Update app.py

app.py

@@ -1,16 +1,32 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 import os
 
-model_name = "meta-llama/Llama-4-11B-Instruct"
+app = FastAPI()
 
-# use the name of your secret, here "test"
-token = os.getenv("test")
+# Model and token
+model_name = "meta-llama/Llama-4-11B-Instruct"
+token = os.getenv("test")  # use the secret you named in HF Space
 
-tokenizer = AutoTokenizer.from_pretrained(model_name, token=token)
+# Load tokenizer + model
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=token)
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
     torch_dtype=torch.float16,
     device_map="auto",
-    token=token
+    use_auth_token=token
 )
+
+# Input schema
+class InputText(BaseModel):
+    text: str
+
+# API endpoint
+@app.post("/predict")
+def predict(item: InputText):
+    inputs = tokenizer(item.text, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=200)
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return {"result": result}