Create app.py

app.py

@@ -0,0 +1,39 @@
+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()