edit

app.py

@@ -1,99 +1,3 @@
-# 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)
-
 import gradio as gr
 from transformers import pipeline
 
@@ -188,4 +92,4 @@ with gr.Blocks(
             outputs=[feedback_input]
         )
 
-VisualQAApp.launch()
+VisualQAApp.launch(share=True)