app.py

from transformers import pipeline

# Load the model pipeline
model = pipeline("audio-classification", model="HareemFatima/distilhubert-finetuned-stutterdetection")

# Define a function to map predicted labels to types of stuttering
def map_label_to_stutter_type(label):
    if label == 0:
        return "nonstutter"
    elif label == 1:
        return "prolongation"
    elif label == 2:
        return "repetition"
    elif label == 3:
        return "blocks"
    else:
        return "Unknown"

# Function to classify audio input and return the stutter type
def classify_audio(audio_input):
    # Call your model pipeline to classify the audio
    prediction = model(audio_input)
    # Get the predicted label
    predicted_label = prediction[0]["label"]
    # Map the label to the corresponding stutter type
    stutter_type = map_label_to_stutter_type(predicted_label)
    return stutter_type

# Streamlit app
def main():
    st.title("Stutter Classification App")
    st.audio("path_to_your_audio_file", format="audio/wav")  # Add audio input widget here
    if st.button("Classify"):
        audio_input = st.audio("path_to_your_audio_file", format="audio/wav")  # Add audio input widget here
        stutter_type = classify_audio(audio_input)
        st.write("Predicted Stutter Type:", stutter_type)

if __name__ == "__main__":
    main()