Create app.py

app.py

@@ -0,0 +1,66 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the summarization pipeline
+pipe = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Define the summarization function
+def summarize_text(text, max_length=130, min_length=30, length_penalty=2.0):
+    response = pipe(
+        text,
+        max_length=max_length,
+        min_length=min_length,
+        length_penalty=length_penalty,
+        truncation=True
+    )
+    return response[0]['summary_text']
+
+# Create the Gradio app interface
+with gr.Blocks() as app:
+    gr.Markdown("## Text Summarization App")
+    gr.Markdown(
+        "Enter a long text below, and the model will generate a concise summary. "
+        "This app uses the `facebook/bart-large-cnn` model."
+    )
+
+    with gr.Row():
+        input_text = gr.Textbox(
+            label="Input Text",
+            placeholder="Paste your text here...",
+            lines=10
+        )
+        output_summary = gr.Textbox(label="Summary", lines=5)
+
+    max_length = gr.Slider(
+        label="Max Length", 
+        minimum=50, 
+        maximum=200, 
+        step=10, 
+        value=130
+    )
+    min_length = gr.Slider(
+        label="Min Length", 
+        minimum=10, 
+        maximum=100, 
+        step=10, 
+        value=30
+    )
+    length_penalty = gr.Slider(
+        label="Length Penalty", 
+        minimum=0.5, 
+        maximum=3.0, 
+        step=0.1, 
+        value=2.0
+    )
+
+    submit_button = gr.Button("Summarize")
+    
+    submit_button.click(
+        fn=summarize_text,
+        inputs=[input_text, max_length, min_length, length_penalty],
+        outputs=output_summary
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch()