MOdifying with minimal app

app..py

@@ -1,42 +1,7 @@
+# app.py (temporary test)
 from fastapi import FastAPI
-from pydantic import BaseModel
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from peft import PeftModel
-import torch
-
 app = FastAPI()
 
-# 👇 Replace with your actual repos
-BASE_MODEL = "unsloth/llama-3-8b-Instruct-bnb-4bit"
-ADAPTER_REPO = "rayymaxx/DirectEd-AI-LoRA"  # your adapter repo
-
-# Load tokenizer
-tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
-
-# Load base model
-model = AutoModelForCausalLM.from_pretrained(
-    BASE_MODEL,
-    device_map="auto",
-    torch_dtype=torch.float16,
-)
-
-# Load adapter
-model = PeftModel.from_pretrained(model, ADAPTER_REPO)
-
-# Pipeline
-pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
-
-class Request(BaseModel):
-    prompt: str
-    max_new_tokens: int = 150
-    temperature: float = 0.7
-
-@app.post("/generate")
-def generate(req: Request):
-    output = pipe(
-        req.prompt,
-        max_new_tokens=req.max_new_tokens,
-        temperature=req.temperature,
-        do_sample=True
-    )
-    return {"response": output[0]["generated_text"]}
+@app.get("/")
+def root():
+    return {"status": "ok", "message": "minimal app works"}