Update app.py

app.py

@@ -1,12 +1,10 @@
-import chromadb
 from chromadb.config import Settings
-from chromadb import Client
+from chromadb import PersistentClient
 from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification, pipeline
 import pandas as pd
 import numpy as np
 import streamlit as st
 import speech_recognition as sr
-from textblob import TextBlob
 from google.oauth2.service_account import Credentials
 from googleapiclient.discovery import build
 import torch
@@ -24,7 +22,32 @@ SERVICE_ACCOUNT_FILE = r"C:\Users\bhagy\AI\credentials.json"
 
 csv_file_path = r"C:\Users\bhagy\OneDrive\Desktop\INFOSYS PROJECT\900_products_dataset.csv"
 
-# sentiment_pipeline = pipeline("sentiment-analysis")
+
+class CustomEmbeddingFunction:
+    def __init__(self, model_name="sentence-transformers/all-MiniLM-L6-v2"):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModel.from_pretrained(model_name)
+
+    def __call__(self, text):
+        inputs = self.tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        embeddings = outputs.last_hidden_state.mean(dim=1).squeeze().numpy()
+        return embeddings
+
+
+sentiment_pipeline = pipeline("sentiment-analysis")
+persist_directory = "chromadb_storage"
+chroma_client = PersistentClient(path=persist_directory)
+
+collection_name = "crm_data"
+
+try:
+    collection = chroma_client.get_collection(name=collection_name)
+except Exception:
+    collection = chroma_client.create_collection(name=collection_name)
+embedding_fn = CustomEmbeddingFunction()
+
 def get_google_sheets_service():
     creds = Credentials.from_service_account_file(
         SERVICE_ACCOUNT_FILE,
@@ -60,8 +83,11 @@ def update_google_sheet(transcribed_text, sentiment,objection, recommendations,o
         st.error(f"Failed to update Google Sheets: {e}")
 
 load_dotenv()
-huggingface_api_key= os.getenv("HUGGINGFACE_TOKEN")
-login(token=huggingface_api_key)
+hf_token= os.getenv("HUGGINGFACE_TOKEN")
+login(token=hf_token)
+if not hf_token:
+    raise ValueError("Hugging Face API key not found! Please set the HUGGINGFACE_TOKEN variable.")
+print(f"API Key Loaded: {hf_token[:5]}****")
 
 model_name = "tabularisai/multilingual-sentiment-analysis"
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
@@ -80,7 +106,7 @@ def analyze_sentiment(text):
         
         print(f"Sentiment Analysis Result: {result}")
         
-        # Map raw labels to sentiments
+        
         sentiment_map = {
             'Very Negative': "NEGATIVE",
             'Negative': "NEGATIVE",
@@ -153,27 +179,7 @@ def query_crm_data_with_context(prompt, top_k=3):
         return ["Error in querying recommendations."]
 
 
-class CustomEmbeddingFunction:
-    def __init__(self, model_name="sentence-transformers/all-MiniLM-L6-v2"):
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-        self.model = AutoModel.from_pretrained(model_name)
-
-    def __call__(self, text):
-        inputs = self.tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
-        with torch.no_grad():
-            outputs = self.model(**inputs)
-        embeddings = outputs.last_hidden_state.mean(dim=1).squeeze().numpy()
-        return embeddings
-
-chroma_client = Client(Settings(persist_directory="chromadb_storage"))
-embedding_fn = CustomEmbeddingFunction()
-collection_name = "crm_data"
 
-try:
-    collection = chroma_client.get_collection(collection_name)
-except Exception:
-    collection = chroma_client.create_collection(collection_name)
-    
 sentence_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
 faiss_index = faiss.IndexFlatL2(384)
 
@@ -514,7 +520,7 @@ def generate_post_call_summary(sentiment_history, recommendations=[]):
 
     st.markdown("### **Future Insights**")
     
-    
+   
     if overall_sentiment == "Negative":
         st.write("Consider addressing customer pain points more directly. More empathy might improve the sentiment.")
     elif overall_sentiment == "Positive":
@@ -535,6 +541,8 @@ def generate_post_call_summary(sentiment_history, recommendations=[]):
 
 # Main
 def main():
+    
+    st.set_page_config(page_title="RealTime AI-Powered Sales Assistant", layout="wide")
     st.title("🤖 RealTime AI-Powered Sales Assistant For Enhanced Conversation")
     st.markdown(
         "An intelligent assistant to analyze speech, handle objections, and recommend products in real-time."
@@ -609,8 +617,7 @@ def main():
                 st.markdown(f"{idx}. {feedback_entry}")
         else:
             st.warning("No feedback submitted yet.")
-        
-
+    
     file_path = csv_file_path  
     data = load_csv(file_path)