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

app = FastAPI()

MODEL_NAME = "Qwen/Qwen2.5-Coder-7B"

# ---- Quantization config (CPU safe) ----
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float32,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4"
)

tokenizer = AutoTokenizer.from_pretrained(
    MODEL_NAME,
    trust_remote_code=True
)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    device_map="cpu",
    quantization_config=bnb_config,
    trust_remote_code=True
)

class Prompt(BaseModel):
    message: str

@app.post("/chat")
def chat(prompt: Prompt):
    inputs = tokenizer(prompt.message, return_tensors="pt")

    outputs = model.generate(
        **inputs,
        max_new_tokens=200,
        temperature=0.7,
        do_sample=True
    )

    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"response": response}