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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
# 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"
) |