update code

app.py

@@ -81,24 +81,28 @@ def format_answer(image, question, history):
         return f"Error: {str(e)}", history
 
 def clear_history():
-    return "", []
+    return None, "", []
 
 def undo_last(history):
     if history:
         history.pop()
-    return "", history
+    if history:
+        last_image, last_entry = history[-1]
+        return last_image, last_entry, history
+    return None, "", history
 
-def retry_last(image, question, history):
+def retry_last(history):
     if history:
         last_image, last_entry = history[-1]
+        question = last_entry.split(" | ")[0].replace("Question: ", "")
         return format_answer(last_image, question, history[:-1])
-    return "No previous analysis to retry.", history
+    return None, "No previous analysis to retry.", 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)
+        return gr.themes.Soft(primary_hue=gr.themes.colors.green, secondary_hue=gr.themes.colors.green)
 
 def save_feedback(feedback):
     return "Thank you for your feedback!"
@@ -113,8 +117,8 @@ def display_history(history):
 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,
+        primary_hue=gr.themes.colors.green,
+        secondary_hue=gr.themes.colors.green,
     )
 ) as VisualQAApp:
     gr.HTML(html_content)  # Display the HTML content
@@ -122,10 +126,9 @@ with gr.Blocks(
     with gr.Row():
         with gr.Column():
             image_input = gr.Image(label="Upload image", type="pil")
+        with gr.Column():
             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
@@ -144,20 +147,20 @@ with gr.Blocks(
 
         retry_button.click(
             retry_last,
-            inputs=[image_input, question_input, history_state],
-            outputs=[answer_output, history_state]
+            inputs=[history_state],
+            outputs=[answer_output, image_input, history_state]
         )
 
         undo_button.click(
             undo_last,
             inputs=[history_state],
-            outputs=[answer_output, history_state]
+            outputs=[image_input, answer_output, history_state]
         )
 
         clear_button.click(
             clear_history,
             inputs=[],
-            outputs=[answer_output, history_state]
+            outputs=[image_input, answer_output, history_state]
         )
 
     with gr.Row():

index.html

@@ -109,7 +109,7 @@
 					<div>
 						<p class="overview-content">
 							The model is trained with Colorectal Nuclear Segmentation and Phenotypes (CoNSeP) dataset
-							<a href="https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet" target="_blank">https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet</a>. Images were extracted from 16 colorectal adenocarcinoma (CRA) WSIs.
+							<a href="https://huggingface.co/datasets/mdwiratathya/SLAKE-vqa-english" target="_blank">https://huggingface.co/datasets/mdwiratathya/SLAKE-vqa-english</a>. Images were extracted from 16 colorectal adenocarcinoma (CRA) WSIs.
 						</p>
 						<ul>
 							<li>Target: Nuclei</li>

style.css

@@ -1,79 +0,0 @@
-* {
-	box-sizing: border-box;
-}
-
-body {
-	font-family: 'Source Sans Pro', sans-serif;
-	font-size: 16px;
-}
-
-.container {
-	width: 100%;
-	margin: 0 auto;
-}
-
-.title {
-	font-size: 24px !important;
-	font-weight: 600 !important;
-	letter-spacing: 0em;
-	text-align: center;
-	color: #374159 !important;
-}
-
-.subtitle {
-	font-size: 24px !important;
-	font-style: italic;
-	font-weight: 400 !important;
-	letter-spacing: 0em;
-	text-align: center;
-	color: #1d652a !important;
-	padding-bottom: 0.5em;
-}
-
-.overview-heading {
-	font-size: 24px !important;
-	font-weight: 600 !important;
-	letter-spacing: 0em;
-	text-align: left;
-}
-
-.overview-content {
-	font-size: 14px !important;
-	font-weight: 400 !important;
-	line-height: 33px !important;
-	letter-spacing: 0em;
-	text-align: left;
-}
-
-.content-image {
-	width: 100% !important;
-	height: auto !important;
-}
-
-.vl {
-	border-left: 5px solid #1d652a;
-	padding-left: 20px;
-	color: #1d652a !important;
-}
-
-.grid-container {
-	display: grid;
-	grid-template-columns: 1fr 2fr;
-	gap: 20px;
-	align-items: flex-start;
-  margin-bottom: 1em;
-}
-
-@media screen and (max-width: 768px) {
-	.container {
-		width: 90%;
-	}
-
-	.grid-container {
-		display: block;
-	}
-
-	.overview-heading {
-		font-size: 18px !important;
-	}
-}