Update Website

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README.md

@@ -1,13 +1,13 @@
----
-title: GEO Bench
-emoji: 📚
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: GEO Bench
+emoji: 📚
+colorFrom: purple
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.50.2
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,255 +1,255 @@
-import gradio as gr
-import pandas as pd
-import os
-import itertools
-from constants import metric_dict, tags, columns
-
-# Download from github and load the data
-
-# TODO: Download every x hours
-def download_data(url = "https://github.com/Pranjal2041/GEO/GEO-Bench/leaderboard/leaderboard.jsonl", path = "leaderboard.jsonl"):
-    ret_code = os.system(f'wget {url} -O {path}_tmp')
-    if ret_code != 0:
-        return ret_code
-    os.system(f'mv {path}_tmp {path}')
-    return 0
-
-def search_leaderboard(df, queries):
-    # Assuming DATA_OVERALL is the DataFrame containing the leaderboard data
-    # filtered_data = df[df["Method"].str.contains(query, case=False, na=False)]
-    temp_pds = []
-    for query in queries:
-        temp_pds.append(df[df["Method"].str.contains(query, case=False, na=False)])
-    return pd.concat(temp_pds).drop_duplicates()
- 
-def search_tags_leaderboard(df, tag_blocks, queries):
-    return search_leaderboard(filter_tags(df, tag_blocks), queries)
-
-def filter_tags(df, tag_blocks):
-    def fuzzy_in(x, y_set):
-        return any(x in z for z in y_set)
-    all_tags_sets = [set(tag.lower() for tag in tag_block) for tag_block in tag_blocks]
-
-    filtered_rows = [i for i, tags in enumerate(complete_dt['tags']) if all('any' in tag_set or any(fuzzy_in(tag.lower(), tag_set) for tag in tags) for tag_set in all_tags_sets)]
-
-    return prepare_complete_dt(df.iloc[filtered_rows])
-
-def prepare_complete_dt(complete_dt):
-    data = []
-    DATA_OVERALL = complete_dt.copy()
-    for Method in set(complete_dt['Method']):
-        data.append([])
-        data[-1].append(Method)
-        for metric in metric_dict:
-            metric_val = metric_dict[metric]
-            data[-1].append(complete_dt[complete_dt['Method'] == Method][metric_val].mean())
-        data[-1].append(complete_dt[complete_dt['Method'] == Method]['source'].iloc[0])
-        DATA_OVERALL = pd.DataFrame(data, columns=columns)
-    try:
-        DATA_OVERALL.sort_values(by=['WordPos Overall'], inplace=True, ascending=False)
-    except: ...
-    return DATA_OVERALL
-
-def format_df_for_leaderboard(df):
-    # The source column needs to be embedded directly into the Method column using appropriate markdown.
-    df['Method'] = df[['source', 'Method']].apply(lambda x: f'<a target="_blank" style="text-decoration: underline; color: #3571d7;" href="{x[0]}">{x[1]}</a>', axis=1)
-    # Convert all float metrics to 1 decimal
-    df_copy = df.copy()
-    for metric in metric_dict:
-        df_copy[metric] = df_copy[metric].apply(lambda x: float(f'{(100*x):.1f}'))
-    # drop the source column
-    return df_copy.drop(columns=['source'])
-
-
-ret_code = 0
-# ret_code = download_data()
-if ret_code != 0:
-    print("Leaderboard Download failed")
-
-complete_dt = pd.read_json('leaderboard.jsonl', lines=True, orient='records')
-DATA_OVERALL = prepare_complete_dt(complete_dt)
-
-
-with gr.Blocks() as demo:
-    
-    demo_content = """
-<style>
-  .badge-container {
-    text-align: center;
-    display: flex;
-    justify-content: center;
-  }
-  .badge {
-    margin: 1px;
-  }
-</style>
-<h1 style="text-align: center;">GEO-Bench Leaderboard</h1>
-<div class="badge-container">
-    <a href="https://pranjal2041.github.io/geo/" class="badge">
-        <img src="https://img.shields.io/website?down_message=down&style=for-the-badge&up_message=up&url=https%3A%2F%2Fpranjal2041.github.io/geo/" alt="Website">
-    </a>
-    <a href="https://arxiv.org/abs/2310.18xxx" class="badge">
-        <img src="https://img.shields.io/badge/arXiv-2310.18xxx-red.svg?style=for-the-badge" alt="Arxiv Paper">
-    </a>
-    <a href="https://huggingface.co/datasets/Pranjal2041/geo-bench" class="badge">
-        <img src="https://img.shields.io/badge/Dataset-GEO-%2DBENCH-orange?style=for-the-badge" alt="Dataset">
-    </a>
-    <a href="https://github.com/Pranjal2041/GEO" class="badge">
-        <img src="https://img.shields.io/badge/Github-Code-green?style=for-the-badge" alt="Code">
-    </a>
-</div>
-<p>
-    - For benchmarking content optimization Methods for Generative Engines.<br>
-    - GEO-Bench evaluates Methods for optimizing website content to improve visibility in generative engine responses. Benchmark contains 10K queries across 9 datasets covering diverse domains and intents.<br>
-    - Refer to GEO paper for more <a href="https://arxiv.org/abs/2310.18xxx">details</a>
-</p>
-"""
-
-
-    gr.HTML(demo_content)
-
-
-
-
-    with gr.Tabs():
-
-        with gr.TabItem('Overall 📊'):
-
-            with gr.Row():
-                gr.Markdown('## Overall Leaderboard')
-            
-            with gr.Row():
-                data_overall = gr.components.Dataframe(
-                    format_df_for_leaderboard(DATA_OVERALL),
-                    datatype=["markdown"] + ["number"] * (len(DATA_OVERALL.columns) - 2) + ['markdown'],
-                    type="pandas",
-                    wrap=True,
-                    interactive=False,
-                )
-                # data_overall.
-        
-            with gr.Row():
-                # search_bar = gr.Textbox(type="text", label="Search for a Method:")
-                search_bar = gr.Textbox(
-                    placeholder=" 🔍 Search for your Method (separate multiple queries with `,`) and press ENTER...",
-                    show_label=False,
-                    elem_id="search-bar",
-                )
-
-                def search_button_click(query):
-                    filtered_data = search_leaderboard(DATA_OVERALL, [x.strip() for x in query.split(',')])
-                    return format_df_for_leaderboard(filtered_data)
-                
-        with gr.TabItem('Tag-Wise Results 📊'):
-            with gr.Row():
-                gr.Markdown(f"""
-                ## Tag-Wise Results
-                - The following table shows the results for each tag.
-                - The tags are sorted in the order of their performance.
-                - The table is sorted in the order of the overall score.
-                """)
-            with gr.Row():
-
-                search_bar_tag = gr.Textbox(
-                    placeholder=" 🔍 Search for your Method (separate multiple queries with `,`) and press ENTER...",
-                    show_label=False,
-                    elem_id="search-bar",
-                )
-
-                def search_button_click(query):
-                    filtered_data = search_leaderboard(DATA_OVERALL, [x.strip() for x in query.split(',')])
-                    return format_df_for_leaderboard(filtered_data)
-
-            with gr.Row():
-                boxes = dict()
-                with gr.Column(min_width=320):
-                    for tag in list(tags.keys())[:3]:
-                        with gr.Box(elem_id="box-filter"):
-                            boxes[tag] = gr.CheckboxGroup(
-                                label=tag,
-                                choices=tags[tag],
-                                value=tags[tag],
-                                interactive=True,
-                                elem_id=f"filter-{tag}",
-                            )
-                with gr.Column(min_width=320):
-                    for tag in list(tags.keys())[4:]:
-                        with gr.Box(elem_id="box-filter"):
-                            boxes[tag] = gr.CheckboxGroup(
-                                label=tag,
-                                choices=tags[tag],
-                                value=tags[tag],
-                                interactive=True,
-                                elem_id=f"filter-{tag}",
-                            )
-            with gr.Row():
-                tag = list(tags.keys())[3]
-                with gr.Box(elem_id="box-filter"):
-                    boxes[tag] = gr.CheckboxGroup(
-                        label=tag,
-                        choices=tags[tag],
-                        value=tags[tag],
-                        interactive=True,
-                        elem_id=f"filter-{tag}",
-                    )
-            with gr.Row():
-                data_tag_wise = gr.components.Dataframe(
-                    format_df_for_leaderboard(DATA_OVERALL),
-                    datatype=["markdown"] + ["number"] * (len(DATA_OVERALL.columns) - 2) + ['markdown'],
-                    type="pandas",
-                    wrap=True,
-                    interactive=False,
-                )
-            def filter_tag_click(*boxes):
-                return format_df_for_leaderboard(filter_tags(complete_dt, list(boxes)))
-            def search_tag_click(query, *boxes):
-                return format_df_for_leaderboard(search_tags_leaderboard(complete_dt, list(boxes), [x.strip() for x in query.split(',')]))
-            for box in boxes:
-                boxes[box].change(fn=filter_tag_click, inputs=list(boxes.values()), outputs=data_tag_wise)
-                search_bar_tag.submit(fn=search_tag_click, inputs=[search_bar_tag] + list(boxes.values()), outputs=data_tag_wise)
-
-        with gr.TabItem('About GEO-bench 📖'):
-            with gr.Row():
-                gr.Markdown(f"""
-                ## About GEO-bench
-                - GEO-bench is a benchmarking platform for content optimization Methods for generative engines.
-                - It is a part of the work released under [GEO](https://arxiv.org/abs/2310.18xxx)
-                - The benchmark comprises of 9 datasets, 7 of which were publicly available, while 2 have been released by us.
-                - Dataset can be downloaded from [here](huggingface.co/datasets/pranjal2041/geo-bench)""")
-
-            with gr.Row():
-
-                # Goal of benchmarking content optimization for generative engines
-                # Contains 10K carefully curated queries
-                # Queries are diverse and cover many domains/intents
-                # Annotated with tags/dimensions like domain, difficulty, etc.
-                # Above list in HTML format
-                gr.HTML(f"""
-                <h3>Key-Highlights of GEO-bench</h3>
-                <ul>
-                    <li>Goal of benchmarking content optimization for generative engines</li>
-                    <li>Contains 10K carefully curated queries</li>
-                    <li>Queries are diverse and cover many domains/intents</li>
-                    <li>Annotated with tags/dimensions like domain, difficulty, etc.</li>
-                </ul>
-                """)
-
-                # Benchmark Link:
-                # gr.Markdown(f"""### Benchmark Link: [GEO-bench](huggingface.co/datasets/pranjal2041/geo-bench)""")
-
-                # Info about tags and other statistics
-                            
-
-        with gr.TabItem('Submit 📝'):
-            with gr.Row():
-                gr.Markdown(f"""
-                ## Submit
-                - To submit your Method, please check [here](github.com/Pranjal2041/GEO/GEO-Bench/leaderboard/Readme.md)""")
-
-
-                # Create a form to submit, the response should be sent to a google form
-                            
-        search_bar.submit(fn=search_button_click, inputs=search_bar, outputs=data_overall)
-
-if __name__ == "__main__":
+import gradio as gr
+import pandas as pd
+import os
+import itertools
+from constants import metric_dict, tags, columns
+
+# Download from github and load the data
+
+# TODO: Download every x hours
+def download_data(url = "https://github.com/Pranjal2041/GEO/GEO-Bench/leaderboard/leaderboard.jsonl", path = "leaderboard.jsonl"):
+    ret_code = os.system(f'wget {url} -O {path}_tmp')
+    if ret_code != 0:
+        return ret_code
+    os.system(f'mv {path}_tmp {path}')
+    return 0
+
+def search_leaderboard(df, queries):
+    # Assuming DATA_OVERALL is the DataFrame containing the leaderboard data
+    # filtered_data = df[df["Method"].str.contains(query, case=False, na=False)]
+    temp_pds = []
+    for query in queries:
+        temp_pds.append(df[df["Method"].str.contains(query, case=False, na=False)])
+    return pd.concat(temp_pds).drop_duplicates()
+ 
+def search_tags_leaderboard(df, tag_blocks, queries):
+    return search_leaderboard(filter_tags(df, tag_blocks), queries)
+
+def filter_tags(df, tag_blocks):
+    def fuzzy_in(x, y_set):
+        return any(x in z for z in y_set)
+    all_tags_sets = [set(tag.lower() for tag in tag_block) for tag_block in tag_blocks]
+
+    filtered_rows = [i for i, tags in enumerate(complete_dt['tags']) if all('any' in tag_set or any(fuzzy_in(tag.lower(), tag_set) for tag in tags) for tag_set in all_tags_sets)]
+
+    return prepare_complete_dt(df.iloc[filtered_rows])
+
+def prepare_complete_dt(complete_dt):
+    data = []
+    DATA_OVERALL = complete_dt.copy()
+    for Method in set(complete_dt['Method']):
+        data.append([])
+        data[-1].append(Method)
+        for metric in metric_dict:
+            metric_val = metric_dict[metric]
+            data[-1].append(complete_dt[complete_dt['Method'] == Method][metric_val].mean())
+        data[-1].append(complete_dt[complete_dt['Method'] == Method]['source'].iloc[0])
+        DATA_OVERALL = pd.DataFrame(data, columns=columns)
+    try:
+        DATA_OVERALL.sort_values(by=['WordPos Overall'], inplace=True, ascending=False)
+    except: ...
+    return DATA_OVERALL
+
+def format_df_for_leaderboard(df):
+    # The source column needs to be embedded directly into the Method column using appropriate markdown.
+    df['Method'] = df[['source', 'Method']].apply(lambda x: f'<a target="_blank" style="text-decoration: underline; color: #3571d7;" href="{x[0]}">{x[1]}</a>', axis=1)
+    # Convert all float metrics to 1 decimal
+    df_copy = df.copy()
+    for metric in metric_dict:
+        df_copy[metric] = df_copy[metric].apply(lambda x: float(f'{(100*x):.1f}'))
+    # drop the source column
+    return df_copy.drop(columns=['source'])
+
+
+ret_code = 0
+# ret_code = download_data()
+if ret_code != 0:
+    print("Leaderboard Download failed")
+
+complete_dt = pd.read_json('leaderboard.jsonl', lines=True, orient='records')
+DATA_OVERALL = prepare_complete_dt(complete_dt)
+
+
+with gr.Blocks() as demo:
+    
+    demo_content = """
+<style>
+  .badge-container {
+    text-align: center;
+    display: flex;
+    justify-content: center;
+  }
+  .badge {
+    margin: 1px;
+  }
+</style>
+<h1 style="text-align: center;">GEO-Bench Leaderboard</h1>
+<div class="badge-container">
+    <a href="https://pranjal2041.github.io/GEO/" class="badge">
+        <img src="https://img.shields.io/website?down_message=down&style=for-the-badge&up_message=up&url=https%3A%2F%2Fpranjal2041.github.io/GEO/" alt="Website">
+    </a>
+    <a href="https://arxiv.org/abs/2310.18xxx" class="badge">
+        <img src="https://img.shields.io/badge/arXiv-2310.18xxx-red.svg?style=for-the-badge" alt="Arxiv Paper">
+    </a>
+    <a href="https://huggingface.co/datasets/Pranjal2041/geo-bench" class="badge">
+        <img src="https://img.shields.io/badge/Dataset-GEO-%2DBENCH-orange?style=for-the-badge" alt="Dataset">
+    </a>
+    <a href="https://github.com/Pranjal2041/GEO" class="badge">
+        <img src="https://img.shields.io/badge/Github-Code-green?style=for-the-badge" alt="Code">
+    </a>
+</div>
+<p>
+    - For benchmarking content optimization Methods for Generative Engines.<br>
+    - GEO-Bench evaluates Methods for optimizing website content to improve visibility in generative engine responses. Benchmark contains 10K queries across 9 datasets covering diverse domains and intents.<br>
+    - Refer to GEO paper for more <a href="https://arxiv.org/abs/2310.18xxx">details</a>
+</p>
+"""
+
+
+    gr.HTML(demo_content)
+
+
+
+
+    with gr.Tabs():
+
+        with gr.TabItem('Overall 📊'):
+
+            with gr.Row():
+                gr.Markdown('## Overall Leaderboard')
+            
+            with gr.Row():
+                data_overall = gr.components.Dataframe(
+                    format_df_for_leaderboard(DATA_OVERALL),
+                    datatype=["markdown"] + ["number"] * (len(DATA_OVERALL.columns) - 2) + ['markdown'],
+                    type="pandas",
+                    wrap=True,
+                    interactive=False,
+                )
+                # data_overall.
+        
+            with gr.Row():
+                # search_bar = gr.Textbox(type="text", label="Search for a Method:")
+                search_bar = gr.Textbox(
+                    placeholder=" 🔍 Search for your Method (separate multiple queries with `,`) and press ENTER...",
+                    show_label=False,
+                    elem_id="search-bar",
+                )
+
+                def search_button_click(query):
+                    filtered_data = search_leaderboard(DATA_OVERALL, [x.strip() for x in query.split(',')])
+                    return format_df_for_leaderboard(filtered_data)
+                
+        with gr.TabItem('Tag-Wise Results 📊'):
+            with gr.Row():
+                gr.Markdown(f"""
+                ## Tag-Wise Results
+                - The following table shows the results for each tag.
+                - The tags are sorted in the order of their performance.
+                - The table is sorted in the order of the overall score.
+                """)
+            with gr.Row():
+
+                search_bar_tag = gr.Textbox(
+                    placeholder=" 🔍 Search for your Method (separate multiple queries with `,`) and press ENTER...",
+                    show_label=False,
+                    elem_id="search-bar",
+                )
+
+                def search_button_click(query):
+                    filtered_data = search_leaderboard(DATA_OVERALL, [x.strip() for x in query.split(',')])
+                    return format_df_for_leaderboard(filtered_data)
+
+            with gr.Row():
+                boxes = dict()
+                with gr.Column(min_width=320):
+                    for tag in list(tags.keys())[:3]:
+                        with gr.Box(elem_id="box-filter"):
+                            boxes[tag] = gr.CheckboxGroup(
+                                label=tag,
+                                choices=tags[tag],
+                                value=tags[tag],
+                                interactive=True,
+                                elem_id=f"filter-{tag}",
+                            )
+                with gr.Column(min_width=320):
+                    for tag in list(tags.keys())[4:]:
+                        with gr.Box(elem_id="box-filter"):
+                            boxes[tag] = gr.CheckboxGroup(
+                                label=tag,
+                                choices=tags[tag],
+                                value=tags[tag],
+                                interactive=True,
+                                elem_id=f"filter-{tag}",
+                            )
+            with gr.Row():
+                tag = list(tags.keys())[3]
+                with gr.Box(elem_id="box-filter"):
+                    boxes[tag] = gr.CheckboxGroup(
+                        label=tag,
+                        choices=tags[tag],
+                        value=tags[tag],
+                        interactive=True,
+                        elem_id=f"filter-{tag}",
+                    )
+            with gr.Row():
+                data_tag_wise = gr.components.Dataframe(
+                    format_df_for_leaderboard(DATA_OVERALL),
+                    datatype=["markdown"] + ["number"] * (len(DATA_OVERALL.columns) - 2) + ['markdown'],
+                    type="pandas",
+                    wrap=True,
+                    interactive=False,
+                )
+            def filter_tag_click(*boxes):
+                return format_df_for_leaderboard(filter_tags(complete_dt, list(boxes)))
+            def search_tag_click(query, *boxes):
+                return format_df_for_leaderboard(search_tags_leaderboard(complete_dt, list(boxes), [x.strip() for x in query.split(',')]))
+            for box in boxes:
+                boxes[box].change(fn=filter_tag_click, inputs=list(boxes.values()), outputs=data_tag_wise)
+                search_bar_tag.submit(fn=search_tag_click, inputs=[search_bar_tag] + list(boxes.values()), outputs=data_tag_wise)
+
+        with gr.TabItem('About GEO-bench 📖'):
+            with gr.Row():
+                gr.Markdown(f"""
+                ## About GEO-bench
+                - GEO-bench is a benchmarking platform for content optimization Methods for generative engines.
+                - It is a part of the work released under [GEO](https://arxiv.org/abs/2310.18xxx)
+                - The benchmark comprises of 9 datasets, 7 of which were publicly available, while 2 have been released by us.
+                - Dataset can be downloaded from [here](huggingface.co/datasets/pranjal2041/geo-bench)""")
+
+            with gr.Row():
+
+                # Goal of benchmarking content optimization for generative engines
+                # Contains 10K carefully curated queries
+                # Queries are diverse and cover many domains/intents
+                # Annotated with tags/dimensions like domain, difficulty, etc.
+                # Above list in HTML format
+                gr.HTML(f"""
+                <h3>Key-Highlights of GEO-bench</h3>
+                <ul>
+                    <li>Goal of benchmarking content optimization for generative engines</li>
+                    <li>Contains 10K carefully curated queries</li>
+                    <li>Queries are diverse and cover many domains/intents</li>
+                    <li>Annotated with tags/dimensions like domain, difficulty, etc.</li>
+                </ul>
+                """)
+
+                # Benchmark Link:
+                # gr.Markdown(f"""### Benchmark Link: [GEO-bench](huggingface.co/datasets/pranjal2041/geo-bench)""")
+
+                # Info about tags and other statistics
+                            
+
+        with gr.TabItem('Submit 📝'):
+            with gr.Row():
+                gr.Markdown(f"""
+                ## Submit
+                - To submit your Method, please check [here](github.com/Pranjal2041/GEO/GEO-Bench/leaderboard/Readme.md)""")
+
+
+                # Create a form to submit, the response should be sent to a google form
+                            
+        search_bar.submit(fn=search_button_click, inputs=search_bar, outputs=data_overall)
+
+if __name__ == "__main__":
     demo.launch()

constants.py

@@ -1,31 +1,31 @@
-# metrics = ['relevance_detailed', 'uniqueness_detailed', 'subjcount_detailed', 'follow_detailed', 'simple_wordpos', 'simple_pos', 'influence_detailed', 'subjective_score', 'diversity_detailed', 'simple_word', 'subjpos_detailed']
-columns = ['Method', 'Word', 'Position', 'WordPos Overall', 'Rel.', 'Infl.', 'Unique', 'Div.', 'FollowUp', 'Pos.', 'Count', 'Subjective Average', 'source']
-metric_dict = {
-    'Word': 'simple_word',
-    'Position': 'simple_pos',
-    'WordPos Overall': 'simple_wordpos',
-    'Rel.': 'relevance_detailed',
-    'Infl.': 'influence_detailed',
-    'Unique': 'uniqueness_detailed',
-    'Div.': 'diversity_detailed',
-    'FollowUp': 'follow_detailed',  
-    'Pos.': 'subjpos_detailed',
-    'Count': 'subjcount_detailed',
-    'Subjective Average': 'subjective_score',
-}
-
-tags = {
-    "Difficulty Level": ["Simple", "Intermediate", "Complex", "Multi-faceted", "Open-ended", 'any'],
-    "Nature of Query": ["Informational", "Navigational", "Transactional", "Debate", "Opinion", "Comparison", "Instructional", "Descriptive", "Predictive", 'any'],
-    "Sensitivity": ["Sensitive", "Non-sensitive",'any'],
-    "Genre": [
-        "🎭 Arts and Entertainment", "🚗 Autos and Vehicles", "💄 Beauty and Fitness", "📚 Books and Literature", "🏢 Business and Industrial",
-        "💻 Computers and Electronics", "💰 Finance", "🍔 Food and Drink", "🎮 Games", "🏥 Health", "🎨 Hobbies and Leisure", "🏡 Home and Garden",
-        "🌐 Internet and Telecom", "🎓 Jobs and Education", "🏛️ Law and Government", "📰 News", "💬 Online Communities", "👫 People and Society",
-        "🐾 Pets and Animals", "🏡 Real Estate", "📚 Reference", "🔬 Science", "🛒 Shopping", "⚽ Sports", "✈️ Travel",'any'
-    ],
-    "Specific Topics": ["Physics", "Chemistry", "Biology", "Mathematics", "Computer Science", "Economics", 'any'],
-    "User Intent": ["🔍 Research", "💰 Purchase", "🎉 Entertainment", "📚 Learning", "🔄 Comparison", 'any'],
-    "Answer Type": ["Fact", "Opinion", "List", "Explanation", "Guide", "Comparison", "Prediction", 'any'],
-}
-
+# metrics = ['relevance_detailed', 'uniqueness_detailed', 'subjcount_detailed', 'follow_detailed', 'simple_wordpos', 'simple_pos', 'influence_detailed', 'subjective_score', 'diversity_detailed', 'simple_word', 'subjpos_detailed']
+columns = ['Method', 'Word', 'Position', 'WordPos Overall', 'Rel.', 'Infl.', 'Unique', 'Div.', 'FollowUp', 'Pos.', 'Count', 'Subjective Average', 'source']
+metric_dict = {
+    'Word': 'simple_word',
+    'Position': 'simple_pos',
+    'WordPos Overall': 'simple_wordpos',
+    'Rel.': 'relevance_detailed',
+    'Infl.': 'influence_detailed',
+    'Unique': 'uniqueness_detailed',
+    'Div.': 'diversity_detailed',
+    'FollowUp': 'follow_detailed',  
+    'Pos.': 'subjpos_detailed',
+    'Count': 'subjcount_detailed',
+    'Subjective Average': 'subjective_score',
+}
+
+tags = {
+    "Difficulty Level": ["Simple", "Intermediate", "Complex", "Multi-faceted", "Open-ended", 'any'],
+    "Nature of Query": ["Informational", "Navigational", "Transactional", "Debate", "Opinion", "Comparison", "Instructional", "Descriptive", "Predictive", 'any'],
+    "Sensitivity": ["Sensitive", "Non-sensitive",'any'],
+    "Genre": [
+        "🎭 Arts and Entertainment", "🚗 Autos and Vehicles", "💄 Beauty and Fitness", "📚 Books and Literature", "🏢 Business and Industrial",
+        "💻 Computers and Electronics", "💰 Finance", "🍔 Food and Drink", "🎮 Games", "🏥 Health", "🎨 Hobbies and Leisure", "🏡 Home and Garden",
+        "🌐 Internet and Telecom", "🎓 Jobs and Education", "🏛️ Law and Government", "📰 News", "💬 Online Communities", "👫 People and Society",
+        "🐾 Pets and Animals", "🏡 Real Estate", "📚 Reference", "🔬 Science", "🛒 Shopping", "⚽ Sports", "✈️ Travel",'any'
+    ],
+    "Specific Topics": ["Physics", "Chemistry", "Biology", "Mathematics", "Computer Science", "Economics", 'any'],
+    "User Intent": ["🔍 Research", "💰 Purchase", "🎉 Entertainment", "📚 Learning", "🔄 Comparison", 'any'],
+    "Answer Type": ["Fact", "Opinion", "List", "Explanation", "Guide", "Comparison", "Prediction", 'any'],
+}
+