File size: 7,415 Bytes
a616221 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
import os
import json
import time
import pyaudio
from vosk import Model, KaldiRecognizer
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()
# Initialize the ProductRecommender
product_recommender = ProductRecommender(r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet2.csv")
# 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)
# Vosk Speech Recognition Model
vosk_model_path = config["vosk_model_path"]
if not vosk_model_path:
raise ValueError("Error: vosk_model_path is not set in the .env file.")
try:
vosk_model = Model(vosk_model_path)
print("Vosk model loaded successfully.")
except Exception as e:
raise ValueError(f"Failed to load Vosk model: {e}")
recognizer = KaldiRecognizer(vosk_model, 16000)
audio = pyaudio.PyAudio()
stream = audio.open(format=pyaudio.paInt16,
channels=1,
rate=16000,
input=True,
frames_per_buffer=4000)
stream.start_stream()
# Function to analyze sentiment
def preprocess_text(text):
"""Preprocess text for better sentiment analysis."""
# Strip whitespace and convert to lowercase
processed = text.strip().lower()
return processed
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:
while True:
data = stream.read(4000, exception_on_overflow=False)
if recognizer.AcceptWaveform(data):
result = recognizer.Result()
text = json.loads(result)["text"]
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 KeyboardInterrupt:
print("\nExiting...")
stream.stop_stream()
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) |