Create app.py

app.py

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+import torch
+import gradio as gr
+from model import ECAPA_gender
+
+model = ECAPA_gender.from_pretrained("Beijuka/voice-gender-classifier")
+model.eval()
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def predict_gender_confidence(audio_file):
+    if audio_file is None:
+        return "No audio provided"
+    
+    try:
+        # Load audio
+        audio = model.load_audio(audio_file.name if hasattr(audio_file, "name") else audio_file)
+        audio = audio.to(device)
+        
+        # Forward pass
+        with torch.no_grad():
+            logits = model.forward(audio)
+            probs = torch.softmax(logits, dim=1).cpu().numpy()[0] 
+            pred_idx = logits.argmax(dim=1).item()
+            gender_pred = model.pred2gender[pred_idx].capitalize()
+            confidence = probs[pred_idx] * 100 
+        
+        return f"{gender_pred} — {confidence:.1f}% confidence"
+    
+    except Exception as e:
+        return f"Error: {e}"
+
+iface = gr.Interface(
+    fn=predict_gender_confidence,
+    inputs=gr.Audio(type="filepath", label="Upload audio file", sources=["upload"]),
+    outputs=gr.Textbox(label="Predicted Gender with Confidence"),
+    title="Voice Gender Classifier",
+    description="Upload an audio file and the model predicts speaker gender with confidence.",
+    allow_flagging="never"
+)
+
+iface.launch(share=True)