app.py

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

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}
    
    # Load existing results if available
    output_path = f"{task_data['task_name']}_human_results.json"
    if os.path.exists(output_path):
        try:
            with open(output_path, "r") as f:
                saved_results = json.load(f)
                if "annotations" in saved_results:
                    results["annotations"] = saved_results["annotations"]
                    # Update completed_samples based on loaded data
                    for annotation in saved_results["annotations"]:
                        sample_id = annotation.get("sample_id")
                        if sample_id and sample_id in completed_samples:
                            completed_samples[sample_id] = True
        except Exception as e:
            print(f"Error loading existing results: {e}")
    
    def save_ranking(rankings, sample_id):
        """Save the current set of rankings."""
        try:
            # Check if all documents have rankings
            if not rankings or len(rankings) == 0:
                return "⚠️ No rankings provided", f"Progress: {sum(completed_samples.values())}/{len(samples)}"
                
            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 with better error handling
            try:
                processed_rankings = [int(r) for r in rankings]
            except ValueError:
                return "⚠️ Invalid ranking value. Please use only numbers.", f"Progress: {sum(completed_samples.values())}/{len(samples)}"
            
            # 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 in memory
            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
            
            # Always save to file for redundancy
            try:
                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", f"Progress: {sum(completed_samples.values())}/{len(samples)}"
            except Exception as file_error:
                # If file saving fails, still mark as success since we saved in memory
                print(f"File save error: {file_error}")
                return f"✅ Rankings saved in memory (file save failed)", f"Progress: {sum(completed_samples.values())}/{len(samples)}"
        except Exception as e:
            # Return specific error message
            print(f"Save ranking error: {e}")
            return f"Error: {str(e)}", f"Progress: {sum(completed_samples.values())}/{len(samples)}"
    
    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. Your rankings are automatically saved when you submit or navigate
            """.format(instructions=task_data.get("instructions", "Rank documents by their relevance to the query.")))
        
        current_sample_id = gr.State(value=samples[0]["id"])
        current_state = gr.State(value={"auto_save_enabled": True, "last_saved": time.time()})
        
        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)
            auto_save_toggle = gr.Checkbox(label="Auto-save when navigating", value=True)
        
        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 inputs in synchronized pairs
            doc_containers = []
            ranking_inputs = []
            validation_indicators = []
            
            with gr.Column():
                # Quick ranking tools
                with gr.Row():
                    gr.Markdown("### Quick Ranking Options:")
                    sequential_btn = gr.Button("Rank in Order (1,2,3...)")
                    reverse_btn = gr.Button("Reverse Order (n,n-1,...)")
                    clear_btn = gr.Button("Clear All Rankings")
                
                # Document display with better UI for ranking
                for i, doc in enumerate(samples[0]["candidates"]):
                    with gr.Row():
                        with gr.Column(scale=4):
                            doc_box = gr.Textbox(
                                value=doc, 
                                label=f"Document {i+1}",
                                interactive=False
                            )
                            doc_containers.append(doc_box)
                        
                        with gr.Column(scale=1):
                            # Use Radio buttons for ranking rather than dropdowns
                            # This provides a more visual and error-resistant interface
                            rank_input = gr.Radio(
                                choices=[str(j) for j in range(1, len(samples[0]["candidates"])+1)],
                                label=f"Rank",
                                value="",
                                interactive=True
                            )
                            ranking_inputs.append(rank_input)
                        
                        # Add validation indicator
                        with gr.Column(scale=1, min_width=50):
                            validation = gr.HTML(value="")
                            validation_indicators.append(validation)
            
            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")
            
            with gr.Row():
                save_btn = gr.Button("💾 Save All Results", variant="secondary")
                results_info = gr.HTML(value=f"<p>Results will be saved to <code>{task_data['task_name']}_human_results.json</code></p>")
        
        def validate_rankings(*rankings):
            """Validate rankings and update indicators."""
            results = []
            all_valid = True
            for rank in rankings:
                if rank is None or rank == "":
                    results.append("⚠️")
                    all_valid = False
                else:
                    results.append("✓")
            
            return results, all_valid
        
        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_inputs) + validation_indicators + [sample_id, 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_inputs)
            
            # 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)"
            
            # Initialize validation indicators
            validation_results, _ = validate_rankings(*new_rankings)
            
            return [new_query] + new_docs + new_rankings + validation_results + [sample_id, new_progress, new_status]
        
        def auto_save_and_navigate(direction, current_id, auto_save, *rankings):
            """Save rankings if auto-save is enabled, then navigate."""
            # Extract rankings (remove validation indicators)
            actual_rankings = rankings[:len(ranking_inputs)]
            
            # If auto-save is enabled, try to save the current rankings
            status_msg = ""
            progress_msg = f"Progress: {sum(completed_samples.values())}/{len(samples)}"
            
            if auto_save:
                # Only save if all rankings are provided
                validation_results, all_valid = validate_rankings(*actual_rankings)
                if all_valid:
                    status_msg, progress_msg = save_ranking(actual_rankings, current_id)
            
            # Navigate to the next/previous sample
            if direction == "next":
                new_id = next_sample(current_id)
            else:
                new_id = prev_sample(current_id)
            
            # Return the new sample ID and status message
            return new_id, status_msg, progress_msg
        
        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"
            try:
                with open(output_path, "w") as f:
                    json.dump(results, f, indent=2)
                current_state.value["last_saved"] = time.time()
                return f"✅ Results saved to {output_path} ({len(results['annotations'])} annotations)"
            except Exception as e:
                return f"Error saving results: {str(e)}"
        
        # Function to assign sequential ranks
        def assign_sequential_ranks():
            return [str(i+1) for i in range(len(samples[0]["candidates"]))]
        
        # Function to assign reverse ranks
        def assign_reverse_ranks():
            n = len(samples[0]["candidates"])
            return [str(n-i) for i in range(n)]
        
        # Function to clear all rankings
        def clear_rankings():
            return ["" for _ in range(len(samples[0]["candidates"]))]
        
        # Define a function that collects all ranking values and validates them
        def submit_rankings(*args):
            # Get the last argument (sample_id) and the rankings
            if len(args) < 1:
                return "Error: No arguments provided", progress_text.value
            
            # Verify we have enough rankings
            if len(args) < len(ranking_inputs) + 1:
                return "Error: Not enough ranking inputs provided", progress_text.value
            
            sample_id = args[-1]
            rankings = args[:len(ranking_inputs)]
            
            # First validate the rankings
            validation_results, all_valid = validate_rankings(*rankings)
            
            # Update validation indicators
            for i, result in enumerate(validation_results):
                validation_indicators[i].update(value=result)
            
            # If not all valid, return error message
            if not all_valid:
                return "⚠️ Please assign a rank to all documents before submitting", progress_text.value
            
            # Save the validated rankings
            status, progress = save_ranking(rankings, sample_id)
            return status, progress
        
        # Connect events - Direct input/output connections for reliability
        submit_btn.click(
            submit_rankings,
            inputs=ranking_inputs + [current_sample_id],
            outputs=[status_box, progress_text]
        )
        
        # Apply auto-save before navigation if enabled
        next_btn.click(
            auto_save_and_navigate,
            inputs=["next", current_sample_id, auto_save_toggle] + ranking_inputs,
            outputs=[current_sample_id, status_box, progress_text]
        ).then(
            load_sample,
            inputs=[current_sample_id],
            outputs=[query_text] + doc_containers + ranking_inputs + validation_indicators + [current_sample_id, progress_text, status_box]
        )
        
        prev_btn.click(
            auto_save_and_navigate,
            inputs=["prev", current_sample_id, auto_save_toggle] + ranking_inputs,
            outputs=[current_sample_id, status_box, progress_text]
        ).then(
            load_sample,
            inputs=[current_sample_id],
            outputs=[query_text] + doc_containers + ranking_inputs + validation_indicators + [current_sample_id, progress_text, status_box]
        )
        
        # Connect quick ranking buttons
        sequential_btn.click(
            assign_sequential_ranks,
            outputs=ranking_inputs
        )
        
        reverse_btn.click(
            assign_reverse_ranks,
            outputs=ranking_inputs
        )
        
        clear_btn.click(
            clear_rankings,
            outputs=ranking_inputs
        )
        
        # Connect save button
        save_btn.click(save_results, outputs=[status_box])
        
        # Add validation on ranking changes
        for i, ranking in enumerate(ranking_inputs):
            ranking.change(
                validate_rankings,
                inputs=ranking_inputs,
                outputs=validation_indicators + [gr.State(value=None)]  # Add dummy output to match function return
            )
        
        # Set up auto-save feature
        auto_save_toggle.change(
            lambda x: {"auto_save_enabled": x},
            inputs=[auto_save_toggle],
            outputs=[current_state]
        )
    
    return demo

# Main app with file upload capability and improved task management
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)
                    task_path = os.path.join("uploaded_tasks", uploaded_tasks[0])
                    
                    # Verify this is a valid task file
                    try:
                        with open(task_path, "r") as f:
                            task_data = json.load(f)
                            if "task_name" in task_data and "samples" in task_data:
                                return task_path
                    except:
                        pass
                
                # Look for task files in the current directory
                current_dir_tasks = [f for f in os.listdir(".") if f.endswith("_human_eval.json")]
                if current_dir_tasks:
                    # Sort by modification time, newest first
                    current_dir_tasks.sort(key=lambda x: os.path.getmtime(x), reverse=True)
                    return current_dir_tasks[0]
                
                # Fall back to fixed example if available
                if os.path.exists("AskUbuntuDupQuestions_human_eval.json"):
                    return "AskUbuntuDupQuestions_human_eval.json"
                
                # No valid task file found
                return None
            
            # Load the task file
            task_file = get_latest_task_file()
            
            if 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.")
            else:
                gr.Markdown("**No task file found**")
                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}" 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()
                    loaded_task_info = gr.JSON(label="Loaded Task Information", visible=False)
            
            # Handle file upload and storage
            def handle_upload(file):
                if not file:
                    return "Please upload a task file", task_list.value, task_container.value, loaded_task_info.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, loaded_task_info.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)
                    
                    # Show task info
                    task_info = {
                        "task_name": task_data["task_name"],
                        "samples": len(task_data["samples"]),
                        "file_path": task_filename
                    }
                    
                    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>
                    """, task_info
                except Exception as e:
                    return f"Error processing task file: {str(e)}", task_list.value, task_container.value, loaded_task_info.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, loaded_task_info])
            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
                        
                        # Try uploaded_tasks directory first
                        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", []))
                        else:
                            # Try human_eval file in current directory
                            task_file = f"{task_name}_human_eval.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")
            
            # 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()