Update app.py

app.py

@@ -1,23 +1,28 @@
 import gradio as gr
 import torch
 
-# This is a simplified version showing just the UI structure
-# You'll need to integrate your actual model loading and generation code
-
 def generate_text(prompt, max_length, temperature, top_p, top_k, repetition_penalty):
-    # Your text generation function would go here
-    # This is just a placeholder that returns the prompt as a demonstration
-    return f"Generated text based on: {prompt}"
+    # This is a placeholder function
+    # In a real implementation, you would:
+    # 1. Preprocess the input prompt
+    # 2. Load your ML model
+    # 3. Generate text using the specified parameters
+    # For now, we'll just return a simple generated text
+    
+    if not prompt:
+        return "Please enter a prompt."
+    
+    return f"Another important factor in promoting healthy aging is social interaction. Social isolation and loneliness have been linked to an increased risk of cognitive decline and dementia. Maintaining strong social connections with family, friends, and community members can help to keep the brain active and engaged.\n\nIn addition to these lifestyle factors, there are also medical interventions that can help to promote healthy aging. For example, certain medications can help to slow down the progression of Alzheimer's disease and other forms of dementia. Other treatments, such as cognitive training programs and brain stimulation therapies, may also be effective in improving cognitive function in older adults.\n\nDespite these promising developments, there is still much work to be done in understanding the"
 
-# Create the interface with a layout matching image 2
+# Create the Gradio interface
 with gr.Blocks() as demo:
     with gr.Row():
         # Left column - Input
         with gr.Column():
             prompt = gr.Textbox(
-                label="Enter your prompt",
-                placeholder="Type your prompt here...",
-                lines=5
+                label="Enter your prompt", 
+                placeholder="Recent advances in neuroimaging suggest that",
+                lines=3
             )
             
             with gr.Row():
@@ -26,34 +31,34 @@ with gr.Blocks() as demo:
             
             with gr.Accordion("Advanced Options", open=False):
                 max_length = gr.Slider(
-                    label="Maximum Length",
+                    label="Maximum Length", 
                     minimum=64, maximum=1024, value=512, step=64
                 )
                 
                 temperature = gr.Slider(
-                    label="Temperature (0 = deterministic, 0.7 = creative, 1.5 = random)",
+                    label="Temperature (0 = deterministic, 0.7 = creative, 1.5 = random)", 
                     minimum=0, maximum=1.5, value=0.7, step=0.1
                 )
                 
                 top_p = gr.Slider(
-                    label="Top-p (nucleus sampling)",
+                    label="Top-p (nucleus sampling)", 
                     minimum=0.1, maximum=1.0, value=0.9, step=0.1
                 )
                 
                 top_k = gr.Slider(
-                    label="Top-k",
+                    label="Top-k", 
                     minimum=1, maximum=100, value=40, step=1
                 )
                 
                 repetition_penalty = gr.Slider(
-                    label="Repetition Penalty",
+                    label="Repetition Penalty", 
                     minimum=1.0, maximum=2.0, value=1.1, step=0.1
                 )
         
         # Right column - Output
         with gr.Column():
             output = gr.Textbox(
-                label="Generated Text",
+                label="Generated Text", 
                 lines=20
             )
     
@@ -65,21 +70,22 @@ with gr.Blocks() as demo:
         "What are the key differences between neurons and glial cells?"
     ]
     
+    # Add examples
     gr.Examples(
         examples=example_prompts,
         inputs=prompt
     )
     
-    # Set up event handlers
+    # Event handlers
     generate_btn.click(
-        fn=generate_text,
-        inputs=[prompt, max_length, temperature, top_p, top_k, repetition_penalty],
+        fn=generate_text, 
+        inputs=[prompt, max_length, temperature, top_p, top_k, repetition_penalty], 
         outputs=output
     )
     
     clear_btn.click(
-        fn=lambda: ("", ""),
-        inputs=None,
+        fn=lambda: None, 
+        inputs=None, 
         outputs=[prompt, output]
     )