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from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import logging
import gradio as gr
import uvicorn
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
MODEL_ID = "tugstugi/Qwen2.5-Coder-0.5B-QwQ-draft"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID).to(device)
class ChatMessage(BaseModel):
role: str
content: str
class ChatRequest(BaseModel):
messages: list[ChatMessage]
class ChatResponse(BaseModel):
response: str
status: str = "success"
def build_prompt(messages):
prompt = ""
for message in messages:
if message["role"] == "user":
prompt += f"<|im_start|>user\n{message['content']}<|im_end|>\n"
elif message["role"] == "assistant":
prompt += f"<|im_start|>assistant\n{message['content']}<|im_end|>\n"
prompt += "<|im_start|>assistant\n"
return prompt
def generate_response(conversation_history, max_new_tokens=1500):
prompt_text = build_prompt(conversation_history)
inputs = tokenizer(prompt_text, return_tensors="pt").to(device)
generated_ids = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=0.8,
top_p=0.95,
pad_token_id=tokenizer.eos_token_id
)
generated_text = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
return generated_text.strip()
@app.post("/api/chat", response_model=ChatResponse)
async def chat_endpoint(request: ChatRequest):
try:
conversation = [{"role": msg.role, "content": msg.content} for msg in request.messages]
response_text = generate_response(conversation)
return ChatResponse(response=response_text)
except Exception as e:
logger.error(f"Error: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/health")
async def health_check():
return {"status": "healthy"}
# Gradio setup
iface = gr.Interface(fn=lambda input: generate_response([{"role": "user", "content": input}]),
inputs="text", outputs="text")
app = gr.mount_gradio_app(app, iface, path="/")
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
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