from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Optional, Dict, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, TextStreamer
import torch
import os

app = FastAPI()

# Define the request schema
class PromptRequest(BaseModel):
    prompt: str
    history: Optional[List[Dict[str, Any]]] = None
    parameters: Optional[Dict[str, Any]] = None

@app.on_event("startup")
def load_model():
    global model, tokenizer, pipe
    os.environ["TRANSFORMERS_CACHE"] = "./cache"
    model_path = "model/models--meta-llama--Llama-3.2-3B-Instruct/snapshots/0cb88a4f764b7a12671c53f0838cd831a0843b95"
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True)
    model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, cache_dir="./cache")
    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, streamer=streamer)

@app.post("/generate/")
async def generate_response(request: PromptRequest):
    # Format the prompt with message history
    history_text = ""
    if request.history:
        for message in request.history:
            role = message.get("role", "user")
            content = message.get("content", "")
            history_text += f"{role}: {content}\n"
    
    # Combine history with the current prompt
    full_prompt = f"{history_text}\nUser: {request.prompt}\nAssistant:"

    # Set default parameters and update with any provided
    gen_params = {
        "max_new_tokens": 256,
        "temperature": 0.7,
        "top_p": 0.9,
    }
    if request.parameters:
        gen_params.update(request.parameters)

    # Generate the response
    try:
        result = pipe(full_prompt, **gen_params)
        return {"response": result[0]["generated_text"]}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))