app.py

import gradio as gr
import json
import os
from pathlib import Path

def create_reranking_interface(task_data):
    """Create a Gradio interface for reranking evaluation using drag and drop."""
    samples = task_data["samples"]
    results = {"task_name": task_data["task_name"], "task_type": "reranking", "annotations": []}
    completed_samples = {s["id"]: False for s in samples}
    
    # Define helper functions before UI elements are created
    def generate_sortable_html(candidates, existing_ranks=None):
        """Generate HTML with simple dropdowns for ranking."""
        # Use existing ranks if available
        ranks = [0] * len(candidates)
        if existing_ranks and len(existing_ranks) == len(candidates):
            ranks = existing_ranks.copy()
        
        # Generate a unique ID for this set of dropdowns to avoid conflicts
        import random
        import time
        dropdown_group_id = f"rank_group_{int(time.time())}_{random.randint(1000, 9999)}"
        
        html = f"""
        <div class="ranking-simple">
            <input type="hidden" id="rank-order-state" value="">
            <div class="rank-instructions">Select a rank (1-{len(candidates)}) for each document.</div>
        """
        
        # Add each document with a dropdown selector
        for i, doc in enumerate(candidates):
            import html as html_escaper
            escaped_doc = html_escaper.escape(doc)
            current_rank = ranks[i] if ranks[i] > 0 else i + 1
            
            html += f"""
            <div class="rank-item" data-doc-id="{i}">
                <div class="rank-selector">
                    <select class="rank-dropdown" data-doc-id="{i}" onchange="updateRankOrder('{dropdown_group_id}')">
            """
            
            # Add options 1 through N
            for rank in range(1, len(candidates) + 1):
                selected = "selected" if rank == current_rank else ""
                html += f'<option value="{rank}" {selected}>{rank}</option>'
            
            html += f"""
                    </select>
                </div>
                <div class="doc-content">{escaped_doc}</div>
            </div>
            """
        
        # Add the JavaScript for handling rank updates
        html += f"""
        <script>
        // Function to update the hidden state when dropdowns change
        function updateRankOrder(groupId) {{
            const items = document.querySelectorAll('.rank-item');
            const selectedRanks = new Map();
            const docOrder = [];
            
            // First collect all selected ranks
            items.forEach(item => {{
                const docId = parseInt(item.getAttribute('data-doc-id'));
                const dropdown = item.querySelector('.rank-dropdown');
                const rank = parseInt(dropdown.value);
                selectedRanks.set(docId, rank);
            }});
            
            // Sort documents by their selected rank
            const sortedDocs = Array.from(selectedRanks.entries())
                .sort((a, b) => a[1] - b[1])
                .map(entry => entry[0]);
                
            // Update the order state
            const orderInput = document.querySelector('#current-order textarea');
            if (orderInput) {{
                orderInput.value = JSON.stringify(sortedDocs);
                const event = new Event('input', {{ bubbles: true }});
                orderInput.dispatchEvent(event);
            }}
        }}
        
        // Initialize on page load
        document.addEventListener('DOMContentLoaded', function() {{
            updateRankOrder('{dropdown_group_id}');
        }});
        
        // Backup initialization for iframe environments
        setTimeout(function() {{
            updateRankOrder('{dropdown_group_id}');
        }}, 1000);
        </script>
        </div>
        """
        
        return html
    
    def save_ranking(order_json, sample_id):
        """Save the current ranking to results."""
        try:
            if not order_json or order_json == "[]":
                return "⚠️ Drag documents to set the ranking before submitting.", progress_text.value
            order = json.loads(order_json)
            num_candidates = len(next(s["candidates"] for s in samples if s["id"] == sample_id))
            if len(order) != num_candidates:
                return f"⚠️ Ranking order length mismatch. Expected {num_candidates}, got {len(order)}.", progress_text.value
            rankings = [0] * num_candidates
            for rank_minus_1, doc_idx in enumerate(order):
                if doc_idx < num_candidates:
                    rankings[doc_idx] = rank_minus_1 + 1
                else:
                    raise ValueError(f"Invalid document index {doc_idx} found in order.")
            if sorted(rankings) != list(range(1, num_candidates + 1)):
                return "⚠️ Ranking validation failed. Ranks are not 1 to N.", progress_text.value
            annotation = {"sample_id": sample_id, "rankings": rankings}
            existing_idx = next((i for i, a in enumerate(results["annotations"]) if a["sample_id"] == sample_id), None)
            if existing_idx is not None:
                results["annotations"][existing_idx] = annotation
            else:
                results["annotations"].append(annotation)
            completed_samples[sample_id] = True
            output_path = f"{task_data['task_name']}_human_results.json"
            with open(output_path, "w") as f:
                json.dump(results, f, indent=2)
            return f"✅ Rankings saved successfully ({len(results['annotations'])}/{len(samples)} completed)", f"Progress: {sum(completed_samples.values())}/{len(samples)}"
        except json.JSONDecodeError:
            return "⚠️ Error decoding ranking order. Please try again.", progress_text.value
        except Exception as e:
            import traceback
            print(traceback.format_exc())
            return f"Error saving ranking: {str(e)}", progress_text.value
    
    def load_sample(sample_id):
        """Load a sample into the interface."""
        try:
            sample = next((s for s in samples if s["id"] == sample_id), None)
            if not sample:
                return gr.update(), gr.update(value="[]"), gr.update(), gr.update()
            existing_ranking = next((anno["rankings"] for anno in results["annotations"] if anno["sample_id"] == sample_id), None)
            new_html = generate_sortable_html(sample["candidates"], existing_ranking)
            status = "Ready to rank" if not completed_samples.get(sample_id, False) else "Already ranked"
            progress = f"Progress: {sum(completed_samples.values())}/{len(samples)}"
            return sample["query"], new_html, "[]", progress, status
        except Exception as e:
            return gr.update(), gr.update(value="[]"), gr.update(), gr.update(value=f"Error loading sample: {str(e)}")
    
    def next_sample_id(current_id):
        current_idx = next((i for i, s in enumerate(samples) if s["id"] == current_id), -1)
        if current_idx == -1:
            return current_id
        next_idx = min(current_idx + 1, len(samples) - 1)
        return samples[next_idx]["id"]
    
    def prev_sample_id(current_id):
        current_idx = next((i for i, s in enumerate(samples) if s["id"] == current_id), -1)
        if current_idx == -1:
            return current_id
        prev_idx = max(current_idx - 1, 0)
        return samples[prev_idx]["id"]
    
    def save_results():
        output_path = f"{task_data['task_name']}_human_results.json"
        try:
            with open(output_path, "w") as f:
                json.dump(results, f, indent=2)
            return f"✅ Results saved to {output_path} ({len(results['annotations'])} annotations)"
        except Exception as e:
            return f"⚠️ Error saving results file: {str(e)}"
    
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown(f"# {task_data['task_name']} - Human Reranking Evaluation")
        with gr.Accordion("Instructions", open=True):
            gr.Markdown("""
            ## Task Instructions
            
            {instructions}
            
            ### How to use this interface:
            1. Read the query at the top
            2. Drag and drop documents to reorder them based on relevance
            3. Top document = Rank 1, Second = Rank 2, etc.
            4. Click "Submit Rankings" when you're done with the current query
            5. Use "Previous" and "Next" to navigate between queries
            6. Click "Save All Results" periodically to ensure your work is saved
            """.format(instructions=task_data["instructions"]))
        
        current_sample_id = gr.State(value=samples[0]["id"])
        
        with gr.Row():
            progress_text = gr.Textbox(label="Progress", value=f"Progress: 0/{len(samples)}", interactive=False)
            status_box = gr.Textbox(label="Status", value="Ready to start evaluation", interactive=False)
        
        with gr.Group():
            gr.Markdown("## Query:")
            query_text = gr.Textbox(value=samples[0]["query"], label="", interactive=False)
            gr.Markdown("## Documents to Rank (Drag to Reorder):")
            sortable_list = gr.HTML(generate_sortable_html(samples[0]["candidates"], []), elem_id="sortable-list-container")
            order_state = gr.Textbox(value="[]", visible=False, elem_id="current-order")
            with gr.Row():
                prev_btn = gr.Button("← Previous Query", size="sm", elem_id="prev-btn")
                submit_btn = gr.Button("Submit Rankings", size="lg", variant="primary", elem_id="submit-btn")
                next_btn = gr.Button("Next Query →", size="sm", elem_id="next-btn")
            save_btn = gr.Button("💾 Save All Results", variant="secondary")
        
        js_code = """
        <style>
        /* Simple dropdown ranking styles */
        .ranking-simple {
            width: 100%;
            max-width: 100%;
            margin: 0 auto;
        }

        .rank-instructions {
            margin-bottom: 15px;
            padding: 10px;
            background-color: #f0f9ff;
            border-left: 4px solid #3b82f6;
            border-radius: 4px;
        }

        .rank-item {
            display: flex;
            align-items: flex-start;
            padding: 12px;
            margin-bottom: 10px;
            background: white;
            border: 1px solid #e0e0e0;
            border-radius: 6px;
        }

        .rank-selector {
            margin-right: 15px;
            min-width: 70px;
        }

        .rank-dropdown {
            width: 60px;
            padding: 6px;
            border: 1px solid #d1d5db;
            border-radius: 4px;
            background-color: white;
            font-size: 14px;
        }

        .doc-content {
            flex: 1;
            line-height: 1.5;
            padding: 5px 0;
        }
        </style>
        """
        gr.HTML(js_code)
        
        submit_btn.click(
            save_ranking,
            inputs=[order_state, current_sample_id],
            outputs=[status_box, progress_text]
        )
        
        next_btn.click(
            next_sample_id, inputs=[current_sample_id], outputs=[current_sample_id]
        ).then(
            load_sample,
            inputs=[current_sample_id],
            outputs=[query_text, sortable_list, order_state, progress_text, status_box]
        )
        
        prev_btn.click(
            prev_sample_id, inputs=[current_sample_id], outputs=[current_sample_id]
        ).then(
            load_sample,
            inputs=[current_sample_id],
            outputs=[query_text, sortable_list, order_state, progress_text, status_box]
        )
        
        save_btn.click(save_results, outputs=[status_box])
        
        demo.load(lambda: load_sample(samples[0]['id']), 
                  outputs=[query_text, sortable_list, order_state, progress_text, status_box])
        
    return demo

# Main app with file upload capability
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# MTEB Human Evaluation Demo")
    
    with gr.Tabs():
        with gr.TabItem("Demo"):
            gr.Markdown("""
            ## MTEB Human Evaluation Interface
            
            This interface allows you to evaluate the relevance of documents for reranking tasks.
            """)
            
            # Function to get the most recent task file
            def get_latest_task_file():
                # Check first in uploaded_tasks directory
                os.makedirs("uploaded_tasks", exist_ok=True)
                uploaded_tasks = [f for f in os.listdir("uploaded_tasks") if f.endswith(".json")]
                
                if uploaded_tasks:
                    # Sort by modification time, newest first
                    uploaded_tasks.sort(key=lambda x: os.path.getmtime(os.path.join("uploaded_tasks", x)), reverse=True)
                    return os.path.join("uploaded_tasks", uploaded_tasks[0])
                
                # Fall back to default example
                return "AskUbuntuDupQuestions_human_eval.json"
            
            # Load the task file
            task_file = get_latest_task_file()
            
            try:
                with open(task_file, "r") as f:
                    task_data = json.load(f)
                
                # Show which task is currently loaded
                gr.Markdown(f"**Current Task: {task_data['task_name']}** ({len(task_data['samples'])} samples)")
                
                # Display the interface
                reranking_demo = create_reranking_interface(task_data)
            except Exception as e:
                gr.Markdown(f"**Error loading task: {str(e)}**")
                gr.Markdown("Please upload a valid task file in the 'Upload & Evaluate' tab.")
        
        with gr.TabItem("Upload & Evaluate"):
            gr.Markdown("""
            ## Upload Your Own Task File
            
            If you have a prepared task file, you can upload it here to create an evaluation interface.
            """)
            
            with gr.Row():
                with gr.Column():
                    file_input = gr.File(label="Upload a task file (JSON)")
                    load_btn = gr.Button("Load Task")
                    message = gr.Textbox(label="Status", interactive=False)
                    
                    # Add task list for previously uploaded tasks
                    gr.Markdown("### Previous Uploads")
                    
                    # Function to list existing task files in the tasks directory
                    def list_task_files():
                        os.makedirs("uploaded_tasks", exist_ok=True)
                        tasks = [f for f in os.listdir("uploaded_tasks") if f.endswith(".json")]
                        if not tasks:
                            return "No task files uploaded yet."
                        return "\n".join([f"- [{t}](javascript:selectTask('{t}'))" for t in tasks])
                    
                    task_list = gr.Markdown(list_task_files())
                    refresh_btn = gr.Button("Refresh List")
                    
                    # Add results management section
                    gr.Markdown("### Results Management")
                    
                    # Function to list existing result files
                    def list_result_files():
                        results = [f for f in os.listdir(".") if f.endswith("_human_results.json")]
                        if not results:
                            return "No result files available yet."
                        
                        result_links = []
                        for r in results:
                            # Calculate completion stats
                            try:
                                with open(r, "r") as f:
                                    result_data = json.load(f)
                                annotation_count = len(result_data.get("annotations", []))
                                task_name = result_data.get("task_name", "Unknown")
                                result_links.append(f"- {r} ({annotation_count} annotations for {task_name})")
                            except:
                                result_links.append(f"- {r}")
                        
                        return "\n".join(result_links)
                    
                    results_list = gr.Markdown(list_result_files())
                    download_results_btn = gr.Button("Download Results")
                    
                # Right side - will contain the actual interface
                with gr.Column():
                    task_container = gr.HTML()
            
            # Handle file upload and storage
            def handle_upload(file):
                if not file:
                    return "Please upload a task file", task_list.value, task_container.value
                
                try:
                    # Create directory if it doesn't exist
                    os.makedirs("uploaded_tasks", exist_ok=True)
                    
                    # Read the uploaded file
                    with open(file.name, "r") as f:
                        task_data = json.load(f)
                    
                    # Validate task format
                    if "task_name" not in task_data or "samples" not in task_data:
                        return "Invalid task file format. Must contain 'task_name' and 'samples' fields.", task_list.value, task_container.value
                    
                    # Save to a consistent location
                    task_filename = f"uploaded_tasks/{task_data['task_name']}_task.json"
                    with open(task_filename, "w") as f:
                        json.dump(task_data, f, indent=2)
                    
                    # Instead of trying to create the interface here,
                    # we'll return a message with instructions
                    return f"Task '{task_data['task_name']}' uploaded successfully with {len(task_data['samples'])} samples. Please refresh the app and use the Demo tab to evaluate it.", list_task_files(), f"""
                    <div style="padding: 20px; background-color: #f0f0f0; border-radius: 10px;">
                        <h3>Task uploaded successfully!</h3>
                        <p>Task Name: {task_data['task_name']}</p>
                        <p>Samples: {len(task_data['samples'])}</p>
                        <p>To evaluate this task:</p>
                        <ol>
                            <li>Refresh the app</li>
                            <li>The Demo tab will now use your uploaded task</li>
                            <li>Complete your evaluations</li>
                            <li>Results will be saved as {task_data['task_name']}_human_results.json</li>
                        </ol>
                    </div>
                    """
                except Exception as e:
                    return f"Error processing task file: {str(e)}", task_list.value, task_container.value
            
            # Function to prepare results for download
            def prepare_results_for_download():
                results = [f for f in os.listdir(".") if f.endswith("_human_results.json")]
                if not results:
                    return None
                
                # Create a zip file with all results
                import zipfile
                zip_path = "mteb_human_eval_results.zip"
                with zipfile.ZipFile(zip_path, 'w') as zipf:
                    for r in results:
                        zipf.write(r)
                
                return zip_path
            
            # Connect events
            load_btn.click(handle_upload, inputs=[file_input], outputs=[message, task_list, task_container])
            refresh_btn.click(list_task_files, outputs=[task_list])
            download_results_btn.click(prepare_results_for_download, outputs=[gr.File(label="Download Results")])
        
        with gr.TabItem("Results Management"):
            gr.Markdown("""
            ## Manage Evaluation Results
            
            View, download, and analyze your evaluation results.
            """)
            
            # Function to load and display result stats
            def get_result_stats():
                results = [f for f in os.listdir(".") if f.endswith("_human_results.json")]
                if not results:
                    return "No result files available yet."
                
                stats = []
                for r in results:
                    try:
                        with open(r, "r") as f:
                            result_data = json.load(f)
                        
                        task_name = result_data.get("task_name", "Unknown")
                        annotations = result_data.get("annotations", [])
                        annotation_count = len(annotations)
                        
                        # Calculate completion percentage
                        sample_ids = set(a.get("sample_id") for a in annotations)
                        
                        # Try to get the total sample count from the corresponding task file
                        total_samples = 0
                        task_file = f"uploaded_tasks/{task_name}_task.json"
                        if os.path.exists(task_file):
                            with open(task_file, "r") as f:
                                task_data = json.load(f)
                            total_samples = len(task_data.get("samples", []))
                        
                        completion = f"{len(sample_ids)}/{total_samples}" if total_samples else f"{len(sample_ids)} samples"
                        
                        stats.append(f"### {task_name}\n- Annotations: {annotation_count}\n- Completion: {completion}\n- File: {r}")
                    except Exception as e:
                        stats.append(f"### {r}\n- Error loading results: {str(e)}")
                
                return "\n\n".join(stats)
            
            result_stats = gr.Markdown(get_result_stats())
            refresh_results_btn = gr.Button("Refresh Results")
            
            # Add download options
            with gr.Row():
                with gr.Column():
                    download_all_btn = gr.Button("Download All Results (ZIP)")
                with gr.Column():
                    result_select = gr.Dropdown(choices=[f for f in os.listdir(".") if f.endswith("_human_results.json")], label="Select Result to Download", value=None)
                    download_selected_btn = gr.Button("Download Selected")
            
            # Add results visualization placeholder
            gr.Markdown("### Results Visualization")
            gr.Markdown("*Visualization features will be added in a future update.*")
            
            # Connect events
            refresh_results_btn.click(get_result_stats, outputs=[result_stats])
            
            # Function to prepare all results for download as ZIP
            def prepare_all_results():
                import zipfile
                zip_path = "mteb_human_eval_results.zip"
                with zipfile.ZipFile(zip_path, 'w') as zipf:
                    for r in [f for f in os.listdir(".") if f.endswith("_human_results.json")]:
                        zipf.write(r)
                return zip_path
            
            # Function to return a single result file
            def get_selected_result(filename):
                if not filename:
                    return None
                if os.path.exists(filename):
                    return filename
                return None
            
            # Update dropdown when refreshing results
            def update_result_dropdown():
                return gr.Dropdown.update(choices=[f for f in os.listdir(".") if f.endswith("_human_results.json")])
            
            refresh_results_btn.click(update_result_dropdown, outputs=[result_select])
            download_all_btn.click(prepare_all_results, outputs=[gr.File(label="Download All Results")])
            download_selected_btn.click(get_selected_result, inputs=[result_select], outputs=[gr.File(label="Download Selected Result")])

if __name__ == "__main__":
    demo.launch()