app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Optional
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
import logging
import os

# ตั้งค่า logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI()

try:
    model_name = "scb10x/llama-3-typhoon-v1.5-8b-instruct"
    tokenizer = AutoTokenizer.from_pretrained(model_name)

    # ตรวจสอบว่ามี GPU หรือไม่
    if torch.cuda.is_available():
        logger.info("GPU is available. Using CUDA.")
        device = "cuda"
    else:
        logger.info("No GPU found. Using CPU.")
        device = "cpu"

    # กำหนดการตั้งค่าสำหรับการโหลดโมเดล
    model_kwargs = {
        "torch_dtype": torch.float32 if device == "cpu" else torch.float16,
        "low_cpu_mem_usage": True,
    }

    if device == "cuda":
        from transformers import BitsAndBytesConfig
        model_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)

    # โหลดโมเดล
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        device_map="auto" if device == "cuda" else None,
        **model_kwargs
    )

    model.to(device)
    logger.info(f"Model loaded successfully on {device}")
except Exception as e:
    logger.error(f"Error loading model: {str(e)}")
    raise

class Query(BaseModel):
    queryResult: Optional[dict] = None
    queryText: Optional[str] = None

@app.post("/webhook")
async def webhook(query: Query):
    try:
        user_query = query.queryResult.get('queryText') if query.queryResult else query.queryText
        
        if not user_query:
            raise HTTPException(status_code=400, detail="No query text provided")
        
        prompt = f"Human: {user_query}\nAI:"
        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
        
        with torch.no_grad():
            output = model.generate(input_ids, max_new_tokens=100, temperature=0.7)
        response = tokenizer.decode(output[0], skip_special_tokens=True)
        
        ai_response = response.split("AI:")[-1].strip()
        
        return {"fulfillmentText": ai_response}
    except Exception as e:
        logger.error(f"Error in webhook: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)