Update app.py

app.py

@@ -1,19 +1,28 @@
 import gradio as gr
+from transformers import BertTokenizer, BertForSequenceClassification
 import torch
-from transformers import pipeline
 
-# Load the model from Hugging Face space
-model = pipeline(task="audio-classification", model="FYP_Model")
+# 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)
 
 def classify_audio(audio_file):
-    # Perform inference using the Hugging Face model
-    result = model(audio_file.read())
-    return result[0]["label"]
+    # Read the audio file and tokenize it
+    audio_content = audio_file.read()
+    inputs = tokenizer(audio_content, return_tensors="pt", truncation=True)
+
+    # Perform inference using the loaded model
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits).item()
+
+    return f"Predicted class: {predicted_class}"
 
 # Gradio Interface
 iface = gr.Interface(
     fn=classify_audio,
-    inputs=gr.Audio(type="filepath", label="Upload or Record Audio"),
+    inputs=gr.Audio(type="file", label="Upload or Record Audio"),
     outputs=gr.Textbox(),
     live=True,
 )