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import gradio as gr
import time
def update_fortune(user_input):
# Simulate processing and an "animation"
for _ in range(3):
time.sleep(0.5)
# Update the fortune image and reserved text field
new_image = "fortune2.png" # New fortune image after submission.
reserved_text = f"Processed input: {user_input}" # Placeholder reserved text.
# Make the bottom left enquiry components visible.
show_bottom = gr.update(visible=True)
return new_image, reserved_text, show_bottom, show_bottom
def enquiry_action():
# This function is triggered by the bottom left enquiry button.
return "Here are some words after the enquiry."
with gr.Blocks() as demo:
gr.Markdown("# Wong Tai Sin Fortuen Stick Enquiry")
with gr.Row():
# Left column with two sections (top left and bottom left)
with gr.Column(scale=1):
# Top left container with subtitle and input field + submit button
with gr.Group():
gr.Markdown("**有什麼問題/Type your question**")
with gr.Row():
user_input = gr.Textbox(placeholder="Enter your sentence here...", show_label=False)
submit_btn = gr.Button("Submit")
# Bottom left container (initially hidden) with its subtitle and enquiry button
with gr.Group(visible=False) as bottom_left_group:
gr.Markdown("**解籤/Stick Enquiry**")
enquiry_btn = gr.Button("解籤/Stick Enquiry")
enquiry_output = gr.Textbox(label="Enquiry Output", placeholder="Output will appear here...")
# Right column: merged section for fortune image and reserved text
with gr.Column(scale=2):
gr.Markdown("**籤文/fortune explanation**")
fortune_image = gr.Image(value="fortune.png", label=None, height=300)
reserved_text = gr.Textbox(label="Reserved Text Field", placeholder="Reserved for future content...", interactive=False)
# When the submit button is clicked:
# - Update the fortune image and reserved text,
# - And reveal the bottom left enquiry section.
submit_btn.click(
fn=update_fortune,
inputs=user_input,
outputs=[fortune_image, reserved_text, enquiry_btn, enquiry_output]
)
# The enquiry button's click updates its output.
enquiry_btn.click(fn=enquiry_action, inputs=[], outputs=enquiry_output)
demo.launch()
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