evaluation_dashboard.py

"""
Evaluation Results Dashboard for Multilingual News Article Summarizer
"""

import streamlit as st
import pandas as pd
import math
from typing import Dict, Any


def load_evaluation_data():
    """Load evaluation results from CSV files."""
    try:
        # Load English results
        df_en = pd.read_csv("evaluation_results_en.csv")

        # Load French results
        df_fr = pd.read_csv("evaluation_results_fr.csv")

        return df_en, df_fr
    except FileNotFoundError as e:
        st.error(f"Could not load evaluation files: {e}")
        return None, None


def display_summary_metrics(df: pd.DataFrame, title: str):
    """Display summary metrics in a highlighted card format."""
    # Get summary row (last row with sample_id = 'SUMMARY')
    summary_row = (
        df[df["sample_id"] == "SUMMARY"].iloc[0]
        if len(df[df["sample_id"] == "SUMMARY"]) > 0
        else None
    )

    if summary_row is not None:
        st.markdown(f"### 📊 {title} - Summary Results")

        # Create metrics columns with ample spacing
        col1, col2, col3 = st.columns([1, 1, 1])

        with col1:
            st.metric(
                label="ROUGE-1", value=f"{summary_row['rouge1_f']:.4f}", delta=None
            )

        with col2:
            st.metric(
                label="ROUGE-2", value=f"{summary_row['rouge2_f']:.4f}", delta=None
            )

        with col3:
            st.metric(
                label="ROUGE-L", value=f"{summary_row['rougeL_f']:.4f}", delta=None
            )

        st.markdown("---")


def display_paginated_table(
    df: pd.DataFrame, columns_to_show: list, page_size: int = 15
):
    """Display a paginated table with the specified columns."""
    # Filter out summary row for the detailed table
    df_filtered = df[df["sample_id"] != "SUMMARY"].copy()
    df_display = df_filtered[columns_to_show].copy()

    # Rename columns for better display
    column_rename = {
        "sample_id": "Sample ID",
        "rouge1_f": "ROUGE-1",
        "rouge2_f": "ROUGE-2",
        "rougeL_f": "ROUGE-L",
        "reference_summary": "Reference Summary",
        "generated_summary": "Generated Summary",
    }
    df_display = df_display.rename(columns=column_rename)

    # Format ROUGE scores to 4 decimal places
    for col in ["ROUGE-1", "ROUGE-2", "ROUGE-L"]:
        if col in df_display.columns:
            df_display[col] = df_display[col].apply(lambda x: f"{x:.4f}")

    # Calculate pagination
    total_rows = len(df_display)
    total_pages = math.ceil(total_rows / page_size)

    if total_pages > 1:
        # Page selector
        col1, col2, col3 = st.columns([1, 2, 1])
        with col2:
            page = st.selectbox(
                "Select Page",
                range(1, total_pages + 1),
                format_func=lambda x: f"Page {x} of {total_pages}",
                key="page_selector",
            )
    else:
        page = 1

    # Calculate start and end indices
    start_idx = (page - 1) * page_size
    end_idx = min(start_idx + page_size, total_rows)

    # Display page info
    st.caption(f"Showing rows {start_idx + 1}-{end_idx} of {total_rows}")

    # Display the table
    df_page = df_display.iloc[start_idx:end_idx]
    st.dataframe(
        df_page,
        use_container_width=True,
        hide_index=True,
        column_config={
            "Reference Summary": st.column_config.TextColumn(
                width="medium", help="Ground truth summary from the dataset"
            ),
            "Generated Summary": st.column_config.TextColumn(
                width="medium", help="Summary generated by our model"
            ),
            "ROUGE-1": st.column_config.NumberColumn(
                help="ROUGE-1 F1 score", format="%.4f"
            ),
            "ROUGE-2": st.column_config.NumberColumn(
                help="ROUGE-2 F1 score", format="%.4f"
            ),
            "ROUGE-L": st.column_config.NumberColumn(
                help="ROUGE-L F1 score", format="%.4f"
            ),
        },
    )


def display_benchmark_table():
    """Display the official Pegasus benchmark results."""
    st.markdown("### 🏆 Official Google Pegasus Benchmark Results")
    st.markdown("*ROUGE scores in format: ROUGE-1/ROUGE-2/ROUGE-L*")

    # Create the benchmark data
    benchmark_data = {
        "Dataset": ["xsum", "cnn_dailymail", "newsroom", "multi_news", "gigaword"],
        "C4": [
            "45.20/22.06/36.99",
            "43.90/21.20/40.76",
            "45.07/33.39/41.28",
            "46.74/17.95/24.26",
            "38.75/19.96/36.14",
        ],
        "HugeNews": [
            "47.21/24.56/39.25",
            "44.17/21.47/41.11",
            "45.15/33.51/41.33",
            "47.52/18.72/24.91",
            "39.12/19.86/36.24",
        ],
        "Mixed & Stochastic": [
            "47.60/24.83/39.64",
            "44.16/21.56/41.30",
            "45.98/34.20/42.18",
            "47.65/18.75/24.95",
            "39.65/20.47/36.76",
        ],
    }

    df_benchmark = pd.DataFrame(benchmark_data)

    st.dataframe(
        df_benchmark,
        use_container_width=True,
        hide_index=True,
        column_config={
            "Dataset": st.column_config.TextColumn(
                width="medium", help="Evaluation dataset"
            ),
            "C4": st.column_config.TextColumn(
                width="medium", help="C4 pre-training configuration"
            ),
            "HugeNews": st.column_config.TextColumn(
                width="medium", help="HugeNews pre-training configuration"
            ),
            "Mixed & Stochastic": st.column_config.TextColumn(
                width="medium", help="Mixed & Stochastic pre-training configuration"
            ),
        },
    )


def show_evaluation_page():
    """Evaluation dashboard page function for navigation."""
    st.title("📊 Evaluation Results Dashboard")
    st.markdown(
        "*Comprehensive evaluation results for the Multilingual News Article Summarizer*"
    )
    st.markdown("---")

    # Load data
    df_en, df_fr = load_evaluation_data()

    if df_en is None or df_fr is None:
        st.error(
            "⚠️ Could not load evaluation data. Please ensure evaluation CSV files are present."
        )
        return  # Create selection dropdown
    st.markdown("### 🔍 Select Evaluation Results to View")

    # Use dropdown for clean, modern selection
    option = st.selectbox(
        "Choose an option:",
        [
            "🏆 Official Google Pegasus Benchmark Results",
            "🇺🇸 Our English Evaluation (CNN/DailyMail)",
            "🇫🇷 Our French Evaluation (MLSUM)",
        ],
        index=0,
        key="evaluation_option",
    )

    st.markdown("---")  # Display content based on selection
    if option == "🏆 Official Google Pegasus Benchmark Results":
        display_benchmark_table()

        # Page-specific disclaimer
        st.markdown("---")
        st.info(
            "📖 **Additional Information**: For more details about the Pegasus model, visit the [official HuggingFace model page](https://huggingface.co/google/pegasus-cnn_dailymail)."
        )

    elif option == "🇺🇸 Our English Evaluation (CNN/DailyMail)":
        # Display summary metrics first
        display_summary_metrics(df_en, "English (CNN/DailyMail)")

        # Display detailed results
        st.markdown("### 📝 Detailed Sample Results")
        columns_to_show = [
            "sample_id",
            "rouge1_f",
            "rouge2_f",
            "rougeL_f",
            "reference_summary",
            "generated_summary",
        ]
        display_paginated_table(df_en, columns_to_show)

        # Page-specific disclaimer
        st.markdown("---")
        st.warning(
            "⚠️ **Disclaimer**: ROUGE scores shown are based on a small test set of 25 articles per dataset, due to time and computational constraints. These results are indicative but not fully representative. Performance is expected to improve with larger, more comprehensive test sets."
        )
        st.info(
            "📖 **Dataset Information**: For more details about the CNN/DailyMail dataset used in this evaluation, visit the [official dataset page](https://huggingface.co/datasets/abisee/cnn_dailymail)."
        )

    elif option == "🇫🇷 Our French Evaluation (MLSUM)":
        # Display summary metrics first
        display_summary_metrics(df_fr, "French (MLSUM)")

        # Display detailed results
        st.markdown("### 📝 Detailed Sample Results")
        columns_to_show = [
            "sample_id",
            "rouge1_f",
            "rouge2_f",
            "rougeL_f",
            "reference_summary",
            "generated_summary",
        ]
        display_paginated_table(df_fr, columns_to_show)  # Page-specific disclaimer
        st.markdown("---")
        st.warning(
            "⚠️ **Disclaimer**: Evaluations for non-English summaries (e.g., French) tend to be lower than for English ones primarily due to cascading errors introduced during the machine translation step. Our translation model, while generally good, is a distilled, research-focused version and not intended for production deployment. This means it can struggle with the nuances of news articles, introducing inaccuracies or losing subtle context. These translation imperfections are then amplified when fed into the English-optimized summarization model, often resulting in less precise content in the final summary. For more details about the translation model limitations and specifications, visit the [NLLB-200 distilled model page](https://huggingface.co/facebook/nllb-200-distilled-600M)."
        )
        st.info(
            "📖 **Dataset Information**: For more details about the MLSUM dataset used in this evaluation, visit the [official dataset page](https://huggingface.co/datasets/reciTAL/mlsum)."
        )


# For backwards compatibility when run directly
def main():
    """Main function for backwards compatibility."""
    st.set_page_config(
        page_title="Evaluation Results Dashboard", page_icon="📊", layout="wide"
    )
    show_evaluation_page()


if __name__ == "__main__":
    main()