Update app.py

app.py

@@ -1,3 +1,28 @@
 import gradio as gr
+from transformers import pipeline
 
-gr.load("models/HareemFatima/distilhubert-finetuned-stutterdetection").launch()
+# Load the model pipeline
+model = pipeline("audio-classification", model="HareemFatima/distilhubert-finetuned-stutterdetection")
+
+# Define a function to classify the audio and return the predicted label
+def classify_audio(audio_input):
+    # Call the 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
+    if predicted_label == 0:
+        return "nonstutter"
+    elif predicted_label == 1:
+        return "prolongation"
+    elif predicted_label == 2:
+        return "repetition"
+    elif predicted_label == 3:
+        return "blocks"
+    else:
+        return "Unknown"
+
+# Create the Gradio interface
+audio_input = gr.inputs.Audio(source="microphone", type="file")
+output_label = gr.outputs.Label()
+gr.Interface(fn=classify_audio, inputs=audio_input, outputs=output_label).launch()