Update app.py

app.py

@@ -4,35 +4,30 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 from peft import PeftModel, PeftConfig
 from fastapi.middleware.cors import CORSMiddleware
 import torch
-from huggingface_hub import login
 from dotenv import load_dotenv
 import os
 
-load_dotenv()
 
+load_dotenv()
 hf_token = os.getenv("HF_TOKEN")
 
-login(token=hf_token)
 
 app = FastAPI()
 
-# Allow frontend communication
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["http://localhost:3000"],
+    allow_origins=["*"], 
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
-# === Load Base + Adapter ===
-adapter_path = "C:/Users/nimes/Desktop/NLP Projects/Multi-label Email Classifier/checkpoint-711"
+adapter_path = "./checkpoint-711"  
 
 try:
-    # Load PEFT config to get base model path
     peft_config = PeftConfig.from_pretrained(adapter_path)
-    
-    # Load base model and tokenizer (CPU-safe)
+
+  
     base_model = AutoModelForCausalLM.from_pretrained(
         peft_config.base_model_name_or_path,
         torch_dtype=torch.float32,
@@ -40,10 +35,9 @@ try:
     )
     tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)
 
-    # Load LoRA adapter
+
     model = PeftModel.from_pretrained(base_model, adapter_path, device_map={"": "cpu"})
 
-    # Build inference pipeline
     pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
 except Exception as e: