Update app.py

app.py

@@ -1,30 +1,21 @@
-import gradio as gr
-from transformers import pipeline
-import torch
-import os
-import tempfile
-import soundfile as sf
+from fastapi import FastAPI
+from pydantic import BaseModel
 
-# Load the Whisper model once during startup
-device = 0 if torch.cuda.is_available() else -1
-asr_pipeline = pipeline(model="openai/whisper-small", device=device)
+app = FastAPI()
 
-# Function to handle the transcription process
-def transcribe_audio(audio_file_path):
-    # Perform the transcription using the audio file path
-    transcription = asr_pipeline(audio_file_path)
+# Sample request model
+class Item(BaseModel):
+    name: str
+    description: str
+    price: float
+    tax: float
 
-    # Return the transcription result
-    return transcription['text']
+# Basic GET endpoint
+@app.get("/")
+def read_root():
+    return {"message": "Welcome to the FastAPI app on Hugging Face Spaces!"}
 
-# Create Gradio interface
-interface = gr.Interface(
-    fn=transcribe_audio,  # The function to call when audio is uploaded
-    inputs=gr.Audio(type="filepath"),  # Use 'filepath' to get the path to the audio file
-    outputs="text",  # Output type: text (transcription)
-    title="Whisper Audio Transcription",  # Title of the Gradio interface
-    description="Upload an audio file to get a transcription using OpenAI's Whisper model"
-)
-
-# Launch the Gradio interface
-interface.launch()
+# POST endpoint
+@app.post("/items/")
+def create_item(item: Item):
+    return {"item": item}