Update app.py

app.py

@@ -1,23 +1,27 @@
 import gradio as gr
 from transformers import pipeline
 
-# Initialize the audio classification pipeline with the MIT model
-pipe = pipeline("audio-classification", model="MIT/ast-finetuned-audioset-10-10-0.4593")
+# Load the summarization pipeline with the BART model
+pipe = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# Define the function to classify an audio file
-def classify_audio(audio):
-    result = pipe(audio)
-    return {label['label']: label['score'] for label in result}
+def summarize_text(text):
+    # Use the summarization pipeline to summarize the input text
+    summary = pipe(text, max_length=150, min_length=40, do_sample=False)
+    return summary[0]['summary_text']
 
-# Set up the Gradio interface
-app = gr.Interface(
-    fn=classify_audio,                  # Function to classify audio
-    inputs=gr.Audio(),                  # Input for uploading an audio file (no 'source' or 'type' needed)
-    outputs=gr.Label(num_top_classes=3), # Output with top 3 classification results
-    title="Audio Classification",        # App title
-    description="Upload an audio file to classify it using MIT's fine-tuned AudioSet model."
+# Gradio interface setup
+title = "Text Summarization with BART"
+description = "Provide a block of text, and the BART model will summarize it."
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=summarize_text,
+    inputs=gr.Textbox(lines=10, label="Input Text"),
+    outputs=gr.Textbox(label="Summarized Text"),
+    title=title,
+    description=description,
 )
 
-# Launch the app
+# Launch the Gradio app
 if __name__ == "__main__":
-    app.launch()
+    interface.launch()