import pandas as pd
import plotly.express as px
import gradio as gr


data_path = '0926-OCRBench-opensource.csv'
data_mmlm_path = 'filtered_opencompass.csv'
data = pd.read_csv(data_path).fillna(0)

######## OCRBench ########
# set the data types for the columns
dtype_dict = {
    "Model": str,
    "Param (B)": float,
    "OCRBench":int,
    "Text Recognition":int,
    "Scene Text-centric VQA":int,
    "Document Oriented VQA":int,
    "KIE":int,
    "Handwritten Math Expression Recognition":int}


# preprocess the dataframe
data_valid = data[:25].copy()
data_valid = data_valid.astype(dtype_dict)
data_valid.drop(columns=['Unnamed: 11'], inplace=True)

# Add a new column that assigns categories to Model A, Model B, and Model C, and 'Other' to the rest
def categorize_model(model):
    if model in ["H2OVL-Mississippi-2B", "H2OVL-Mississippi-1B"]:
        return "H2OVLs"
    elif model.startswith("doctr"):  # Third group for ocr models
        return "traditional ocr models"
    else:
        return "Other"
    

# Apply the categorization to create a new column
data_valid["Category"] = data_valid["Model"].apply(categorize_model) 



# ploting 
def plot_metric(selected_metric):
    filtered_data = data_valid[data_valid[selected_metric] !=0 ]
    
    # Create the scatter plot with different colors for "Special" and "Other"
    fig = px.scatter(
        filtered_data,
        x="Param (B)",
        y=selected_metric,
        text="Model",
        color="Category",  # Different color for Special and Other categories
        title=f"{selected_metric} vs Model Size"
    )
    
    fig.update_traces(marker=dict(size=10), mode='markers+text', textposition="middle right", textfont=dict(size=10))
    # Extend the x-axis range
    max_x_value = filtered_data["Param (B)"].max()
    fig.update_layout(
        xaxis_range=[0, max_x_value + 5],  # Extend the x-axis range to give more space for text
        xaxis_title="Model Size (B)",
        yaxis_title=selected_metric,
        showlegend=False,
        height=800,
        margin=dict(t=50, l=50, r=100, b=50),  # Increase right margin for more space
    )
    
    # Use texttemplate to ensure full model name is displayed
    fig.update_traces(texttemplate='%{text}')

    return fig


####### OpenCompass ########
data_mmlm = pd.read_csv(data_mmlm_path).fillna(0)
data_mmlm.rename(columns={"Avg. Score (8 single image benchmarks)": "Average Score"}, inplace=True)
metrics_column = list(data_mmlm.columns)[6:]

def plot_metric_mmlm_grouped(category):
    # Filter the data based on the selected category
    filtered_data = data_mmlm[data_mmlm["Category"] == category].copy()
    
    # Melt the dataframe to have a "Metric" column and a "Score" column
    melted_data = pd.melt(
        filtered_data,
        id_vars=["Models"],  # Keep the Model column as identifier
        value_vars=metrics_column,  # Melt all the metric columns
        var_name="Metrics",  # Name for the new column containing metrics
        value_name="Score"  # Name for the new column containing scores
    )
    
    # Generate a grouped bar chart
    fig = px.bar(
        melted_data,
        x="Metrics",
        y="Score",
        color="Models",  # Differentiate metrics by color
        barmode="group",  # Grouped bars
        title=f"Scores for All Metrics in {category} Category"
    )
    
    fig.update_layout(
        xaxis_title="Metrics",
        yaxis_title="Score",
        height=600,
        margin=dict(t=50, l=50, r=100, b=50),
    )
    return fig


# Gradio Blocks Interface with Tabs
def create_interface():
    with gr.Blocks() as interface:
        with gr.Tabs():
            with gr.Tab("OCRBench"):
                with gr.Row():
                    with gr.Column(scale=4):  # Column for the plot (takes 4 parts of the total space)
                        plot = gr.Plot(value=plot_metric("OCRBench"), label="OCR Benchmark Metrics")  # default plot component initially
                    with gr.Column(scale=1):  # Column for the dropdown (takes 1 part of the total space)
                        metrics = list(data_valid.columns[5:-1])  # List of metric columns (excluding 'Model' and 'Parameter Size')
                        dropdown = gr.Dropdown(metrics, label="Select Metric", value="OCRBench")
                        
                # Update the plot when dropdown selection changes
                dropdown.change(fn=plot_metric, inputs=dropdown, outputs=plot)
                
            
            with gr.Tab("8 Multi-modal Benchmarks"):
                with gr.Row():
                    # Dropdown for selecting the category
                    categories = data_mmlm["Category"].unique().tolist()
                    category_dropdown = gr.Dropdown(categories, label="Select Category", value=categories[0])
                
                with gr.Row():
                    mm_plot = gr.Plot(value=plot_metric_mmlm_grouped(categories[0]), label="Grouped Metrics for Models")
                
                # Update the plot based on category dropdown changes
                category_dropdown.change(fn=plot_metric_mmlm_grouped, inputs=category_dropdown, outputs=mm_plot)

    return interface

# Launch the interface
if __name__ == "__main__":
    create_interface().launch()