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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
import logging
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# FastAPI app
app = FastAPI()
# CORS Middleware (for frontend access)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Update to specific frontend URL in production
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"
try:
logger.info(f"Loading model {model_name}...")
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
logger.info("Model loaded successfully.")
except Exception as e:
logger.error(f"Failed to load model: {e}")
raise
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
async def stream_tokens():
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
):
token_id = output.sequences[0][-1]
token_text = tokenizer.decode([token_id], skip_special_tokens=True)
if token_text:
yield token_text
await asyncio.sleep(0.01) # Control streaming speed
logger.info("Streaming completed.")
return stream_tokens()
except Exception as e:
logger.error(f"Error during generation: {e}")
yield f"Error occurred: {e}"
@app.post("/ask")
async def ask(question: Question):
logger.info(f"Received question: {question.question}")
return StreamingResponse(
generate_response_chunks(question.question),
media_type="text/plain"
)
@app.get("/")
async def root():
return {"message": "Orion AI Chat API is running!"}
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