Update app

app.py

@@ -1,72 +1,10 @@
-# app.py (safe, use /tmp for cache)
-import os
-import logging
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-import tempfile
-
-# --- Put caches in a writable temp dir to avoid permission errors ---
-TMP_CACHE = os.environ.get("HF_CACHE_DIR", os.path.join(tempfile.gettempdir(), "hf_cache"))
-try:
-    os.makedirs(TMP_CACHE, exist_ok=True)
-except Exception as e:
-    # if even this fails, fall back to tempfile.gettempdir()
-    TMP_CACHE = tempfile.gettempdir()
-
-# export environment vars before importing transformers
-os.environ["TRANSFORMERS_CACHE"] = TMP_CACHE
-os.environ["HF_HOME"] = TMP_CACHE
-os.environ["HF_DATASETS_CACHE"] = TMP_CACHE
-os.environ["HF_METRICS_CACHE"] = TMP_CACHE
-
-app = FastAPI(title="DirectEd LoRA API (safe startup)")
+from fastapi import FastAPI
+app = FastAPI()
 
 @app.get("/health")
 def health():
     return {"ok": True}
 
-class Request(BaseModel):
-    prompt: str
-    max_new_tokens: int = 150
-    temperature: float = 0.7
-
-pipe = None
-
-@app.on_event("startup")
-def load_model():
-    global pipe
-    try:
-        # heavy imports done during startup
-        from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-        from peft import PeftModel
-
-        BASE_MODEL = "unsloth/llama-3-8b-Instruct-bnb-4bit"
-        ADAPTER_REPO = "rayymaxx/DirectEd-AI-LoRA"  # <-- replace with your adapter repo
-
-        tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
-        base_model = AutoModelForCausalLM.from_pretrained(
-            BASE_MODEL,
-            device_map="auto",
-            low_cpu_mem_usage=True,
-            torch_dtype="auto",
-        )
-
-        model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)
-        model.eval()
-
-        pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
-        logging.info("Model and adapter loaded successfully.")
-    except Exception as e:
-        logging.exception("Failed to load model at startup: %s", e)
-        pipe = None
-
-@app.post("/generate")
-def generate(req: Request):
-    if pipe is None:
-        raise HTTPException(status_code=503, detail="Model not loaded. Check logs.")
-    try:
-        out = pipe(req.prompt, max_new_tokens=req.max_new_tokens, temperature=req.temperature, do_sample=True)
-        return {"response": out[0]["generated_text"]}
-    except Exception as e:
-        logging.exception("Generation failed: %s", e)
-        raise HTTPException(status_code=500, detail=str(e))
+@app.get("/")
+def root():
+    return {"Minimal code running"}