added a few example images and prompts

app.py

@@ -280,7 +280,7 @@ def clear_all():
 
 # Build the Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# **H2O-Mississippi**")
+    gr.Markdown("# **H2OVL-Mississippi**")
     
     state= gr.State()
     image_state = gr.State()
@@ -320,11 +320,11 @@ with gr.Blocks() as demo:
                 interactive=True,
                 label="Max New Tokens (default: 1024)"
             )
+            
     with gr.Row():
         submit_button = gr.Button("Submit")
         regenerate_button = gr.Button("Regenerate")
-        clear_button = gr.Button("Clear")
-        
+        clear_button = gr.Button("Clear")        
 
     # When the submit button is clicked, call the inference function
     submit_button.click(
@@ -339,11 +339,24 @@ with gr.Blocks() as demo:
         outputs=[chatbot, state, image_state]
     )
 
-
     clear_button.click(
         fn=clear_all, 
         inputs=None, 
         outputs=[chatbot, state, image_state, image_input]
+    )            
+    gr.Examples(
+        examples=[
+            ["assets/driver_license.png", "Extract the text from the image and fill the following json {'license_number':'',\n'full_name':'',\n'date_of_birth':'',\n'address':'',\n'issue_date':'',\n'expiration_date':'',\n}"],
+            # ["assets/receipt.jpg", "Read the text on the image"],
+            ["assets/invoice.png", "Please extract the following fields, and return the result in JSON format: supplier_name, supplier_address, customer_name, customer_address, invoice_number, invoice_total_amount, invoice_tax_amount"],
+            ["assets/CBA-1H23-Results-Presentation_wheel.png", "What is the efficiency of H2O.AI in document processing?"],
+        ],
+        inputs = [image_input, user_input, temperature_input, top_p_input, max_new_tokens_input, chatbot, state, image_state],
+        outputs = [chatbot, state, image_state, user_input],
+        fn=inference,
+        label = "examples",
     )
+    
+
 
 demo.launch()