Update app.py

app.py

@@ -4,7 +4,7 @@ import faiss
 import os
 import numpy as np
 from sentence_transformers import SentenceTransformer
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForCausalLM  # Added import
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification
 
 # Model paths
 sent = "dnzblgn/Sentiment-Analysis-Customer-Reviews"
@@ -12,17 +12,23 @@ sarc = "dnzblgn/Sarcasm-Detection-Customer-Reviews"
 doc = "dnzblgn/Customer-Reviews-Classification"
 embedding_model = SentenceTransformer('multi-qa-mpnet-base-dot-v1')
 
+
 # Load sentiment, sarcasm, and classification models
-sentiment_tokenizer = AutoTokenizer.from_pretrained(sent,use_fast=False)
-sentiment_model = AutoModelForSequenceClassification.from_pretrained(sent)
-sarcasm_tokenizer = AutoTokenizer.from_pretrained(sarc,use_fast=False)
-sarcasm_model = AutoModelForSequenceClassification.from_pretrained(sarc)
-classification_tokenizer = AutoTokenizer.from_pretrained(doc,use_fast=False)
-classification_model = AutoModelForSequenceClassification.from_pretrained(doc)
-
-# Load Mistral LLM for conversational answers
-mistral_tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
-mistral_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16).eval()
+sentiment_tokenizer = AutoTokenizer.from_pretrained(sent, use_fast=False, use_auth_token=HF_TOKEN)
+sentiment_model = AutoModelForSequenceClassification.from_pretrained(sent, use_auth_token=HF_TOKEN)
+sarcasm_tokenizer = AutoTokenizer.from_pretrained(sarc, use_fast=False, use_auth_token=HF_TOKEN)
+sarcasm_model = AutoModelForSequenceClassification.from_pretrained(sarc, use_auth_token=HF_TOKEN)
+classification_tokenizer = AutoTokenizer.from_pretrained(doc, use_fast=False, use_auth_token=HF_TOKEN)
+classification_model = AutoModelForSequenceClassification.from_pretrained(doc, use_auth_token=HF_TOKEN)
+
+import os
+HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")  # Read token from the environment variable
+
+# Use the token in the model loading
+mistral_tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_auth_token=HF_TOKEN)
+mistral_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16, use_auth_token=HF_TOKEN)
+
+
 
 # Paths and files
 UPLOAD_FOLDER = "uploads"