe_with_chunks(objections_dict)
    print("Final transcriptions and sentiments:", transcribed_chunks)