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import gradio as gr
from transformers import pipeline
# Load the Visual QA model
generator = pipeline("visual-question-answering", model="jihadzakki/blip1-medvqa")
def format_answer(image, question, history):
try:
result = generator(image, question, max_new_tokens=50)
predicted_answer = result[0].get('answer', 'No answer found')
history.append((image, f"Question: {question} | Answer: {predicted_answer}"))
return f"Predicted Answer: {predicted_answer}", history
except Exception as e:
return f"Error: {str(e)}", history
def switch_theme(mode):
if mode == "Light Mode":
return gr.themes.Default()
else:
return gr.themes.Soft(primary_hue=gr.themes.colors.blue, secondary_hue=gr.themes.colors.orange)
def save_feedback(feedback):
return "Thank you for your feedback!"
def display_history(history):
log_entries = []
for img, text in history:
log_entries.append((img, text))
return log_entries
# Build the Visual QA application using Gradio with improvements
with gr.Blocks(
theme=gr.themes.Soft(
font=[gr.themes.GoogleFont("Inconsolata"), "Arial", "sans-serif"],
primary_hue=gr.themes.colors.blue,
secondary_hue=gr.themes.colors.red,
)
) as VisualQAApp:
gr.Markdown("# Visual Question Answering using BLIP Model", elem_classes="title")
with gr.Row():
with gr.Column():
image_input = gr.Image(label="Upload image", type="pil")
question_input = gr.Textbox(show_label=False, placeholder="Enter your question here...")
submit_button = gr.Button("Submit", variant="primary")
with gr.Column():
answer_output = gr.Textbox(label="Result Prediction")
history_state = gr.State([]) # Initialize the history state
submit_button.click(
format_answer,
inputs=[image_input, question_input, history_state],
outputs=[answer_output, history_state],
show_progress=True
)
with gr.Row():
history_gallery = gr.Gallery(label="History Log", elem_id="history_log")
submit_button.click(
display_history,
inputs=[history_state],
outputs=[history_gallery]
)
with gr.Accordion("Help", open=False):
gr.Markdown("**Upload image**: Select the chest X-ray image you want to analyze.")
gr.Markdown("**Enter your question**: Type the question you have about the image, such as 'Is there any sign of pneumonia?'")
gr.Markdown("**Submit**: Click the submit button to get the prediction from the model.")
with gr.Accordion("User Preferences", open=False):
gr.Markdown("**Mode**: Choose between light and dark mode for your comfort.")
mode_selector = gr.Radio(choices=["Light Mode", "Dark Mode"], label="Select Mode")
apply_theme_button = gr.Button("Apply Theme")
apply_theme_button.click(
switch_theme,
inputs=[mode_selector],
outputs=[],
)
with gr.Accordion("Feedback", open=False):
gr.Markdown("**We value your feedback!** Please provide any feedback you have about this application.")
feedback_input = gr.Textbox(label="Feedback", lines=4)
submit_feedback_button = gr.Button("Submit Feedback")
submit_feedback_button.click(
save_feedback,
inputs=[feedback_input],
outputs=[feedback_input]
)
VisualQAApp.launch(share=True, server_name="0.0.0.0", server_port=8080, debug=True)
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