Add files

app.py

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+import tempfile
+from transformers import pipeline
+import gradio as gr
+from fastapi import FastAPI, UploadFile, File, Request
+import os
+from typing import Optional
+
+# Initialize classifier
+classifier = pipeline("audio-classification", model="superb/hubert-large-superb-er")
+
+# Create FastAPI app (works with Gradio)
+app = FastAPI()
+
+def save_upload_file(upload_file: UploadFile) -> str:
+    """Save uploaded file to temporary location"""
+    try:
+        suffix = os.path.splitext(upload_file.filename)[1]
+        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+            tmp.write(upload_file.file.read())
+            return tmp.name
+    finally:
+        upload_file.file.close()
+
+@app.post("/api/predict")
+async def predict_from_upload(file: UploadFile = File(...)):
+    """API endpoint for FormData uploads"""
+    try:
+        # Save the uploaded file temporarily
+        temp_path = save_upload_file(file)
+        
+        # Process the audio
+        predictions = classifier(temp_path)
+        
+        # Clean up
+        os.unlink(temp_path)
+        
+        return {"predictions": predictions}
+    except Exception as e:
+        return {"error": str(e)}, 500
+
+# Gradio interface for testing
+def gradio_predict(audio_file):
+    """Gradio interface that handles both file objects and paths"""
+    if isinstance(audio_file, str):  # Path from Gradio upload
+        audio_path = audio_file
+    else:  # Direct file object
+        temp_path = save_upload_file(audio_file)
+        audio_path = temp_path
+    
+    predictions = classifier(audio_path)
+    
+    if hasattr(audio_file, 'file'):  # Clean up if we created temp file
+        os.unlink(audio_path)
+    
+    return {p["label"]: p["score"] for p in predictions}
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=gradio_predict,
+    inputs=gr.Audio(type="filepath", label="Upload Audio"),
+    outputs=gr.Label(num_top_classes=5),
+    title="Audio Emotion Recognition",
+    description="Upload an audio file to analyze emotional content"
+)
+
+# Mount Gradio app
+app = gr.mount_gradio_app(app, demo, path="/")
+
+# For running locally
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+fastapi
+transformers
+torch
+librosa
+gradio
+python-multipart
+uvicorn