Update app.py

app.py

@@ -1,31 +1,18 @@
-import gradio as gr
-from transformers import BertTokenizer, BertForSequenceClassification
-import torch
-
-# Load the model and tokenizer from the folder in Hugging Face space
-model_folder = "FYP_Model"  # Replace with your actual username and model name
-tokenizer = BertTokenizer.from_pretrained(model_folder)
-model = BertForSequenceClassification.from_pretrained(model_folder)
+from transformers import pipeline
 
-def classify_audio(audio_file):
-    # Read the audio file and tokenize it
-    audio_content = audio_file.read()
-    inputs = tokenizer(audio_content, return_tensors="pt", truncation=True)
+model_id = "arham061/distilhubert-finetuned-RHD_Dataset"
+pipe = pipeline("audio-classification", model=model_id)
 
-    # Perform inference using the loaded model
-    outputs = model(**inputs)
-    logits = outputs.logits
-    predicted_class = torch.argmax(logits).item()
+def classify_audio(filepath):
+    preds = pipe(filepath)
+    outputs = {}
+    for p in preds:
+        outputs[p["label"]] = p["score"]
+    return outputs
 
-    return f"Predicted class: {predicted_class}"
+import gradio as gr
 
-# Gradio Interface
-iface = gr.Interface(
-    fn=classify_audio,
-    inputs=gr.Audio(type="file", label="Upload or Record Audio"),
-    outputs=gr.Textbox(),
-    live=True,
+demo = gr.Interface(
+    fn=classify_audio, inputs=gr.Audio(type="filepath"), outputs="label"
 )
-
-# Launch the Gradio interface
-iface.launch()
+demo.launch(debug=True)