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."""
    samples = task_data["samples"]
    results = {"task_name": task_data["task_name"], "task_type": "reranking", "annotations": []}
    completed_samples = {s["id"]: False for s in samples}
    
    def save_ranking(rankings, sample_id):
        """Save the current set of rankings."""
        # Check if all documents have rankings
        all_ranked = all(r is not None and r != "" for r in rankings)
        if not all_ranked:
            return "⚠️ Please assign a rank to all documents before submitting", f"Progress: {sum(completed_samples.values())}/{len(samples)}"
        
        # Convert rankings to integers
        processed_rankings = [int(r) for r in rankings]
        
        # Check for duplicate rankings
        if len(set(processed_rankings)) != len(processed_rankings):
            return "⚠️ Each document must have a unique rank. Please review your rankings.", f"Progress: {sum(completed_samples.values())}/{len(samples)}"
                
        # Store this annotation
        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] = {
                "sample_id": sample_id,
                "rankings": processed_rankings
            }
        else:
            results["annotations"].append({
                "sample_id": sample_id,
                "rankings": processed_rankings
            })
        
        completed_samples[sample_id] = True
        success_msg = f"✅ Rankings for query '{sample_id}' successfully saved!"
        progress = f"Progress: {sum(completed_samples.values())}/{len(samples)}"
        
        # Auto-save results after each submission
        output_path = f"{task_data['task_name']}_human_results.json"
        with open(output_path, "w") as f:
            json.dump(results, f, indent=2)
        
        return success_msg, progress
    
    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. Review each document carefully
            3. Assign a rank to each document (1 = most relevant, higher numbers = less relevant)
            4. Each document must have a unique rank
            5. Click "Submit Rankings" when you're done with the current query
            6. Use "Previous" and "Next" to navigate between queries
            7. 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:")
            
            # Create document displays and ranking dropdowns in synchronized pairs
            doc_containers = []
            ranking_dropdowns = []
            
            with gr.Column():
                for i, doc in enumerate(samples[0]["candidates"]):
                    with gr.Row():
                        doc_box = gr.Textbox(
                            value=doc, 
                            label=f"Document {i+1}",
                            interactive=False
                        )
                        dropdown = gr.Dropdown(
                            choices=[str(j) for j in range(1, len(samples[0]["candidates"])+1)],
                            label=f"Rank",
                            value=""
                        )
                        doc_containers.append(doc_box)
                        ranking_dropdowns.append(dropdown)
            
            with gr.Row():
                prev_btn = gr.Button("← Previous Query", size="sm")
                submit_btn = gr.Button("Submit Rankings", size="lg", variant="primary")
                next_btn = gr.Button("Next Query →", size="sm")
            
            save_btn = gr.Button("💾 Save All Results", variant="secondary")
        
        def load_sample(sample_id):
            """Load a specific sample into the interface."""
            sample = next((s for s in samples if s["id"] == sample_id), None)
            if not sample:
                return [query_text.value] + [d.value for d in doc_containers] + [""] * len(ranking_dropdowns) + [current_sample_id.value, progress_text.value, status_box.value]
            
            # Update query
            new_query = sample["query"]
            
            # Update documents
            new_docs = []
            for i, doc in enumerate(sample["candidates"]):
                if i < len(doc_containers):
                    new_docs.append(doc)
                    
            # Initialize rankings
            new_rankings = [""] * len(ranking_dropdowns)
            
            # Check if this sample has already been annotated
            existing_annotation = next((a for a in results["annotations"] if a["sample_id"] == sample_id), None)
            if existing_annotation:
                # Restore previous rankings
                for i, rank in enumerate(existing_annotation["rankings"]):
                    if i < len(new_rankings) and rank is not None:
                        new_rankings[i] = str(rank)
            
            # Update progress
            current_idx = samples.index(sample)
            new_progress = f"Progress: {sum(completed_samples.values())}/{len(samples)}"
            
            new_status = f"Viewing query {current_idx + 1} of {len(samples)}"
            if completed_samples[sample_id]:
                new_status += " (already completed)"
            
            return [new_query] + new_docs + new_rankings + [sample["id"], new_progress, new_status]
        
        def next_sample(current_id):
            """Load the next sample."""
            current_sample = next((s for s in samples if s["id"] == current_id), None)
            if not current_sample:
                return current_id
            
            current_idx = samples.index(current_sample)
            if current_idx < len(samples) - 1:
                next_sample = samples[current_idx + 1]
                return next_sample["id"]
            return current_id
        
        def prev_sample(current_id):
            """Load the previous sample."""
            current_sample = next((s for s in samples if s["id"] == current_id), None)
            if not current_sample:
                return current_id
            
            current_idx = samples.index(current_sample)
            if current_idx > 0:
                prev_sample = samples[current_idx - 1]
                return prev_sample["id"]
            return current_id
        
        def save_results():
            """Save all collected results to a file."""
            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"✅ Results saved to {output_path} ({len(results['annotations'])} annotations)"
        
        # Connect events
        submit_btn.click(
            save_ranking,
            inputs=ranking_dropdowns + [current_sample_id],
            outputs=[status_box, progress_text]
        )
        
        next_btn.click(
            next_sample,
            inputs=[current_sample_id],
            outputs=[current_sample_id]
        ).then(
            load_sample,
            inputs=[current_sample_id],
            outputs=[query_text] + doc_containers + ranking_dropdowns + [current_sample_id, progress_text, status_box]
        )
        
        prev_btn.click(
            prev_sample,
            inputs=[current_sample_id],
            outputs=[current_sample_id]
        ).then(
            load_sample,
            inputs=[current_sample_id],
            outputs=[query_text] + doc_containers + ranking_dropdowns + [current_sample_id, progress_text, status_box]
        )
        
        save_btn.click(save_results, outputs=[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(scale=1):
                    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(scale=2):
                    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():
                download_all_btn = gr.Button("Download All Results (ZIP)")
                result_select = gr.Dropdown(choices=[f for f in os.listdir(".") if f.endswith("_human_results.json")], label="Select Result to Download")
                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()