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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)}")
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