edit

app.py

@@ -1,3 +1,99 @@
+# 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