gemma_backend_service.py

"""
FastAPI Backend AI Service using Gemma-3n-E4B-it-GGUF
Provides OpenAI-compatible chat completion endpoints powered by llama-cpp-python
"""

import os
import warnings
import logging
import time
from contextlib import asynccontextmanager
from typing import List, Dict, Any, Optional, Union

from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import StreamingResponse, JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field, field_validator

# Import llama-cpp-python for GGUF model support
try:
    from llama_cpp import Llama
    llama_cpp_available = True
except ImportError:
    llama_cpp_available = False

import uvicorn

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

# Pydantic models for OpenAI-compatible API (same as original)
class ChatMessage(BaseModel):
    role: str = Field(..., description="The role of the message author")
    content: str = Field(..., description="The content of the message")
    
    @field_validator('role')
    @classmethod
    def validate_role(cls, v: str) -> str:
        if v not in ["system", "user", "assistant"]:
            raise ValueError("Role must be one of: system, user, assistant")
        return v

class ChatCompletionRequest(BaseModel):
    model: str = Field(default="gemma-3n-e4b-it", description="The model to use for completion")
    messages: List[ChatMessage] = Field(..., description="List of messages in the conversation")
    max_tokens: Optional[int] = Field(default=512, ge=1, le=2048, description="Maximum tokens to generate")
    temperature: Optional[float] = Field(default=1.0, ge=0.0, le=2.0, description="Sampling temperature")
    top_p: Optional[float] = Field(default=0.95, ge=0.0, le=1.0, description="Top-p sampling")
    top_k: Optional[int] = Field(default=64, ge=1, le=100, description="Top-k sampling")
    stream: Optional[bool] = Field(default=False, description="Whether to stream responses")

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

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

class HealthResponse(BaseModel):
    status: str
    model: str
    version: str

class ModelInfo(BaseModel):
    id: str
    object: str = "model"
    created: int
    owned_by: str = "huggingface"

class ModelsResponse(BaseModel):
    object: str = "list"
    data: List[ModelInfo]

# Global variables for model management
current_model = os.environ.get("AI_MODEL", "unsloth/gemma-3n-E4B-it-GGUF")
llm = None

def create_gemma_chat_template():
    """
    Create a custom chat template for Gemma 3n
    Based on the format: <bos><start_of_turn>user\n{user_message}<end_of_turn>\n<start_of_turn>model\n{assistant_response}<end_of_turn>
    """
    return """<bos>{% for message in messages %}{% if message['role'] == 'user' %}<start_of_turn>user
{{ message['content'] }}<end_of_turn>
{% elif message['role'] == 'assistant' %}<start_of_turn>model
{{ message['content'] }}<end_of_turn>
{% elif message['role'] == 'system' %}<start_of_turn>system
{{ message['content'] }}<end_of_turn>
{% endif %}{% endfor %}<start_of_turn>model
"""

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan manager for startup and shutdown events"""
    global llm
    logger.info("🚀 Starting Gemma 3n Backend Service...")
    
    if not llama_cpp_available:
        logger.error("❌ llama-cpp-python is not available. Please install with: pip install llama-cpp-python")
        raise RuntimeError("llama-cpp-python not available")
    
    try:
        logger.info(f"📥 Loading Gemma 3n model from {current_model}...")
        
        # Configure model parameters for Gemma 3n
        # Using recommended settings from Gemma 3n documentation
        llm = Llama.from_pretrained(
            repo_id=current_model,
            filename="*q4_k_m.gguf",  # Start with Q4_K_M for good balance
            verbose=True,
            # Gemma 3n specific settings
            n_ctx=4096,  # Start with 4K context, can be increased to 32K
            n_threads=4,  # Adjust based on your CPU
            n_gpu_layers=-1,  # Use all GPU layers if CUDA available
            # Chat template for Gemma 3n format
            chat_format="gemma",  # Try built-in gemma format first
        )
        
        logger.info("✅ Successfully loaded Gemma 3n model")
        
    except Exception as e:
        logger.error(f"❌ Failed to initialize Gemma 3n model: {e}")
        # Fallback to try without from_pretrained
        try:
            logger.info("🔄 Trying alternative model loading approach...")
            # You might need to download the model file locally first
            logger.warning("⚠️ Please download the GGUF model file locally and update the path")
            logger.warning("⚠️ You can download from: https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF")
            
            # For now, we'll raise an error with instructions
            raise RuntimeError(
                "Model loading failed. Please download the GGUF model locally:\n"
                "1. Visit: https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF\n"
                "2. Download a GGUF file (recommended: gemma-3n-e4b-it-q4_k_m.gguf)\n"
                "3. Update the model path in the code"
            )
            
        except Exception as fallback_error:
            logger.error(f"❌ Fallback loading also failed: {fallback_error}")
            raise RuntimeError(f"Service initialization failed: {fallback_error}")
    
    yield
    logger.info("🔄 Shutting down Gemma 3n Backend Service...")
    if llm:
        # Clean up model resources
        llm = None

# Initialize FastAPI app
app = FastAPI(
    title="Gemma 3n Backend Service",
    description="OpenAI-compatible chat completion API powered by Gemma-3n-E4B-it-GGUF",
    version="1.0.0",
    lifespan=lifespan
)

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

def ensure_model_ready():
    """Check if model is loaded and ready"""
    if llm is None:
        raise HTTPException(status_code=503, detail="Service not ready - Gemma 3n model not initialized")

def convert_messages_to_prompt(messages: List[ChatMessage]) -> str:
    """Convert OpenAI messages format to Gemma 3n chat format"""
    # Gemma 3n uses specific format with <start_of_turn> and <end_of_turn>
    prompt_parts = ["<bos>"]
    
    for message in messages:
        role = message.role
        content = message.content
        
        if role == "system":
            prompt_parts.append(f"<start_of_turn>system\n{content}<end_of_turn>")
        elif role == "user":
            prompt_parts.append(f"<start_of_turn>user\n{content}<end_of_turn>")
        elif role == "assistant":
            prompt_parts.append(f"<start_of_turn>model\n{content}<end_of_turn>")
    
    # Add the start for model response
    prompt_parts.append("<start_of_turn>model\n")
    
    return "\n".join(prompt_parts)

async def generate_response_gemma(
    messages: List[ChatMessage], 
    max_tokens: int = 512, 
    temperature: float = 1.0,
    top_p: float = 0.95,
    top_k: int = 64
) -> str:
    """Generate response using Gemma 3n model"""
    ensure_model_ready()
    
    try:
        # Use the chat completion method if available
        if hasattr(llm, 'create_chat_completion'):
            # Convert to dict format for llama-cpp-python
            messages_dict = [{"role": msg.role, "content": msg.content} for msg in messages]
            
            response = llm.create_chat_completion(
                messages=messages_dict,
                max_tokens=max_tokens,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                stop=["<end_of_turn>", "<eos>"]  # Gemma 3n stop tokens
            )
            
            return response['choices'][0]['message']['content'].strip()
        
        else:
            # Fallback to direct prompt completion
            prompt = convert_messages_to_prompt(messages)
            
            response = llm(
                prompt,
                max_tokens=max_tokens,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                stop=["<end_of_turn>", "<eos>"],
                echo=False
            )
            
            return response['choices'][0]['text'].strip()
            
    except Exception as e:
        logger.error(f"Generation failed: {e}")
        return "I apologize, but I'm having trouble generating a response right now. Please try again."

@app.get("/", response_class=JSONResponse)
async def root() -> Dict[str, Any]:
    """Root endpoint with service information"""
    return {
        "message": "Gemma 3n Backend Service is running!",
        "model": current_model,
        "version": "1.0.0",
        "endpoints": {
            "health": "/health",
            "models": "/v1/models",
            "chat_completions": "/v1/chat/completions"
        }
    }

@app.get("/health", response_model=HealthResponse)
async def health_check():
    """Health check endpoint"""
    return HealthResponse(
        status="healthy" if (llm is not None) else "unhealthy",
        model=current_model,
        version="1.0.0"
    )

@app.get("/v1/models", response_model=ModelsResponse)
async def list_models():
    """List available models (OpenAI-compatible)"""
    models = [
        ModelInfo(
            id="gemma-3n-e4b-it",
            created=int(time.time()),
            owned_by="google"
        )
    ]
    
    return ModelsResponse(data=models)

@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
async def create_chat_completion(
    request: ChatCompletionRequest
) -> ChatCompletionResponse:
    """Create a chat completion (OpenAI-compatible) using Gemma 3n"""
    try:
        if not request.messages:
            raise HTTPException(status_code=400, detail="Messages cannot be empty")
        
        logger.info(f"Generating Gemma 3n response for {len(request.messages)} messages")
        
        response_text = await generate_response_gemma(
            request.messages,
            request.max_tokens or 512,
            request.temperature or 1.0,
            request.top_p or 0.95,
            request.top_k or 64
        )
        
        response_text = response_text.strip() if response_text else "No response generated."
        
        return ChatCompletionResponse(
            id=f"chatcmpl-{int(time.time())}",
            created=int(time.time()),
            model=request.model,
            choices=[ChatCompletionChoice(
                index=0,
                message=ChatMessage(role="assistant", content=response_text),
                finish_reason="stop"
            )]
        )
        
    except Exception as e:
        logger.error(f"Error in chat completion: {e}")
        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")

# Main entry point
if __name__ == "__main__":
    import uvicorn
    uvicorn.run("gemma_backend_service:app", host="0.0.0.0", port=8000, reload=True)