plots

app.py

@@ -1,9 +1,10 @@
 import gradio as gr
 import pandas as pd
+import matplotlib.pyplot as plt
 
 # Function to load data from a given CSV file
 def load_data(version):
-    file_path = f'versions/{version}.csv'  # Assuming filenames are version1.csv, version2.csv, version3.csv
+    file_path = f'versions/{version}.csv'  # Replace with your file paths
     return pd.read_csv(file_path)
 
 # Function for searching in the leaderboard
@@ -18,42 +19,96 @@ def change_version(version):
     new_df = load_data(version)
     return new_df
 
+# Function to create plots
+def create_plots(df, selected_methods):
+    if not selected_methods:
+        return plt.figure()  # Return an empty plot if no method is selected
+
+    filtered_df = df[df['Method'].isin(selected_methods)]
+    fig, ax = plt.subplots()
+    for method in selected_methods:
+        method_df = filtered_df[filtered_df['Method'] == method]
+        ax.plot(method_df['PPL'], label=method)  # Example: Plotting PPL, replace with your metrics
+
+    ax.set_xlabel('Index')  # Modify as per your data
+    ax.set_ylabel('PPL')  # Modify as per your data
+    ax.legend()
+    return fig
+
 # Initialize Gradio app
 demo = gr.Blocks()
 
 with demo:
     gr.Markdown("## 🥇 TOFU Leaderboard")
 
-    with gr.Row():
-        version_dropdown = gr.Dropdown(
-            choices=["llama", "phi", "stable-lm"],
-            label="🔄 Select Base Model",
-            value="llama",
-        )
-
-    with gr.Row():
-        search_bar = gr.Textbox(
-            placeholder="Search for methods...",
-            show_label=False,
-        )
-
-    leaderboard_table = gr.components.Dataframe(
-        value=load_data("llama"),  # Load initial version (version llama)
-        interactive=True,
-        visible=True,
-    )
-
-    version_dropdown.change(
-        change_version,
-        inputs=version_dropdown,
-        outputs=leaderboard_table
-    )
-
-    search_bar.change(
-        search_leaderboard,
-        inputs=[leaderboard_table, search_bar],
-        outputs=leaderboard_table
-    )
+    
+
+    with gr.Tabs():
+        with gr.TabItem("Leaderboard"):
+            with gr.Row():
+                version_dropdown = gr.Dropdown(
+                    choices=["llama", "phi", "stable-lm"],
+                    label="🔄 Select Base Model",
+                    value="llama",
+                )
+
+            with gr.Row():
+                search_bar = gr.Textbox(
+                    placeholder="Search for methods...",
+                    show_label=False,
+                )
+
+            leaderboard_table = gr.components.Dataframe(
+                value=load_data("llama"),
+                interactive=True,
+                visible=True,
+            )
+
+            version_dropdown.change(
+                change_version,
+                inputs=version_dropdown,
+                outputs=leaderboard_table
+            )
+
+            search_bar.change(
+                search_leaderboard,
+                inputs=[leaderboard_table, search_bar],
+                outputs=leaderboard_table
+            )
+
+        with gr.TabItem("Plots"):
+            version_dropdown_plots = gr.Dropdown(
+                    choices=["llama", "phi", "stable-lm"],
+                    label="🔄 Select Base Model",
+                    value="llama",
+                )
+
+            with gr.Row():
+                methods_checkbox = gr.CheckboxGroup(
+                    label="Select Methods",
+                    choices=[],  # To be populated dynamically
+                )
+
+            plot_output = gr.Plot()
+
+            # Dynamically update the choices for the methods checkbox
+            def update_method_choices(version):
+                df = load_data(version)
+                methods = df['Method'].unique()
+                methods_checkbox.update(choices=methods)
+                return df
+
+            version_dropdown_plots.change(
+                update_method_choices,
+                inputs=version_dropdown_plots,
+                outputs=[methods_checkbox, plot_output]
+            )
+
+            methods_checkbox.change(
+                create_plots,
+                inputs=[methods_checkbox, leaderboard_table],
+                outputs=plot_output
+            )
 
 # Launch the app
 demo.launch()

app_bkp.py

@@ -0,0 +1,59 @@
+import gradio as gr
+import pandas as pd
+
+# Function to load data from a given CSV file
+def load_data(version):
+    file_path = f'versions/{version}.csv'  # Assuming filenames are version1.csv, version2.csv, version3.csv
+    return pd.read_csv(file_path)
+
+# Function for searching in the leaderboard
+def search_leaderboard(df, query):
+    if query == "":
+        return df
+    else:
+        return df[df['Method'].str.contains(query)]
+
+# Function to change the version of the leaderboard
+def change_version(version):
+    new_df = load_data(version)
+    return new_df
+
+# Initialize Gradio app
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("## 🥇 TOFU Leaderboard")
+
+    with gr.Row():
+        version_dropdown = gr.Dropdown(
+            choices=["llama", "phi", "stable-lm"],
+            label="🔄 Select Base Model",
+            value="llama",
+        )
+
+    with gr.Row():
+        search_bar = gr.Textbox(
+            placeholder="Search for methods...",
+            show_label=False,
+        )
+
+    leaderboard_table = gr.components.Dataframe(
+        value=load_data("llama"),  # Load initial version (version llama)
+        interactive=True,
+        visible=True,
+    )
+
+    version_dropdown.change(
+        change_version,
+        inputs=version_dropdown,
+        outputs=leaderboard_table
+    )
+
+    search_bar.change(
+        search_leaderboard,
+        inputs=[leaderboard_table, search_bar],
+        outputs=leaderboard_table
+    )
+
+# Launch the app
+demo.launch()