Delete app

app/main.py

@@ -1,67 +0,0 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-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()
-
-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_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# === Load Base + Adapter ===
-adapter_path = "C:/Users/nimes/Desktop/NLP Projects/Multi-label Email Classifier/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,
-        device_map={"": "cpu"}
-    )
-    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:
-    raise RuntimeError(f"❌ Failed to load model + adapter: {str(e)}")
-
-# === Request Schema ===
-class EmailInput(BaseModel):
-    subject: str
-    body: str
-
-# === Endpoint ===
-@app.post("/classify")
-async def classify_email(data: EmailInput):
-    prompt = f"""### Subject:\n{data.subject}\n\n### Body:\n{data.body}\n\n### Labels:"""
-    try:
-        result = pipe(prompt, max_new_tokens=50, do_sample=True, top_k=50, top_p=0.95)
-        full_text = result[0]["generated_text"]
-        label_section = full_text.split("### Labels:")[1].strip()
-        return {"label": label_section}
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Model inference failed: {str(e)}")