app.py

from fastapi import FastAPI
from pydantic import BaseModel
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

app = FastAPI()

MODEL_NAME = "BlackGoku7/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    device_map="auto",
    torch_dtype=torch.bfloat16,  # Or torch.float16 if your Space supports it
    trust_remote_code=True
)
model.eval()

class Prompt(BaseModel):
    text: str
    max_new_tokens: int = 200

@app.get("/")
def root():
    return {"message": "POST to /generate with {'text': 'your prompt'}"}

@app.post("/generate")
def generate(prompt: Prompt):
    inputs = tokenizer(prompt.text, return_tensors="pt").to(model.device)
    output = model.generate(
        **inputs,
        max_new_tokens=prompt.max_new_tokens,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
    )
    decoded = tokenizer.decode(output[0], skip_special_tokens=True)
    return {"response": decoded}