Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import argparse
+
+def make_default_md():
+    leaderboard_md = f"""
+    # 🏆 LLms Benchmark
+    
+    The main goal of this project is to utilize Large Language Models (LLMs) to extract specific information from PDF documents and organize it into a structured JSON format.
+    
+    To achieve this objective, we are assessing various LLMs on two benchmarks:
+    
+    1. [Benchmark1](https://huggingface.co/spaces/Nechba/LLms-Benchmark/blob/main/dataset.jsonl): 
+    This benchmark consists of a dataset of 59 pages as context and corresponding JSON extracts from "Interchange and Service Fees Manual: Europe Region".
+    
+    2. [Benchmark2](https://huggingface.co/datasets/Effyis/Table-Extraction): 
+    This benchmark comprises a dataset of 16573 tables as context and corresponding JSON extracts.
+    """
+    return leaderboard_md
+
+
+def make_arena_leaderboard_md(total_models):
+    leaderboard_md = f"""
+Total #models: **{total_models}**. Last updated: Juin 01, 2024.
+
+"""
+    return leaderboard_md
+
+def model_hyperlink(model_name, link):
+    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
+
+def load_leaderboard_table_csv(filename, add_hyperlink=True):
+    rows = []
+    with open(filename, 'r') as file:
+        lines = file.readlines()
+        heads = [v.strip() for v in lines[0].split(",")]
+        for line in lines[1:]:
+            row = [v.strip() for v in line.split(",")]
+            item = {}
+            for h, v in zip(heads, row):
+                item[h] = v
+            if add_hyperlink:
+                item["Model"] = model_hyperlink(item["Model"], item["Link"])
+                item["Notebook link"] = model_hyperlink("Notebook", item["Notebook link"])
+            rows.append(item)
+    return rows
+
+def get_arena_table(model_table_df):
+    # change type Percentage of values column of df
+    model_table_df["Percentage of values"] = model_table_df["Percentage of values"].astype(float)
+    model_table_df["Percentage of keys"] = model_table_df["Percentage of keys"].astype(float)
+    model_table_df["Average time (s)"] = model_table_df["Average time (s)"].astype(float)
+    arena_df = model_table_df.sort_values(by=["Percentage of values"], ascending=False)
+    values = []
+    if not arena_df.empty:  # Check if arena_df is not empty
+        for i in range(len(arena_df)):
+            row = []
+            model_name = arena_df["Model"].values[i]  # Access model name directly without index 0
+            row.append(model_name)
+            row.append(arena_df.iloc[i]["Percentage of values"])
+            row.append(arena_df.iloc[i]["Percentage of keys"])
+            row.append(arena_df.iloc[i]["Average time (s)"])
+            row.append(arena_df.iloc[i]["Notebook link"])
+            row.append(arena_df.iloc[i]["License"])
+            # row.append(arena_df.iloc[i]["Link"])
+            values.append(row)
+    return values
+
+
+
+def build_leaderboard_tab(leaderboard_table_file1, leaderboard_table_file2, show_plot=False):
+    default_md = make_default_md()
+    md_1 = gr.Markdown(default_md, elem_id="leaderboard_markdown")
+    
+    if leaderboard_table_file1:
+        data1 = load_leaderboard_table_csv(leaderboard_table_file1)
+        model_table_df1 = pd.DataFrame(data1)
+        data2 = load_leaderboard_table_csv(leaderboard_table_file2)
+        model_table_df2 = pd.DataFrame(data2)
+        
+        with gr.Tabs() as tabs:
+            with gr.Tab(" 🏅 Benchmark 1", id=0):
+                arena_table_vals = get_arena_table(model_table_df1)
+                md = make_arena_leaderboard_md(len(arena_table_vals))
+                gr.Markdown(md, elem_id="leaderboard_markdown")
+                
+                # Remove height argument
+                gr.Dataframe(
+                    headers=[
+                        "Model",
+                        "Percentage of values (%)",
+                        "Percentage of keys (%)",
+                        "Average time (s)",
+                        "Code",
+                        "License",
+                    ],
+                    datatype=[
+                        "markdown",
+                        "number",
+                        "number",
+                        "number",
+                        "markdown",
+                        "str"
+                    ],
+                    value=arena_table_vals,
+                    elem_id="arena_leaderboard_dataframe",
+                    column_widths=[200, 150, 150, 130, 100, 140],
+                    wrap=True,
+                )
+
+                # Display additional Markdown notes as needed...
+                
+            with gr.Tab("🏅 Benchmark 2", id=1):
+                arena_table_vals = get_arena_table(model_table_df2)
+                md = make_arena_leaderboard_md(len(arena_table_vals))
+                gr.Markdown(md, elem_id="leaderboard_markdown")
+
+                # Remove height argument
+                gr.Dataframe(
+                    headers=[
+                        "Model",
+                        "Percentage of values (%)",
+                        "Percentage of keys (%)",
+                        "Average time (s)",
+                        "Code",
+                        "License",
+                    ],
+                    datatype=[
+                        "markdown",
+                        "number",
+                        "number",
+                        "number",
+                        "markdown",
+                        "str"
+                    ],
+                    value=arena_table_vals,
+                    elem_id="arena_leaderboard_dataframe",
+                    column_widths=[200, 150, 150, 130, 100, 140],
+                    wrap=True,
+                )
+    else:
+        pass
+    
+    return [md_1, plot_1, plot_2]
+
+block_css = """
+#notice_markdown {
+    font-size: 104%
+}
+#notice_markdown th {
+    display: none;
+}
+#notice_markdown td {
+    padding-top: 6px;
+    padding-bottom: 6px;
+}
+#leaderboard_markdown {
+    font-size: 104%
+}
+#leaderboard_markdown td {
+    padding-top: 6px;
+    padding-bottom: 6px;
+}
+#leaderboard_dataframe td {
+    line-height: 0.1em;
+}
+footer {
+    display:none !important
+}
+.sponsor-image-about img {
+    margin: 0 20px;
+    margin-top: 20px;
+    height: 40px;
+    max-height: 100%;
+    width: auto;
+    float: left;
+}
+"""
+
+def build_demo(leaderboard_table_file1, leaderboard_table_file2):
+    text_size = gr.themes.sizes.text_lg
+    with gr.Blocks(
+        title="LLMS Benchmark",
+        theme=gr.themes.Base(text_size=text_size),
+        css=block_css,
+    ) as demo:
+        leader_components = build_leaderboard_tab(
+            leaderboard_table_file1,leaderboard_table_file2, show_plot=True
+        )
+    return demo
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--share", action="store_true")
+    args = parser.parse_args()
+
+    leaderboard_table_file1 = "./Benchmark1/leaderboard.csv"
+    leaderboard_table_file2 = "./Benchmark2/leaderboard.csv"
+    demo = build_demo(leaderboard_table_file1,leaderboard_table_file2)
+    demo.launch(share=args.share)