Create app.py

app.py

@@ -0,0 +1,28 @@
+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+app = FastAPI()
+
+# Load model and tokenizer once at startup
+model_name = "gpt2"  # change this to your own model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+class PromptRequest(BaseModel):
+    prompt: str
+    max_new_tokens: int = 50
+
+@app.post("/generate")
+async def generate_text(req: PromptRequest):
+    inputs = tokenizer(req.prompt, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=req.max_new_tokens,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.95,
+    )
+    generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return {"generated_text": generated}