Create app.py

app.py

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+import gradio as gr
+import torch
+import torchaudio
+from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
+
+# মডেল লোড করো
+model_name = "rakib730/finetuned-gtzan"
+extractor = AutoFeatureExtractor.from_pretrained(model_name)
+model = AutoModelForAudioClassification.from_pretrained(model_name)
+
+# মডেলকে eval মোডে নাও
+model.eval()
+
+# অডিও ক্লাসিফিকেশন ফাংশন
+def classify_music(audio):
+    # audio: (numpy array, sample_rate)
+    waveform, sample_rate = audio
+    # মডেল ট্রেনিংয়ে ব্যবহৃত sample rate ঠিক করো
+    if sample_rate != 16000:
+        resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
+        waveform = resampler(torch.tensor(waveform))
+
+    inputs = extractor(waveform.squeeze().numpy(), sampling_rate=16000, return_tensors="pt")
+    
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        predicted_class_id = torch.argmax(logits, dim=1).item()
+        predicted_label = model.config.id2label[predicted_class_id]
+    
+    return predicted_label
+
+# Gradio UI
+gr.Interface(
+    fn=classify_music,
+    inputs=gr.Audio(type="numpy", label="Upload a Music Clip (WAV/MP3)"),
+    outputs=gr.Textbox(label="Predicted Genre"),
+    title="🎵 Music Genre Classifier",
+    description="Upload a short music clip and get the predicted genre using a fine-tuned GTZAN model.",
+    live=False
+).launch()