mini_server.py

import os
import torch
import uvicorn
from fastapi import FastAPI
from pydantic import BaseModel
from typing import List
from transformers import AutoModelForCausalLM, AutoTokenizer

# CRITICAL: GPU 0 pe host karenge
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

app = FastAPI()

# Tera Merged Model Path
MODEL_PATH = "./qwen-7b-nl2sql-merged"

print("🚀 Loading Local Model for Inference API... (Takes a minute)")
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    attn_implementation="sdpa" # Super stable, no vLLM crashes
)
print("✅ Server Ready! Acting as OpenAI on Port 8000.")

# OpenAI Request Schemas
class Message(BaseModel):
    role: str
    content: str

class ChatRequest(BaseModel):
    model: str
    messages: List[Message]
    temperature: float = 0.2
    max_tokens: int = 512

@app.post("/v1/chat/completions")
async def chat(request: ChatRequest):
    # Convert OpenAI messages to Qwen format
    messages = [{"role": m.role, "content": m.content} for m in request.messages]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    # Generate SQL
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=request.max_tokens,
            temperature=request.temperature,
            do_sample=True if request.temperature > 0 else False,
            pad_token_id=tokenizer.eos_token_id
        )
    
    # Decode only the newly generated text
    response_text = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
    
    # Return EXACT OpenAI JSON Structure
    return {
        "id": "chatcmpl-local-hackathon",
        "object": "chat.completion",
        "created": 1700000000,
        "model": request.model,
        "choices": [{
            "index": 0,
            "message": {"role": "assistant", "content": response_text},
            "finish_reason": "stop"
        }],
        "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
    }

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8001)