Create app.py

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+from transformers import pipeline
+
+# Model options available for summarization
+model_names = ["Falconsai/text_summarization"]
+
+def summarize_article(article, model_name, max_length, temperature, top_k, top_p):
+    """ Summarize the provided article text using the specified model and hyperparameters. """
+    summarizer = pipeline("summarization", model=model_name)
+    summary = summarizer(
+        article,
+        max_length=int(round(max_length)),
+        min_length=30,
+        do_sample=True,
+        temperature=temperature,
+        top_k=int(round(top_k)),
+        top_p=top_p
+    )
+    return summary[0]['summary_text']
+
+# Gradio interface setup
+iface = gr.Interface(
+    fn=summarize_article,
+    inputs=[
+        gr.Textbox(lines=10, placeholder="Enter the article text here..."), 
+        gr.Dropdown(choices=model_names, label="Select Model"),
+        gr.Slider(minimum=10, maximum=200, step=10, label="Max Length of Summary"),
+        gr.Slider(minimum=0.1, maximum=2, step=0.1, label="Temperature for Sampling"),
+        gr.Slider(minimum=1, maximum=100, step=1, label="Top-k"),
+        gr.Slider(minimum=0.1, maximum=1, step=0.1, label="Top-p")
+    ],
+    outputs="text",
+    title="Text Summarization",
+    description="Adjust the parameters below to summarize the article."
+)
+
+iface.launch(debug=True, share=True)