from fastapi import FastAPI, Request
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import asyncio

# FastAPI app
app = FastAPI()

# CORS Middleware (so JS from browser can access it)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Change "*" to your frontend URL for better security
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Request body model
class Question(BaseModel):
    question: str

# Load the model and tokenizer
model_name = "Qwen/Qwen2.5-7B-Instruct"  # Use Qwen2.5-7B-Instruct (adjust for VL if needed)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,  # Use float16 for GPU memory efficiency
    device_map="auto",  # Automatically map to GPU/CPU
    trust_remote_code=True
)

async def generate_response_chunks(prompt: str):
    try:
        # Prepare the input prompt
        messages = [
            {"role": "system", "content": "You are Orion AI assistant created by Abdullah Ali, who is very intelligent, 13 years old, and lives in Lahore."},
            {"role": "user", "content": prompt}
        ]
        inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)

        # Asynchronous generator to yield tokens as they are generated
        async def stream_tokens():
            # Generate tokens one by one
            for output in model.generate(
                inputs,
                max_new_tokens=512,
                temperature=0.7,
                top_p=0.9,
                do_sample=True,
                pad_token_id=tokenizer.eos_token_id,
                return_dict_in_generate=True,
                output_scores=False,
                streaming=True  # Enable streaming in model.generate (if supported)
            ):
                # Decode the latest token
                token_id = output.sequences[0][-1]  # Get the last generated token
                token_text = tokenizer.decode([token_id], skip_special_tokens=True)
                if token_text:
                    yield token_text
                    await asyncio.sleep(0.01)  # Small delay to control streaming speed
                else:
                    # Handle special tokens or empty outputs
                    continue

        return stream_tokens()

    except Exception as e:
        yield f"Error occurred: {e}"

@app.post("/ask")
async def ask(question: Question):
    return StreamingResponse(
        generate_response_chunks(question.question),
        media_type="text/plain"
    )