Update app.py

app.py

@@ -12,12 +12,8 @@ model_path = "sshleifer/distilbart-cnn-12-6"
 text_summary = pipeline("summarization", model=model_path, torch_dtype=torch.bfloat16)
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 
-# Function to process and tokenize text
-def tokenize_text(input_text):
-    return tokenizer.encode(input_text, truncation=False)
-
 # Function to summarize text
-def summarize_text(input_text, min_length=50, max_length=200):
+def summarize_text(input_text, min_length=50, max_length=150):
     summary_output = text_summary(
         input_text,
         min_length=min_length,
@@ -25,7 +21,7 @@ def summarize_text(input_text, min_length=50, max_length=200):
     )
     return summary_output[0]['summary_text']
 
-# Preload article and summary
+# Article Example
 article_example = """Niteesh Nigam: A Visionary in Robotics, Machine Vision, and AI
 
 Niteesh Nigam, a forward-thinking robotics engineer and AI developer, has consistently demonstrated his passion for innovation and his ability to transform complex technological concepts into impactful real-world solutions. With a Master’s degree in Robotics and Autonomous Systems from Arizona State University (ASU) and a Bachelor’s degree in Mechanical Engineering from the Birla Institute of Technology and Science, Pilani Dubai, Niteesh has cultivated a robust foundation in engineering, computer vision, and artificial intelligence. His interdisciplinary expertise and hands-on experience have made him a standout professional in fields such as robotics, machine vision, deep learning, and control systems.
@@ -73,7 +69,7 @@ Niteesh Nigam’s work embodies a perfect blend of technical mastery, innovation
 As he looks to the future, Niteesh remains committed to making meaningful contributions in robotics, AI, and automation, with a focus on scalable solutions that benefit industries and communities alike. His journey is a testament to the transformative potential of technology when guided by a visionary like him.
 """
 
-# Precomputed summary
+# Precomputed Summary
 summary_example = summarize_text(article_example, min_length=50, max_length=150)
 
 # Create Gradio interface
@@ -82,29 +78,32 @@ with gr.Blocks() as demo:
     gr.Markdown(
         "This app showcases the ability to summarize complex content into concise information. Below is an example:"
     )
-    text_input = gr.Textbox(
-        label="Input Text", 
-        lines=10, 
-        value=article_example, 
-        interactive=False
-    )
-    summary_output = gr.Textbox(
-        label="Summarized Output", 
-        lines=10, 
-        value=summary_example, 
-        interactive=False
-    )
 
-    gr.Markdown("## Try It Yourself!")
-    custom_input = gr.Textbox(label="Input Your Text", lines=10, placeholder="Paste your text here...")
+    # Input Text
+    input_text = gr.Textbox(label="Input Text", lines=10)
+
+    # Button to Summarize
     summarize_button = gr.Button("Summarize")
-    custom_summary_output = gr.Textbox(label="Summary Output", lines=10)
 
-    # Button action for custom input
+    # Output Summary
+    summary_output = gr.Textbox(label="Summary Output", lines=10)
+
+    # Add examples using Gradio's example feature
+    gr.Examples(
+        examples=[
+            [article_example],  # Input text example
+        ],
+        inputs=input_text,
+        outputs=summary_output,
+        fn=summarize_text,
+        label="Example: Summarizing a Detailed Article"
+    )
+
+    # Button click to summarize user-provided input
     summarize_button.click(
-        summarize_text, 
-        inputs=[custom_input], 
-        outputs=[custom_summary_output]
+        summarize_text,
+        inputs=[input_text],
+        outputs=[summary_output]
     )
 
 # Launch the Gradio app