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from fastapi import FastAPI, HTTPException
import numpy as np
import torch
from pydantic import BaseModel
import base64
import io
import os
import logging
from pathlib import Path
from inference import InferenceRecipe
from fastapi.middleware.cors import CORSMiddleware
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
app = FastAPI()
# Add CORS middleware
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class AudioRequest(BaseModel):
audio_data: str
sample_rate: int
class AudioResponse(BaseModel):
audio_data: str
text: str = ""
# Model initialization status
INITIALIZATION_STATUS = {
"model_loaded": False,
"error": None
}
# Global model instance
model = None
def initialize_model():
"""Initialize the model from mounted directory"""
global model, INITIALIZATION_STATUS
try:
device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"Initializing model on device: {device}")
model_path = os.getenv("MODEL_PATH", "/app/models")
if not os.path.exists(model_path):
raise RuntimeError(f"Model path {model_path} does not exist")
model = InferenceRecipe(model_path, device=device)
INITIALIZATION_STATUS["model_loaded"] = True
logger.info("Model initialized successfully")
return True
except Exception as e:
INITIALIZATION_STATUS["error"] = str(e)
logger.error(f"Failed to initialize model: {e}")
return False
@app.on_event("startup")
async def startup_event():
"""Initialize model on startup"""
initialize_model()
@app.get("/api/v1/health")
def health_check():
"""Health check endpoint"""
status = {
"status": "healthy" if INITIALIZATION_STATUS["model_loaded"] else "initializing",
"gpu_available": torch.cuda.is_available(),
"initialization_status": INITIALIZATION_STATUS
}
if model is not None:
status.update({
"device": str(model.device),
"model_path": str(model.model_path),
"mimi_loaded": model.mimi is not None,
"tokenizer_loaded": model.text_tokenizer is not None,
"lm_loaded": model.lm_gen is not None
})
return status
@app.post("/api/v1/inference")
async def inference(request: AudioRequest) -> AudioResponse:
"""Run inference on audio input"""
if not INITIALIZATION_STATUS["model_loaded"]:
raise HTTPException(
status_code=503,
detail=f"Model not ready. Status: {INITIALIZATION_STATUS}"
)
try:
# Decode audio from base64
audio_bytes = base64.b64decode(request.audio_data)
audio_array = np.load(io.BytesIO(audio_bytes))
# Run inference
result = model.inference(audio_array, request.sample_rate)
# Encode output audio
buffer = io.BytesIO()
np.save(buffer, result['audio'])
audio_b64 = base64.b64encode(buffer.getvalue()).decode()
return AudioResponse(
audio_data=audio_b64,
text=result.get("text", "")
)
except Exception as e:
logger.error(f"Inference failed: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000) |