Create app.py

app.py

@@ -0,0 +1,67 @@
+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)}")