aiml/03_training/experiments/migrated_experiments/serve.py

#!/usr/bin/env python3
"""
OpenAI-compatible API server for Elizabeth model.
Provides /v1/chat/completions endpoint with full compatibility.
"""

import os
import time
import json
import logging
from typing import List, Dict, Any

from fastapi import FastAPI, HTTPException, Depends, Header
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel, Field
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration
MODEL_PATH = "/home/x/adaptai/experiments/qwen3-8b-elizabeth-simple/"
PORT = 8000
HOST = "0.0.0.0"
API_KEY = os.getenv("API_KEY", "elizabeth-secret-key-2025")

# Pydantic models for OpenAI compatibility
class ChatMessage(BaseModel):
    role: str = Field(..., description="Role of the message sender")
    content: str = Field(..., description="Content of the message")

class ChatCompletionRequest(BaseModel):
    model: str = Field(..., description="Model to use for completion")
    messages: List[ChatMessage] = Field(..., description="List of messages")
    temperature: float = Field(0.7, ge=0.0, le=2.0, description="Sampling temperature")
    max_tokens: int = Field(1024, ge=1, le=4096, description="Maximum tokens to generate")
    stream: bool = Field(False, description="Whether to stream the response")
    top_p: float = Field(1.0, ge=0.0, le=1.0, description="Nucleus sampling parameter")

class ChatCompletionChoice(BaseModel):
    index: int
    message: ChatMessage
    finish_reason: str

class ChatCompletionUsage(BaseModel):
    prompt_tokens: int
    completion_tokens: int
    total_tokens: int

class ChatCompletionResponse(BaseModel):
    id: str
    object: str = "chat.completion"
    created: int
    model: str
    choices: List[ChatCompletionChoice]
    usage: ChatCompletionUsage

# FastAPI app
app = FastAPI(title="Elizabeth API", version="1.0.0")

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Global model and tokenizer
model = None
tokenizer = None

def load_model():
    """Load the model and tokenizer."""
    global model, tokenizer
    
    logger.info("Loading model and tokenizer...")
    start_time = time.time()
    
    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_PATH,
        torch_dtype=torch.bfloat16,
        device_map="auto",
        trust_remote_code=True
    )
    
    load_time = time.time() - start_time
    logger.info(f"Model loaded in {load_time:.2f} seconds")
    logger.info(f"Model device: {model.device}")
    logger.info(f"Model dtype: {model.dtype}")

def authenticate_token(authorization: str = Header(None)):
    """Authenticate API requests."""
    if authorization is None:
        raise HTTPException(status_code=401, detail="Authorization header required")
    
    if not authorization.startswith("Bearer "):
        raise HTTPException(status_code=401, detail="Invalid authorization format")
    
    token = authorization[7:]
    if token != API_KEY:
        raise HTTPException(status_code=401, detail="Invalid API key")
    
    return token

@app.on_event("startup")
async def startup_event():
    """Load model on startup."""
    load_model()

@app.get("/health")
async def health_check():
    """Health check endpoint."""
    return {
        "status": "healthy",
        "model_loaded": model is not None,
        "model_device": str(model.device) if model else None,
        "timestamp": time.time()
    }

@app.get("/metrics")
async def metrics():
    """Prometheus metrics endpoint."""
    # TODO: Add actual metrics collection
    return {
        "requests_processed": 0,
        "average_latency": 0.0,
        "error_rate": 0.0
    }

@app.post("/v1/chat/completions")
async def chat_completions(
    request: ChatCompletionRequest,
    token: str = Depends(authenticate_token)
):
    """OpenAI-compatible chat completions endpoint."""
    
    if model is None or tokenizer is None:
        raise HTTPException(status_code=503, detail="Model not loaded")
    
    try:
        # Prepare prompt from messages
        prompt = ""
        for msg in request.messages:
            prompt += f"{msg.role}: {msg.content}\n"
        prompt += "Assistant:"
        
        # Tokenize
        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
        
        # Generate
        start_time = time.time()
        
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_new_tokens=request.max_tokens,
                do_sample=True,
                temperature=request.temperature,
                top_p=request.top_p,
                pad_token_id=tokenizer.eos_token_id
            )
        
        generation_time = time.time() - start_time
        
        # Decode
        full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        assistant_response = full_response[len(prompt):].strip()
        
        # Calculate tokens
        prompt_tokens = len(inputs.input_ids[0])
        completion_tokens = len(outputs[0]) - prompt_tokens
        
        # Prepare response
        response = ChatCompletionResponse(
            id=f"chatcmpl-{int(time.time())}",
            created=int(time.time()),
            model=request.model,
            choices=[
                ChatCompletionChoice(
                    index=0,
                    message=ChatMessage(
                        role="assistant",
                        content=assistant_response
                    ),
                    finish_reason="stop"
                )
            ],
            usage=ChatCompletionUsage(
                prompt_tokens=prompt_tokens,
                completion_tokens=completion_tokens,
                total_tokens=prompt_tokens + completion_tokens
            )
        )
        
        logger.info(f"Generated response in {generation_time:.2f}s, tokens: {completion_tokens}")
        
        return response
        
    except Exception as e:
        logger.error(f"Error generating response: {e}")
        raise HTTPException(status_code=500, detail=f"Generation error: {str(e)}")

@app.get("/")
async def root():
    """Root endpoint with information."""
    return {
        "message": "Elizabeth API Server",
        "version": "1.0.0",
        "model": "qwen3-8b-elizabeth-simple",
        "endpoints": {
            "chat": "/v1/chat/completions",
            "health": "/health",
            "metrics": "/metrics"
        }
    }

if __name__ == "__main__":
    import uvicorn
    
    logger.info(f"Starting Elizabeth API server on {HOST}:{PORT}")
    uvicorn.run(app, host=HOST, port=PORT)