Create app.py

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+import torch
+import librosa
+from transformers import AutoModelForAudioClassification, Wav2Vec2FeatureExtractor
+
+model_id = "abedir/emotion-detector"
+
+processor = Wav2Vec2FeatureExtractor.from_pretrained(model_id)
+model = AutoModelForAudioClassification.from_pretrained(model_id)
+
+label_map = {
+    0: "Angry/Fearful",
+    1: "Happy/Laugh",
+    2: "Neutral/Calm",
+    3: "Sad/Cry",
+    4: "Surprised/Amazed"
+}
+
+def predict(audio):
+    audio, sr = librosa.load(audio, sr=16000)
+    inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = torch.softmax(logits, dim=1)[0]
+
+    pred = torch.argmax(probs).item()
+    return label_map[pred], float(probs[pred])
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Audio(type="filepath"),
+    outputs=["text", "number"],
+    title="Emotion Detector 🎤"
+)
+
+iface.launch()