Update app.py

app.py

@@ -10,7 +10,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequen
 sent = "dnzblgn/Sentiment-Analysis-Customer-Reviews"
 sarc = "dnzblgn/Sarcasm-Detection-Customer-Reviews"
 doc = "dnzblgn/Customer-Reviews-Classification"
-embedding_model = SentenceTransformer('all-MiniLM-L6-v2')  # Lightweight embedding model for CPU
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
 
 # Your models (no token, no fast tokenizer)
 sentiment_tokenizer = AutoTokenizer.from_pretrained("dnzblgn/Sentiment-Analysis-Customer-Reviews", use_fast=False)
@@ -22,9 +22,10 @@ sarcasm_model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Sarc
 classification_tokenizer = AutoTokenizer.from_pretrained("dnzblgn/Customer-Reviews-Classification", use_fast=False)
 classification_model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Customer-Reviews-Classification")
 
-# Lightweight Causal Language Model (distilgpt2 instead of Mistral)
-causal_tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
-causal_model = AutoModelForCausalLM.from_pretrained("distilgpt2").eval()  # Ensure evaluation mode
+# ** Mistral model for RAG **
+mistral_model_name = "mistralai/Mistral-7B-v0.1"
+causal_tokenizer = AutoTokenizer.from_pretrained(mistral_model_name)
+causal_model = AutoModelForCausalLM.from_pretrained(mistral_model_name, torch_dtype=torch.float16).eval()
 
 # Paths and files
 UPLOAD_FOLDER = "uploads"