app.py

from fastapi import FastAPI, UploadFile, File, Form, HTTPException
from fastapi.responses import JSONResponse
import whisper
import tempfile
import os
from transformers import pipeline
import logging
from pathlib import Path

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

# Create cache directory in the workspace
CACHE_DIR = Path("/code/.cache")
CACHE_DIR.mkdir(exist_ok=True)
os.environ["TRANSFORMERS_CACHE"] = str(CACHE_DIR)
os.environ["HF_HOME"] = str(CACHE_DIR)

app = FastAPI(title="TranscriptoCast AI (Demo)")

# Load models once at startup
try:
    logger.info("Loading Whisper model...")
    whisper_model = whisper.load_model("base", download_root=str(CACHE_DIR))
    logger.info("Whisper model loaded successfully")
except Exception as e:
    logger.error(f"Error loading Whisper model: {str(e)}")
    raise HTTPException(status_code=500, detail=f"Failed to load Whisper model: {str(e)}")

try:
    logger.info("Loading summarization model...")
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn", cache_dir=str(CACHE_DIR))
    logger.info("Summarization model loaded successfully")
except Exception as e:
    logger.error(f"Error loading summarization model: {str(e)}")
    raise HTTPException(status_code=500, detail=f"Failed to load summarization model: {str(e)}")

try:
    logger.info("Loading translation model...")
    translator = pipeline("translation", model="facebook/mbart-large-50-many-to-many-mmt", cache_dir=str(CACHE_DIR))
    logger.info("Translation model loaded successfully")
except Exception as e:
    logger.error(f"Error loading translation model: {str(e)}")
    raise HTTPException(status_code=500, detail=f"Failed to load translation model: {str(e)}")

@app.post("/transcribe")
async def transcribe(file: UploadFile = File(...)):
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
        tmp.write(await file.read())
        tmp_path = tmp.name
    result = whisper_model.transcribe(tmp_path)
    os.remove(tmp_path)
    return JSONResponse(content={"text": result["text"]})

@app.post("/summarize")
async def summarize(text: str = Form(...)):
    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
    return JSONResponse(content={"summary": summary[0]["summary_text"]})

@app.post("/translate")
async def translate(text: str = Form(...), src_lang: str = Form("en_XX"), tgt_lang: str = Form("fr_XX")):
    translation = translator(text, src_lang=src_lang, tgt_lang=tgt_lang)
    return JSONResponse(content={"translation": translation[0]["translation_text"]})

@app.get("/")
def root():
    return {"message": "Welcome to TranscriptoCast AI Hugging Face Space!"}