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