Update sentiment_analysis.py

sentiment_analysis.py

@@ -5,7 +5,7 @@ from speech_recognition import Recognizer, Microphone, AudioData, UnknownValueEr
 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 load_objections, check_objections
 from objection_handler import ObjectionHandler
 from env_setup import config
 from sentence_transformers import SentenceTransformer
@@ -61,6 +61,10 @@ def analyze_sentiment(text):
 
 def transcribe_with_chunks(objections_dict):
     print("Note: If microphone access fails, please use alternative input.")
+    chunks = []
+    current_chunk = []
+    chunk_start_time = time.time()
+    is_listening = False
 
     try:
         # Try to list available microphones
@@ -69,6 +73,17 @@ def transcribe_with_chunks(objections_dict):
     except Exception as e:
         print(f"Could not detect microphones: {e}")
 
+    # Replace hardcoded path with environment variable or relative path
+    objection_file_path = config.get("OBJECTION_DATA_PATH", "objections.csv")
+    product_file_path = config.get("PRODUCT_DATA_PATH", "recommendations.csv")
+
+    # Initialize handlers with semantic search capabilities
+    objection_handler = ObjectionHandler(objection_file_path)
+    product_recommender = ProductRecommender(product_file_path)
+
+    # Load the embeddings model once
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+
     try:
         # Try multiple device indices
         mic = None
@@ -84,14 +99,6 @@ def transcribe_with_chunks(objections_dict):
             print("No microphone available. Please provide text input.")
             return []
 
-    # 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 mic as source:
             recognizer.adjust_for_ambient_noise(source)
             print("Microphone calibrated. Please speak.")
@@ -102,7 +109,6 @@ def transcribe_with_chunks(objections_dict):
                     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.")
@@ -165,7 +171,7 @@ def transcribe_with_chunks(objections_dict):
         return chunks
 
 if __name__ == "__main__":
-    objections_file_path = r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet3.csv"
+    objections_file_path = config.get("OBJECTION_DATA_PATH", "objections.csv")
     objections_dict = load_objections(objections_file_path)
     transcribed_chunks = transcribe_with_chunks(objections_dict)
     print("Final transcriptions and sentiments:", transcribed_chunks)