app.py

#import gradio as gr
#gr.Interface.load("models/pyannote/speaker-diarization").launch()

from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
#from pyannote.audio import Pipeline
 
from transformers import pipeline # le framework de huggingface

app = FastAPI()

#pipe_flan = pipeline("text2text-generation", model="google/flan-t5-small")
classifier = pipeline("automatic-speech-recognition")# la liste des pipelines de huggingface est disponible ici :https://huggingface.co/docs/transformers/quicktour
@app.get("/")
def t5():
    
    #output = pipe_flan(input)
    #pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization")
    #pipeline("file.wav")
    return {"output":"OK"}

#app.mount("/", StaticFiles(directory="static", html=True), name="static")

# @app.get("/")
#def index() -> FileResponse:
#    return FileResponse(path="/app/static/index.html", media_type="text/html")