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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 with correct path resolution"""
global model, INITIALIZATION_STATUS
try:
device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"Initializing model on device: {device}")
# Critical: Use absolute path for model loading
model_path = os.path.abspath(os.path.join('/app/src', 'models'))
logger.info(f"Loading models from: {model_path}")
if not os.path.exists(model_path):
raise RuntimeError(f"Model path {model_path} does not exist")
# Log available model files for debugging
model_files = os.listdir(model_path)
logger.info(f"Available model files: {model_files}")
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",
"initialization_status": INITIALIZATION_STATUS
}
if model is not None:
status.update({
"device": "cpu",
"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))
@app.post("/api/v1/inference")
async def inference(request: AudioRequest) -> AudioResponse:
"""Run inference with enhanced error handling and logging"""
if not INITIALIZATION_STATUS["model_loaded"]:
raise HTTPException(
status_code=503,
detail=f"Model not ready. Status: {INITIALIZATION_STATUS}"
)
try:
# Log input validation
logger.info(f"Received inference request with sample rate: {request.sample_rate}")
# Decode audio
audio_bytes = base64.b64decode(request.audio_data)
audio_array = np.load(io.BytesIO(audio_bytes))
logger.info(f"Decoded audio array shape: {audio_array.shape}, dtype: {audio_array.dtype}")
# Validate input format
if len(audio_array.shape) != 2:
raise ValueError(f"Expected 2D audio array [C,T], got shape {audio_array.shape}")
# Run inference
result = model.inference(audio_array, request.sample_rate)
logger.info(f"Inference complete. Output shape: {result['audio'].shape}")
# Encode output
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)}", exc_info=True)
raise HTTPException(
status_code=500,
detail=str(e)
)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000) |