initial commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,322 @@
+import gradio as gr
+import pandas as pd
+import json
+import os
+
+def strip_timestamp(name):
+    """Remove the timestamp portion from the model name."""
+    parts = name.split('-')
+    return '-'.join(parts[1:]) if len(parts) > 1 else name
+
+# Static grouping mapping for the 10 general submissions.
+GROUPS = [
+    {
+        "mwoz": "20250214_193236-o1",
+        "tau_airline": "20250215_115156-tau-o1-airline",
+        "tau_retail": "20250215_121147-tau-o1-retail"
+    },
+    {
+        "mwoz": "20250131_012338-llama405",
+        "tau_airline": "20250204_144222-tau-llama-405b-airline",
+        "tau_retail": "20250205_033820-tau-llama405b-retail"
+    },
+    {
+        "mwoz": "20250130_140218-4o",
+        "tau_airline": "20250131_152503-tau-4o-airline",
+        "tau_retail": "20250131_152422-tau-4o-retail"
+    },
+    {
+        "mwoz": "20250130_183030-claude",
+        "tau_airline": "20250205_030422-tau-sonnet-airline",
+        "tau_retail": "20250131_152807-tau-sonnet-retail"
+    },
+    {
+        "mwoz": "20250131_012449-llama70",
+        "tau_airline": "20250208_024344-tau-llama70b-airline",
+        "tau_retail": "20250208_030407-tau-llama70b-retail"
+    },
+    {
+        "mwoz": "20250131_013711-qwen72b",
+        "tau_airline": "20250202_112945-qwen72b-airline",
+        "tau_retail": "20250202_140527-qwen72b-retail"
+    },
+    {
+        "mwoz": "20250130_184905-mistrallarge",
+        "tau_airline": "20250205_024823-tau-mistrallarge-airline",
+        "tau_retail": "20250205_044403-tau-mistrallarge-retail"
+    },
+    {
+        "mwoz": "20250131_010143-o1mini",
+        "tau_airline": "20250214_180731-tau-o1-mini-airline",
+        "tau_retail": "20250214_142736-tau-o1-mini-retail"
+    },
+    {
+        "mwoz": "20250130_140439-4omini",
+        "tau_airline": "20250131_152226-tau-4o-mini-airline",
+        "tau_retail": "20250131_152338-tau-4o-mini-retail"
+    },
+    {
+        "mwoz": "20250130_145202-gpt35",
+        "tau_airline": "20250131_152708-tau-gpt35-airline",
+        "tau_retail": "20250131_152610-tau-gpt35-retail"
+    }
+]
+
+def load_mwoz_results():
+    """Load mwoz results from data/mwoz_leaderboard_results.json."""
+    path = os.path.join("data", "mwoz_leaderboard_results.json")
+    if not os.path.exists(path):
+        return []
+    with open(path, "r") as f:
+        return json.load(f)
+
+def load_tau_results():
+    """Load tau results from data/tau_leaderboard_results.json."""
+    path = os.path.join("data", "tau_leaderboard_results.json")
+    if not os.path.exists(path):
+        return []
+    with open(path, "r") as f:
+        return json.load(f)
+
+def create_grouped_leaderboard(selected_mwoz, selected_tau_airline, selected_tau_retail, sort_state):
+    """
+    Create the aggregated leaderboard DataFrame.
+    Aggregates metrics based on the selected variants and sorts the DataFrame using sort_state.
+    """
+    # Ensure at least one variant is active.
+    if not (selected_mwoz or selected_tau_airline or selected_tau_retail):
+        selected_mwoz = True
+
+    mwoz_data = load_mwoz_results()
+    tau_data = load_tau_results()
+    mwoz_lookup = {entry["model_name"]: entry for entry in mwoz_data}
+    tau_lookup = {entry["model_name"]: entry for entry in tau_data}
+    
+    aggregated = []
+    for group in GROUPS:
+        metrics = {"avg_conv_consistency": 0, "avg_backend_consistency": 0, "avg_policy_completeness": 0}
+        count = 0
+        title_parts = []
+        judge_model = ""
+        if selected_mwoz:
+            key = group["mwoz"]
+            if key in mwoz_lookup:
+                record = mwoz_lookup[key]
+                metrics["avg_conv_consistency"] += record.get("avg_conv_consistency", 0)
+                metrics["avg_backend_consistency"] += record.get("avg_backend_consistency", 0)
+                metrics["avg_policy_completeness"] += record.get("avg_policy_completeness", 0)
+                count += 1
+                title_parts.append(strip_timestamp(key))
+                judge_model = record.get("judge_model", "")
+        if selected_tau_airline:
+            key = group["tau_airline"]
+            if key in tau_lookup:
+                record = tau_lookup[key]
+                metrics["avg_conv_consistency"] += record.get("avg_conv_consistency", 0)
+                metrics["avg_backend_consistency"] += record.get("avg_backend_consistency", 0)
+                metrics["avg_policy_completeness"] += record.get("avg_policy_completeness", 0)
+                count += 1
+                title_parts.append(strip_timestamp(key))
+                judge_model = record.get("judge_model", "")
+        if selected_tau_retail:
+            key = group["tau_retail"]
+            if key in tau_lookup:
+                record = tau_lookup[key]
+                metrics["avg_conv_consistency"] += record.get("avg_conv_consistency", 0)
+                metrics["avg_backend_consistency"] += record.get("avg_backend_consistency", 0)
+                metrics["avg_policy_completeness"] += record.get("avg_policy_completeness", 0)
+                count += 1
+                title_parts.append(strip_timestamp(key))
+                judge_model = record.get("judge_model", "")
+        if count > 0:
+            avg_conv = metrics["avg_conv_consistency"] / count
+            avg_backend = metrics["avg_backend_consistency"] / count
+            avg_policy = metrics["avg_policy_completeness"] / count
+            overall_avg = (avg_conv + avg_backend + avg_policy) / 3
+        else:
+            avg_conv = avg_backend = avg_policy = overall_avg = 0
+        
+        aggregated.append({
+            "Model": " / ".join(title_parts),
+            "Average Score": round(overall_avg, 4),
+            "Conversation Consistency": round(avg_conv, 4),
+            "Backend Consistency": round(avg_backend, 4),
+            "Policy Completeness": round(avg_policy, 4),
+            "Judge Model": judge_model
+        })
+    
+    df = pd.DataFrame(aggregated)
+    # Sort if a valid column is provided.
+    allowed_sort_cols = ["Average Score", "Conversation Consistency", "Backend Consistency", "Policy Completeness"]
+    sort_by = sort_state.get("sort_by") if sort_state else None
+    ascending = sort_state.get("ascending") if sort_state else True
+    if sort_by in allowed_sort_cols:
+        df = df.sort_values(sort_by, ascending=ascending)
+    return df
+
+def update_sort_state(current_state, clicked_column):
+    """
+    Update the sort state based on the clicked column.
+    If the same column is clicked, toggle the sort order;
+    otherwise, switch to the new column with ascending order.
+    """
+    if current_state is None:
+        current_state = {"sort_by": clicked_column, "ascending": True}
+    else:
+        if current_state.get("sort_by") == clicked_column:
+            current_state["ascending"] = not current_state.get("ascending", True)
+        else:
+            current_state["sort_by"] = clicked_column
+            current_state["ascending"] = True
+    return current_state
+
+def sort_by_avg(sort_state):
+    return update_sort_state(sort_state, "Average Score")
+
+def sort_by_conv(sort_state):
+    return update_sort_state(sort_state, "Conversation Consistency")
+
+def sort_by_backend(sort_state):
+    return update_sort_state(sort_state, "Backend Consistency")
+
+def sort_by_policy(sort_state):
+    return update_sort_state(sort_state, "Policy Completeness")
+
+def get_color_for_value(value, min_val, max_val):
+    """
+    Compute a color for a given value based on its normalized position.
+    Interpolates from red (lowest) to yellow (mid) to green (highest).
+    """
+    if max_val == min_val:
+        norm = 0.5
+    else:
+        norm = (value - min_val) / (max_val - min_val)
+    if norm < 0.5:
+        ratio = norm / 0.5
+        r = 255
+        g = int(255 * ratio)
+        b = 0
+    else:
+        ratio = (norm - 0.5) / 0.5
+        r = int(255 * (1 - ratio))
+        g = 255
+        b = 0
+    return f"#{r:02X}{g:02X}{b:02X}"
+
+def generate_html_table(df):
+    """
+    Generate an HTML table from the DataFrame.
+    For each numeric column, apply a text color based on its relative value.
+    """
+    numeric_cols = ["Average Score", "Conversation Consistency", "Backend Consistency", "Policy Completeness"]
+    col_min = {}
+    col_max = {}
+    for col in numeric_cols:
+        col_min[col] = df[col].min() if not df.empty else 0
+        col_max[col] = df[col].max() if not df.empty else 0
+
+    html = "<table border='1' style='border-collapse: collapse; text-align: center; width: 100%;'>"
+    # Header row
+    html += "<tr>"
+    for col in df.columns:
+        html += f"<th style='padding: 8px;'>{col}</th>"
+    html += "</tr>"
+
+    # Data rows
+    for _, row in df.iterrows():
+        html += "<tr>"
+        for col in df.columns:
+            cell_value = row[col]
+            if col in numeric_cols:
+                color = get_color_for_value(cell_value, col_min[col], col_max[col])
+                # Now applying the color to the text (color property) instead of background.
+                html += f"<td style='padding: 8px; color: {color};'>{cell_value}</td>"
+            else:
+                html += f"<td style='padding: 8px;'>{cell_value}</td>"
+        html += "</tr>"
+    html += "</table>"
+    return html
+
+def update_leaderboard(selected_mwoz, selected_tau_airline, selected_tau_retail, sort_state):
+    """
+    Update the leaderboard by creating the aggregated DataFrame and converting it to HTML.
+    """
+    df = create_grouped_leaderboard(selected_mwoz, selected_tau_airline, selected_tau_retail, sort_state)
+    html_table = generate_html_table(df)
+    return html_table
+
+with gr.Blocks(title="Rome Leaderboard") as demo:
+    gr.Markdown("# 🏆 Rome Model Evaluation Leaderboard")
+    gr.Markdown("""
+    This leaderboard displays aggregated model performance across multiple evaluation metrics.
+    
+    **Variants:**
+    - **mwoz:** Baseline variant.
+    - **tau-airline:** Airline specialty variant.
+    - **tau-retail:** Retail specialty variant.
+    
+    Use the checkboxes below to select which variants to include. At least one variant must be active.
+    """)
+    
+    with gr.Row():
+        cb_mwoz = gr.Checkbox(label="mwoz", value=True)
+        cb_tau_airline = gr.Checkbox(label="tau-airline", value=True)
+        cb_tau_retail = gr.Checkbox(label="tau-retail", value=True)
+    
+    gr.Markdown("### Sort by (click a button to toggle ascending/descending):")
+    with gr.Row():
+        btn_avg = gr.Button("Average Score")
+        btn_conv = gr.Button("Conversation Consistency")
+        btn_backend = gr.Button("Backend Consistency")
+        btn_policy = gr.Button("Policy Completeness")
+    
+    # Initialize sort state: default sort by Average Score descending.
+    sort_state = gr.State({"sort_by": "Average Score", "ascending": False})
+    
+    leaderboard_display = gr.HTML(label="Aggregated Model Rankings")
+    
+    refresh_btn = gr.Button("🔄 Refresh Leaderboard")
+    
+    # Sort button events.
+    btn_avg.click(fn=sort_by_avg, inputs=[sort_state], outputs=[sort_state]).then(
+        fn=update_leaderboard,
+        inputs=[cb_mwoz, cb_tau_airline, cb_tau_retail, sort_state],
+        outputs=leaderboard_display
+    )
+    btn_conv.click(fn=sort_by_conv, inputs=[sort_state], outputs=[sort_state]).then(
+        fn=update_leaderboard,
+        inputs=[cb_mwoz, cb_tau_airline, cb_tau_retail, sort_state],
+        outputs=leaderboard_display
+    )
+    btn_backend.click(fn=sort_by_backend, inputs=[sort_state], outputs=[sort_state]).then(
+        fn=update_leaderboard,
+        inputs=[cb_mwoz, cb_tau_airline, cb_tau_retail, sort_state],
+        outputs=leaderboard_display
+    )
+    btn_policy.click(fn=sort_by_policy, inputs=[sort_state], outputs=[sort_state]).then(
+        fn=update_leaderboard,
+        inputs=[cb_mwoz, cb_tau_airline, cb_tau_retail, sort_state],
+        outputs=leaderboard_display
+    )
+    
+    # Refresh button event.
+    refresh_btn.click(
+        fn=update_leaderboard,
+        inputs=[cb_mwoz, cb_tau_airline, cb_tau_retail, sort_state],
+        outputs=leaderboard_display
+    )
+    
+    # Update leaderboard immediately when any checkbox changes.
+    cb_mwoz.change(fn=update_leaderboard, inputs=[cb_mwoz, cb_tau_airline, cb_tau_retail, sort_state], outputs=leaderboard_display)
+    cb_tau_airline.change(fn=update_leaderboard, inputs=[cb_mwoz, cb_tau_airline, cb_tau_retail, sort_state], outputs=leaderboard_display)
+    cb_tau_retail.change(fn=update_leaderboard, inputs=[cb_mwoz, cb_tau_airline, cb_tau_retail, sort_state], outputs=leaderboard_display)
+    
+    # Load initial leaderboard on app start.
+    demo.load(
+        fn=update_leaderboard,
+        inputs=[cb_mwoz, cb_tau_airline, cb_tau_retail, sort_state],
+        outputs=leaderboard_display
+    )
+
+if __name__ == "__main__":
+    demo.launch()

data/mwoz_leaderboard_results.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:30f233c17f2a9e1068eb5313c1cc1c1e4b622593eb01a40a34b1a95be2824873
+size 3052

data/tau_leaderboard_results.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6dd7553f04d89e492bfec22e2e8f9ab8d7afb269c0b8c027709aedce3ac63aa9
+size 9396

process_submissions.py

@@ -0,0 +1,76 @@
+import json
+import os
+from pathlib import Path
+import argparse
+
+def process_submissions(input_file, output_dir='submissions'):
+    """
+    Process a JSON file containing multiple model submissions and split it into
+    individual JSON files in the submissions directory.
+    
+    Args:
+        input_file (str): Path to the input JSON file
+        output_dir (str): Directory where individual submission files will be stored
+    """
+    # Create submissions directory if it doesn't exist
+    Path(output_dir).mkdir(exist_ok=True)
+    
+    # Read the input file
+    try:
+        with open(input_file, 'r') as f:
+            submissions = json.load(f)
+        
+        if not isinstance(submissions, list):
+            print(f"Error: Input file {input_file} must contain a JSON array of submissions")
+            return
+        
+        # Process each submission
+        for submission in submissions:
+            if 'model_name' not in submission:
+                print(f"Warning: Skipping submission without model_name field")
+                continue
+                
+            model_name = submission['model_name']
+            # Create a safe filename from the model name
+            safe_filename = f"{model_name.replace('/', '_')}.json"
+            output_path = os.path.join(output_dir, safe_filename)
+            
+            # Write individual submission file
+            with open(output_path, 'w') as f:
+                json.dump(submission, f, indent=4)
+            
+            print(f"Created submission file: {output_path}")
+        
+        print(f"\nProcessed {len(submissions)} submissions successfully!")
+        
+    except FileNotFoundError:
+        print(f"Error: Input file '{input_file}' not found")
+    except json.JSONDecodeError:
+        print(f"Error: Input file '{input_file}' is not valid JSON")
+    except Exception as e:
+        print(f"Error processing submissions: {str(e)}")
+
+def main():
+    # Set up argument parser
+    parser = argparse.ArgumentParser(
+        description='Process a JSON file containing model submissions and split into individual files.'
+    )
+    parser.add_argument(
+        'input_file',
+        help='Path to the input JSON file containing model submissions'
+    )
+    parser.add_argument(
+        '--output-dir',
+        '-o',
+        default='submissions',
+        help='Directory where individual submission files will be stored (default: submissions)'
+    )
+    
+    # Parse arguments
+    args = parser.parse_args()
+    
+    # Process submissions
+    process_submissions(args.input_file, args.output_dir)
+
+if __name__ == "__main__":
+    main() 

requirements.txt

@@ -0,0 +1,2 @@
+gradio>=4.0.0
+pandas>=2.0.0 

submissions/20250130_140218-4o.json

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submissions/20250130_140439-4omini.json

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submissions/20250130_145202-gpt35.json

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submissions/20250130_183030-claude.json

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submissions/20250130_184905-mistrallarge.json

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submissions/20250131_010143-o1mini.json

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submissions/20250131_012338-llama405.json

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submissions/20250131_012449-llama70.json

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submissions/20250131_152226-tau-4o-mini-airline.json

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submissions/20250131_152503-tau-4o-airline.json

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