import os
import requests
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import HTMLResponse
from llama_cpp import Llama
from pydantic import BaseModel
import uvicorn
# Configuration
MODEL_URL = "https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-Q5_K_M.gguf"
MODEL_NAME = "DeepSeek-R1-Distill-Qwen-1.5B-Q5_K_M.gguf"
MODEL_DIR = "model"
MODEL_PATH = os.path.join(MODEL_DIR, MODEL_NAME)
# Create model directory if it doesn't exist
os.makedirs(MODEL_DIR, exist_ok=True)
# Download the model if it doesn't exist
if not os.path.exists(MODEL_PATH):
print(f"Downloading model from {MODEL_URL}...")
response = requests.get(MODEL_URL, stream=True)
if response.status_code == 200:
with open(MODEL_PATH, "wb") as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
print("Model downloaded successfully!")
else:
raise RuntimeError(f"Failed to download model: HTTP {response.status_code}")
else:
print("Model already exists. Skipping download.")
# Initialize FastAPI
app = FastAPI(
title="DeepSeek-R1 OpenAI-Compatible API",
description="OpenAI-compatible API for DeepSeek-R1-Distill-Qwen-1.5B",
version="1.0.0",
)
# CORS Configuration
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
# Load the model
print("Loading model...")
try:
llm = Llama(
model_path=MODEL_PATH, n_ctx=2048, n_threads=4, n_gpu_layers=0, verbose=False
)
print("Model loaded successfully!")
except Exception as e:
raise RuntimeError(f"Failed to load model: {str(e)}")
# Root endpoint with documentation
@app.get("/", response_class=HTMLResponse)
async def root():
return f"""
API Documentation
"""
# OpenAI-Compatible Request Schema
class ChatCompletionRequest(BaseModel):
prompt: str
max_tokens: int = 300
# temperature: float = 0.7
# top_p: float = 0.9
# stream: bool = False
# OpenAI-Compatible Response Schema
class ChatCompletionResponse(BaseModel):
model: str = MODEL_NAME
choices: list[dict]
usage: dict
@app.post("/v1/chat/completions")
async def chat_completion(request: ChatCompletionRequest):
try:
prompt = request.prompt
response = llm(
prompt=prompt,
max_tokens=request.max_tokens,
temperature=0.7,
top_p=0.9,
stop=[""],
)
return ChatCompletionResponse(
choices=[
{
"index": 0,
"message": {
"role": "assistant",
"content": response["choices"][0]["text"].strip(),
},
"finish_reason": "stop",
}
],
usage={
"prompt_tokens": len(prompt),
"completion_tokens": len(response["choices"][0]["text"]),
"total_tokens": len(prompt) + len(response["choices"][0]["text"]),
},
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/health")
def health_check():
return {"status": "healthy"}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)