app.py

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)

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)

    # 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="file", label="Upload or Record Audio"),
    outputs=gr.Textbox(),
    live=True,
)

# Launch the Gradio interface
iface.launch()