app.py

import gradio as gr
import pandas as pd
from glob import glob
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import ListedColormap, BoundaryNorm
from glob import glob
import os


import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import ListedColormap, BoundaryNorm
import pandas as pd


# Load text benchmark results
noncot_results = glob("results/*.pkl")
noncot_results_qwen = glob("results_qwen/*.pkl")
# Load vision benchmark results
vision_results = glob("results-vision/*.pkl")
# Load CoT text benchmark results
cot_text_results = glob("results-cot/*.pkl")
# Load CoT vision benchmark results
# cot_vision_results = glob("results-vision-CoT/*.pkl")


# Function to load data, add model type and name
def load_data(files, model_type):
    data = []
    for file in files:
        df = pd.read_pickle(file)
        df["Model Type"] = model_type
        df["Model Name"] = file.split("/")[-1].replace(".pkl", "")
        data.append(df)
    return pd.concat(data, ignore_index=True)


# Load and label all data
data = load_data(noncot_results, "Text Only")
data_qwen = load_data(noncot_results_qwen, "Text Only")
vision_data = load_data(vision_results, "Vision")
cot_text_data = load_data(cot_text_results, "CoT Text Only")
# cot_vision_data = load_data(cot_vision_results, "CoT Vision")

# Combine all data into a single DataFrame
all_data = pd.concat([data_qwen, vision_data, cot_text_data], ignore_index=True)

all_model_names = all_data["Model Name"].unique()
all_text_only_model_names = list(
    all_data[all_data["Model Type"] == "Text Only"]["Model Name"].unique()
)
all_cot_text_only_models = list(
    all_data[all_data["Model Type"] == "CoT Text Only"]["Model Name"].unique()
)


text_only_filtered_raw = None
text_only_filtered_raw_cot = None

## Continue with the cold code --
# TODO: Update me to read from all_data for later


# Load the csv files into a dict with keys being name of the file and values being the data
data = {file: pd.read_pickle(file) for file in noncot_results}
# Load the vision files into a dict
vision_data = {file: pd.read_pickle(file) for file in vision_results}
# Load the CoT text files into a dict
cot_text_data = {file: pd.read_pickle(file) for file in cot_text_results}
# Load the CoT vision files into a dict
# cot_vision_data = {file: pd.read_pickle(file) for file in cot_vision_results}

data_qwen = {file: pd.read_pickle(file) for file in noncot_results_qwen}


intersection_df = pd.read_pickle(
    "./intersection_results/gpt-3.5-judge-by_Qwen_5times_intersection_subset_1.pkl"
)
# accuracy for each model
intersection_df_acc = (
    intersection_df.groupby("model_name")["parsed_judge_response"].mean().reset_index()
)
intersection_df_acc["Accuracy"] = intersection_df_acc["parsed_judge_response"] * 100
intersection_df_acc.drop("parsed_judge_response", axis=1, inplace=True)
intersection_df_acc.sort_values("Accuracy", ascending=False, inplace=True)


def calculate_accuracy(df):
    return df["parsed_judge_response"].mean() * 100


def accuracy_breakdown(df):
    # 4 level accuracy
    return (df.groupby("difficulty_level")["parsed_judge_response"].mean() * 100).values


# Define the column names with icons
headers_with_icons = [
    "🤖 Model Name",
    "⭐ Overall",
    "📈 Level 1",
    "🔍 Level 2",
    "📘 Level 3",
    "🔬 Level 4",
]

column_names = [
    "Model Name",
    "Overall Accuracy",
    "Level 1 Accuracy",
    "Level 2 Accuracy",
    "Level 3 Accuracy",
    "Level 4 Accuracy",
]


# Function to process data
def process_data(data):
    data_for_df = []
    for file, df in data.items():
        overall_accuracy = round(calculate_accuracy(df), 2)
        breakdown_accuracy = [round(acc, 2) for acc in accuracy_breakdown(df)]
        model_name = file.split("/")[-1].replace(".pkl", "")
        data_for_df.append([model_name, overall_accuracy] + breakdown_accuracy)
    return data_for_df


# Process all data
text_data_for_df = process_data(data)
text_data_for_df_qwen = process_data(data_qwen)

vision_data_for_df = process_data(vision_data)
cot_text_data_for_df = process_data(cot_text_data)
# cot_vision_data_for_df = process_data(cot_vision_data)

# Create DataFrames
accuracy_df = pd.DataFrame(text_data_for_df, columns=column_names)
accuracy_df_qwen = pd.DataFrame(text_data_for_df_qwen, columns=column_names)
vision_accuracy_df = pd.DataFrame(vision_data_for_df, columns=column_names)
cot_text_accuracy_df = pd.DataFrame(cot_text_data_for_df, columns=column_names)
# cot_vision_accuracy_df = pd.DataFrame(cot_vision_data_for_df, columns=column_names)


# Function to finalize DataFrame
def finalize_df(df):
    df = df.round(1)  # Round to one decimal place
    df = df.applymap(lambda x: f"{x:.1f}" if isinstance(x, (int, float)) else x)
    df.columns = headers_with_icons
    df.sort_values(by="⭐ Overall", ascending=False, inplace=True)
    return df


# Finalize all DataFrames
accuracy_df = finalize_df(accuracy_df)
accuracy_df_qwen = finalize_df(accuracy_df_qwen)
vision_accuracy_df = finalize_df(vision_accuracy_df)
cot_text_accuracy_df = finalize_df(cot_text_accuracy_df)
# cot_vision_accuracy_df = finalize_df(cot_vision_accuracy_df)


def load_heatmap(evt: gr.SelectData):
    heatmap_image = gr.Image(f"results/{evt.value}.jpg")
    return heatmap_image


def load_heatmap_qwen(evt: gr.SelectData):
    heatmap_image = gr.Image(f"results_qwen/{evt.value}.jpg")
    return heatmap_image


def load_vision_heatmap(evt: gr.SelectData):
    heatmap_image = gr.Image(f"results-vision/{evt.value}.jpg")
    return heatmap_image


def load_cot_heatmap(evt: gr.SelectData):
    heatmap_image = gr.Image(f"results-cot/{evt.value}.jpg")
    return heatmap_image


def load_cot_vision_heatmap(evt: gr.SelectData):
    heatmap_image = gr.Image(f"results-vision-CoT/{evt.value}.jpg")
    return heatmap_image


def calculate_order_by_first_substring(selected_models):
    global text_only_filtered_raw
    first_columns = all_data[all_data["substring_index"] == 1]
    query_ids_df = first_columns[first_columns["Model Type"] == "Text Only"]
    query_ids_df = query_ids_df[query_ids_df["Model Name"].isin(selected_models)]

    query_ids_df = query_ids_df.groupby("query_id").filter(
        lambda x: x["parsed_judge_response"].eq(1).all()
    )

    fsm_ids = query_ids_df.fsm_id.unique()

    text_only = all_data[all_data["Model Type"] == "Text Only"]
    text_only_filtered = text_only[text_only["fsm_id"].isin(fsm_ids)]
    text_only_filtered_raw = text_only_filtered.copy()

    query_ids = text_only_filtered.query_id.unique()
    text_only_filtered = (
        text_only_filtered.groupby(["Model Name"])["parsed_judge_response"]
        .mean()
        .reset_index()
    )

    text_only_filtered["Accuracy"] = text_only_filtered["parsed_judge_response"] * 100
    text_only_filtered.drop("parsed_judge_response", axis=1, inplace=True)

    text_only_filtered["Accuracy"] = text_only_filtered["Accuracy"].apply(
        lambda x: round(x, 2)
    )
    text_only_filtered.sort_values("Accuracy", ascending=False, inplace=True)

    number_of_queries = len(query_ids)
    number_of_fsms = len(fsm_ids)

    return text_only_filtered, number_of_queries, number_of_fsms


def calculate_order_by_first_substring_cot(selected_models):
    global text_only_filtered_raw_cot
    first_columns = all_data[all_data["substring_index"] == 1]
    query_ids_df = first_columns[first_columns["Model Type"] == "CoT Text Only"]
    query_ids_df = query_ids_df[query_ids_df["Model Name"].isin(selected_models)]

    query_ids_df = query_ids_df.groupby("query_id").filter(
        lambda x: x["parsed_judge_response"].eq(1).all()
    )

    fsm_ids = query_ids_df.fsm_id.unique()

    text_only = all_data[all_data["Model Type"] == "CoT Text Only"]
    text_only_filtered = text_only[text_only["fsm_id"].isin(fsm_ids)]
    text_only_filtered_raw_cot = text_only_filtered.copy()

    query_ids = text_only_filtered.query_id.unique()
    text_only_filtered = (
        text_only_filtered.groupby(["Model Name"])["parsed_judge_response"]
        .mean()
        .reset_index()
    )

    text_only_filtered["Accuracy"] = text_only_filtered["parsed_judge_response"] * 100
    text_only_filtered.drop("parsed_judge_response", axis=1, inplace=True)

    text_only_filtered["Accuracy"] = text_only_filtered["Accuracy"].apply(
        lambda x: round(x, 2)
    )
    text_only_filtered.sort_values("Accuracy", ascending=False, inplace=True)

    number_of_queries = len(query_ids)
    number_of_fsms = len(fsm_ids)

    return text_only_filtered, number_of_queries, number_of_fsms


def generate_heatmap_for_specific_model(model_name):
    global text_only_filtered_raw

    cmap = ListedColormap(["lightblue", "red", "green"])
    bounds = [-1.5, -0.5, 0.5, 1.5]
    norm = BoundaryNorm(bounds, cmap.N)

    model_df = text_only_filtered_raw[
        text_only_filtered_raw["Model Name"] == model_name
    ]
    model_df["fsm_info"] = model_df.apply(
        lambda x: f"{x['num_states']} states, {x['num_alphabet']} alphabet", axis=1
    )
    model_df = model_df.sort_values(by=["num_states", "num_alphabet"])

    pivot_df = (
        model_df.pivot_table(
            index="fsm_info",
            columns="substring_index",
            values="parsed_judge_response",
            aggfunc="first",
        )
        .fillna(-1)
        .astype(float)
    )

    # Dynamically adjust figure size
    num_rows, num_cols = pivot_df.shape
    fig_width = max(12, num_cols * 0.5)  # Adjust width per column
    fig_height = max(8, num_rows * 0.4)  # Adjust height per row

    fig, ax = plt.subplots(figsize=(fig_width, fig_height))
    sns.heatmap(
        pivot_df,
        cmap=cmap,
        linewidths=1,
        linecolor="black",
        norm=norm,
        cbar=False,
        square=True,
        ax=ax,
    )
    plt.title(f"Heatmap for Model: {model_name}", fontsize=12)
    plt.xlabel("Substring Index")
    plt.ylabel("FSM (States, Alphabet)")
    plt.xticks(rotation=45)

    sns.despine(ax=ax, top=True, right=True, left=True, bottom=True)

    return fig


def generate_heatmap_for_specific_model_cot(model_name):
    global text_only_filtered_raw_cot

    cmap = ListedColormap(["lightblue", "red", "green"])
    bounds = [-1.5, -0.5, 0.5, 1.5]
    norm = BoundaryNorm(bounds, cmap.N)

    model_df = text_only_filtered_raw_cot[
        text_only_filtered_raw_cot["Model Name"] == model_name
    ]
    model_df["fsm_info"] = model_df.apply(
        lambda x: f"{x['num_states']} states, {x['num_alphabet']} alphabet", axis=1
    )
    model_df = model_df.sort_values(by=["num_states", "num_alphabet"])

    pivot_df = (
        model_df.pivot_table(
            index="fsm_info",
            columns="substring_index",
            values="parsed_judge_response",
            aggfunc="first",
        )
        .fillna(-1)
        .astype(float)
    )

    # Dynamically adjust figure size
    num_rows, num_cols = pivot_df.shape
    fig_width = max(12, num_cols * 0.5)  # Adjust width per column
    fig_height = max(8, num_rows * 0.4)  # Adjust height per row

    fig, ax = plt.subplots(figsize=(fig_width, fig_height))
    sns.heatmap(
        pivot_df,
        cmap=cmap,
        linewidths=1,
        linecolor="black",
        norm=norm,
        cbar=False,
        square=True,
        ax=ax,
    )
    plt.title(f"Heatmap for Model: {model_name}", fontsize=12)
    plt.xlabel("Substring Index")
    plt.ylabel("FSM (States, Alphabet)")
    plt.xticks(rotation=45)

    sns.despine(ax=ax, top=True, right=True, left=True, bottom=True)

    return fig


def generate_heatmap_for_intersection_model(model_name):
    global intersection_df

    cmap = ListedColormap(["lightblue", "red", "green"])
    bounds = [-1.5, -0.5, 0.5, 1.5]
    norm = BoundaryNorm(bounds, cmap.N)

    # Filter for a specific model
    model_df = intersection_df[intersection_df["model_name"] == model_name].copy()

    if model_df.empty:
        print(f"No data found for model {model_name}. Skipping heatmap generation.")
        return None

    model_df["fsm_info"] = model_df.apply(
        lambda x: f"{x['num_states']} states, {x['num_alphabet']} alphabet", axis=1
    )
    model_df = model_df.sort_values(by=["num_states", "num_alphabet"])

    pivot_df = (
        model_df.pivot_table(
            index="fsm_info",
            columns="substring_index",
            values="parsed_judge_response",
            aggfunc="first",
        )
        .fillna(-1)
        .astype(float)
    )

    # Dynamically adjust figure size
    num_rows, num_cols = pivot_df.shape
    fig_width = max(12, num_cols * 0.5)
    fig_height = max(8, num_rows * 0.4)

    fig, ax = plt.subplots(figsize=(fig_width, fig_height))
    sns.heatmap(
        pivot_df,
        cmap=cmap,
        linewidths=1,
        linecolor="black",
        norm=norm,
        cbar=False,
        square=True,
        ax=ax,
    )
    plt.title(f"Heatmap for Model: {model_name}", fontsize=12)
    plt.xlabel("Substring Index")
    plt.ylabel("FSM (States, Alphabet)")
    plt.xticks(rotation=45)

    sns.despine(ax=ax, top=True, right=True, left=True, bottom=True)

    plt.close(fig)
    return fig


def show_constraint_heatmap(evt: gr.SelectData):
    model_name = evt.value
    return generate_heatmap_for_specific_model(model_name)


def show_constraint_heatmap_cot(evt: gr.SelectData):
    model_name = evt.value
    return generate_heatmap_for_specific_model_cot(model_name)


def show_intersection_heatmap(evt: gr.SelectData):
    model_name = evt.value
    return generate_heatmap_for_intersection_model(model_name)


with gr.Blocks() as demo:
    gr.Markdown("# FSM Benchmark Leaderboard")

    with gr.Tab("Text-only Benchmark"):
        gr.Markdown("# Text-only Leaderboard (Judged by Qwen)")
        leader_board = gr.Dataframe(accuracy_df_qwen, headers=headers_with_icons)
        gr.Markdown("## Heatmap")
        heatmap_image_qwen = gr.Image(label="", show_label=False)
        leader_board.select(fn=load_heatmap_qwen, outputs=[heatmap_image_qwen])

    with gr.Tab("Vision Benchmark", visible=False):
        gr.Markdown("# Vision Benchmark Leaderboard")
        leader_board_vision = gr.Dataframe(
            vision_accuracy_df, headers=headers_with_icons
        )
        gr.Markdown("## Heatmap")
        heatmap_image_vision = gr.Image(label="", show_label=False)
        leader_board_vision.select(
            fn=load_vision_heatmap, outputs=[heatmap_image_vision]
        )

    with gr.Tab("Text-only Benchmark (CoT)", visible=False):
        gr.Markdown("# Text-only Leaderboard (CoT)")
        cot_leader_board_text = gr.Dataframe(
            cot_text_accuracy_df, headers=headers_with_icons
        )
        gr.Markdown("## Heatmap")
        cot_heatmap_image_text = gr.Image(label="", show_label=False)
        cot_leader_board_text.select(
            fn=load_cot_heatmap, outputs=[cot_heatmap_image_text]
        )

    # with gr.Tab("Vision Benchmark (CoT)"):
    #     gr.Markdown("# Vision Benchmark Leaderboard (CoT)")
    #     cot_leader_board_vision = gr.Dataframe(
    #         cot_vision_accuracy_df, headers=headers_with_icons
    #     )
    #     gr.Markdown("## Heatmap")
    #     cot_heatmap_image_vision = gr.Image(label="", show_label=False)
    #     cot_leader_board_vision.select(
    #         fn=load_cot_vision_heatmap, outputs=[cot_heatmap_image_vision]
    #     )

    with gr.Tab("Constraint Text-only Results"):
        gr.Markdown("## Constraint Text-only Leaderboard by first substring")
        included_models = gr.CheckboxGroup(
            label="Models to include",
            choices=all_text_only_model_names,
            value=all_text_only_model_names,
            interactive=True,
        )
        with gr.Row():
            number_of_queries = gr.Textbox(label="Number of included queries")

            number_of_fsms = gr.Textbox(label="Number of included  FSMs")

        constrained_leader_board_text = gr.Dataframe()
        constrained_leader_board_plot = gr.Plot()

    included_models.select(
        fn=calculate_order_by_first_substring,
        inputs=[included_models],
        outputs=[constrained_leader_board_text, number_of_queries, number_of_fsms],
        queue=True,
    )

    with gr.Tab("Constraint Text-only Results (CoT)", visible=False):
        gr.Markdown("## Constraint Text-only Leaderboard by first substrin (CoT)")
        included_models_cot = gr.CheckboxGroup(
            label="Models to include",
            choices=all_cot_text_only_models,
            value=all_cot_text_only_models,
            interactive=True,
        )
        with gr.Row():
            number_of_queries_cot = gr.Textbox(label="Number of included queries")
            number_of_fsms_cot = gr.Textbox(label="Number of included  FSMs")

        constrained_leader_board_text_cot = gr.Dataframe()
        constrained_leader_board_plot_cot = gr.Plot()

    with gr.Tab("Majority Vote (Subset 1)", visible=False):
        gr.Markdown("## Majority Vote (Subset 1)")
        intersection_leader_board = gr.Dataframe(
            intersection_df_acc, headers=headers_with_icons
        )
        heatmap_image = gr.Plot(label="Model Heatmap")

    with gr.Tab("Text-only Benchmark (deprecated)", visible=False):
        gr.Markdown("# Text-only Leaderboard")
        leader_board = gr.Dataframe(accuracy_df, headers=headers_with_icons)
        gr.Markdown("## Heatmap")
        heatmap_image = gr.Image(label="", show_label=False)
        leader_board.select(fn=load_heatmap, outputs=[heatmap_image])

    # ============ Callbacks ============

    included_models_cot.select(
        fn=calculate_order_by_first_substring_cot,
        inputs=[included_models_cot],
        outputs=[
            constrained_leader_board_text_cot,
            number_of_queries_cot,
            number_of_fsms_cot,
        ],
        queue=True,
    )

    constrained_leader_board_text.select(
        fn=show_constraint_heatmap, outputs=[constrained_leader_board_plot]
    )

    constrained_leader_board_text_cot.select(
        fn=show_constraint_heatmap_cot, outputs=[constrained_leader_board_plot_cot]
    )

    intersection_leader_board.select(
        fn=show_intersection_heatmap, outputs=[heatmap_image]
    )

    demo.launch()