Update app.py

app.py

@@ -1,47 +1,32 @@
 import streamlit as st
-import sounddevice as sd
-import soundfile as sf
 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")
     audio_input = st.audio("Capture Audio", format="audio/wav", start_recording=True, channels=1)
     if st.button("Stop Recording"):
-        sd.stop()
-        with st.spinner("Classifying..."):
-            # Read the recorded audio file
-            recording_path = "recording.wav"
-            audio_data, sampling_rate = sf.read(recording_path)
-            # Classify the audio
-            stutter_type = classify_audio(audio_data)
-            st.write("Predicted Stutter Type:", stutter_type)
+        # Assuming the recording is saved as "recording.wav"
+        recording_path = "recording.wav"
+        # Call the model pipeline to classify the audio
+        prediction = model(recording_path)
+        # Get the predicted label
+        predicted_label = prediction[0]["label"]
+        # Map the label to the corresponding stutter type
+        if predicted_label == 0:
+            stutter_type = "nonstutter"
+        elif predicted_label == 1:
+            stutter_type = "prolongation"
+        elif predicted_label == 2:
+            stutter_type = "repetition"
+        elif predicted_label == 3:
+            stutter_type = "blocks"
+        else:
+            stutter_type = "Unknown"
+        st.write("Predicted Stutter Type:", stutter_type)
 
 if __name__ == "__main__":
     main()