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import gradio as gr
from transformers import pipeline
# Project description
description = """
# Kalbe Farma - Visual Question Answering (VQA) for Medical Imaging
## Overview
The project addresses the challenge of accurate and efficient medical imaging analysis in healthcare, aiming to reduce human error and workload for radiologists. The proposed solution involves developing advanced AI models for Visual Question Answering (VQA) to assist healthcare professionals in analyzing medical images quickly and accurately. These models will be integrated into a user-friendly web application, providing a practical tool for real-world healthcare settings.
## Dataset
The model is trained using the [Hugging face](https://huggingface.co/datasets/flaviagiammarino/vqa-rad/viewer).
Reference: [ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0933365723001252)
## Model Architecture
The model uses a Parameterized Hypercomplex Shared Encoder network (PHYSEnet).
Reference: [ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0933365723001252)
## Demo
Please select the example below or upload 4 pairs of mammography exam results.
"""
# 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(description, elem_classes="description")
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)
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