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# app.py
import os
import tempfile
import requests
from fastapi import FastAPI, UploadFile, File, HTTPException
from fastapi.responses import JSONResponse, HTMLResponse
from pydantic import BaseModel
from gradio_client import Client, handle_file
import uvicorn
app = FastAPI(title="Audio Transcription API (via URL)")
# Hugging Face client
try:
client = Client("Ravishankarsharma/voice2text-summarizer")
except Exception as e:
print("Warning: Hugging Face client failed:", e)
client = None
# ✅ Pydantic model for URL input
class AudioURL(BaseModel):
url: str
@app.get("/", response_class=HTMLResponse)
async def home():
return HTMLResponse("""
<html><body>
<h2>Submit an audio URL to transcribe:</h2>
<form action="/transcribe_url" method="post">
<input name="url" type="text" placeholder="Enter audio URL" required>
<button type="submit">Transcribe</button>
</form>
<p>Swagger: <a href="/docs">/docs</a></p>
</body></html>
""")
# ✅ New endpoint: Accepts URL instead of file
@app.post("/transcribe_url")
async def transcribe_from_url(audio: AudioURL):
if not client:
raise HTTPException(status_code=500, detail="Hugging Face client not initialized")
try:
# 1. Download the audio file from given URL
response = requests.get(audio.url, stream=True)
if response.status_code != 200:
raise HTTPException(status_code=400, detail="Failed to download audio file from URL")
# 2. Save it temporarily
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
for chunk in response.iter_content(chunk_size=8192):
tmp.write(chunk)
tmp_path = tmp.name
# 3. Send to Hugging Face model
result = client.predict(handle_file(tmp_path), api_name="/predict")
os.remove(tmp_path)
return {
"source_url": audio.url,
"transcription": result[0],
"summary": result[1],
"api_endpoint": result[2]
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
print("Server running at http://127.0.0.1:8000")
uvicorn.run("app:app", host="127.0.0.1", port=8000, reload=True)
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