Add Blocks and Plots

app.py

@@ -1,33 +1,85 @@
 import gradio as gr
+import pandas as pd
 
-def run_evaluation(dataset_id, methodology):
-    return f'Running evaluation for {dataset_id} with {methodology}'
+data = [
+    ["Category", "Value", "Percentage"],
+    ["Total Reviews", 50000, None],
+    ["Total Sentences", 621647, None],
+    ["Pronouns in Sentences", None, None],
+    ["Male Pronouns", 85615, None],
+    ["Female Pronouns", 39372, None],
+    ["Both Male and Female Pronouns", 7765, None],
+    ["Exclusive Usage of Pronouns", None, None],
+    ["Only Male Pronouns", 77860, 13.77],
+    ["Only Female Pronouns", 31617, 6.33],
+    ["Pronouns and Professions in Sentences", None, None],
+    ["Male Pronouns with Professions", 5580, 0.9],
+    ["Female Pronouns with Professions", 2618, 0.42],
+    ["Exclusive Usage of Pronouns with Professions", None, None],
+    ["Only Male Pronouns with Professions", 5011, 0.81],
+    ["Only Female Pronouns with Professions", 2049, 0.33],
+    ["Pronouns and Professions in Combination", None, None],
+    ["Male or Female Pronouns with Professions", 7629, 1.23],
+    ["Male and Female Pronouns with Professions", 569, 0.09]
+]
 
-    if methodology == 'A':
-        run_a(dataset_id)
-    elif methodology == 'B':
-        run_b(dataset_id)
-    elif methodology == 'C':
-        run_c(dataset_id)
-    
 
-demo = gr.Blocks(theme=gr.themes.Soft())
+def display_methodology(methodology):
+    title = methodology
+    description = ""
+    details = ""
+    if methodology == "Term Identity Diversity Analysis":
+        description = "111"
+        details = "222"
+    elif methodology == "Textual Gender Label Evaluation":
+        description = "333"
+        details = "444"
+    elif methodology == "GenBit":
+        description = "555"
+        details = "666"
+
+    return title, description, details
+
+
+def run_evaluation(dataset, methodology):
+    return f"Running evaluation for {dataset} with {methodology}"
+
+    if methodology == "A":
+        run_a(dataset)
+    elif methodology == "B":
+        run_b(dataset)
+    elif methodology == "C":
+        run_c(dataset)
+
+
+demo = gr.Blocks(title="BiasAware: Dataset Bias Detection",
+                 theme=gr.themes.Soft())
 
 with demo:
     gr.Markdown("# BiasAware: Dataset Bias Detection")
-    
+    gr.Markdown(
+        "Natural Language Processing (NLP) training datasets often reflect the biases present in the data sources they are compiled from, leading to the **perpetuation of stereotypes, underrepresentation, and skewed perspectives in AI models**. BiasAware is designed to **identify and quantify biases present in text data**, making it an invaluable resource for data scientists, machine learning practitioners, and organizations committed to **mitigating bias in AI systems**."
+    )
+
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown("Select a dataset to analyze")
 
-            dataset_id = gr.Text(label="Dataset")
+            dataset = gr.Text(label="Dataset")
             gr.Examples(
                 examples=["imdb", "amazon_reviews_multi", "tweet_eval"],
                 fn=run_evaluation,
-                inputs=[dataset_id]
+                inputs=[dataset],
             )
 
-            methodology = gr.Dropdown(["Term Identity Diversity Analysis", "Textual Gender Label Evaluation", "GenBit"], label="Methodology")
+            methodology = gr.Radio(
+                [
+                    "Term Identity Diversity Analysis",
+                    "Textual Gender Label Evaluation",
+                    "GenBit",
+                ],
+                label="Methodology",
+            )
 
             button = gr.Button("Run Evaluation")
 
@@ -35,23 +87,39 @@ with demo:
             gr.Markdown("### Results")
 
             with gr.Box():
-                methodology_title = gr.Markdown("### Identity Term Sampling")
+                methodology_title = gr.Markdown("### Title")
                 methodology_description = gr.Markdown("lorem ipsum")
-            
-            methodology_test_description = gr.Markdown("lorem ipsum")
-            outputs = gr.Markdown()
+
+            methodology_details = gr.Markdown("lorem ipsum")
+            # outputs = gr.Markdown()
+            outputs = gr.DataFrame(pd.DataFrame(data), headers=[
+                                   "", "Count", "Percentage"])
+
             gr.Error("No results to display")
-        
+
+        with gr.Column(scale=1):
+            gr.Markdown("### Leaderboard")
+            gr.DataFrame(
+                headers=["Dataset", "Score"],
+                value=[
+                    ["imdb", 0.9],
+                    ["amazon_reviews_multi", 0.8],
+                    ["tweet_eval", 0.7],
+                ],
+                interactive=False,
+            )
+
     methodology.change(
-        fn=lambda x: (f'### {x}', "lorem ipseum", "lorem ipsum"),
+        fn=display_methodology,
         inputs=[methodology],
-        outputs=[methodology_title, methodology_description, methodology_test_description]
+        outputs=[
+            methodology_title,
+            methodology_description,
+            methodology_details,
+        ],
     )
 
-    button.click(
-        fn=run_evaluation,
-        inputs=[dataset_id, methodology],
-        outputs=[outputs]
-    )
+    button.click(fn=run_evaluation, inputs=[
+                 dataset, methodology], outputs=[outputs])
 
-demo.launch()
+demo.launch()