app.py

import gradio as gr
import os
import yaml
import json
import random
import re
from datasets import load_dataset, get_dataset_config_names, get_dataset_split_names
from openai import OpenAI
from openevolve import run_evolution
from typing import Dict, List, Tuple, Optional
import tempfile
import shutil
import requests
import glob

# Free models from OpenRouter - Curated selection (verified as of 2025)
# IMPORTANT: The :free suffix is REQUIRED to use the free tier. Without it, requests are charged!
FREE_MODELS = [
    "qwen/qwen-2.5-72b-instruct:free",  # 72B - Strong in coding/math/multilingual (default - better rate limits)
    "meta-llama/llama-3.3-70b-instruct:free",  # 70B - Advanced reasoning
    "google/gemma-3-27b-it:free",  # 27B - Strong instruction-tuned
    "mistralai/mistral-small-3.1-24b-instruct:free",  # 24B - Efficient and capable
    "deepseek/deepseek-r1:free",  # 671B (37B active) - Top-tier but heavily rate-limited
    "meta-llama/llama-3.2-3b-instruct",  # 3B - PAID but very cheap fallback when free models hit rate limits
]


def validate_dataset(dataset_name: str, split: str, input_field: str, target_field: str) -> Tuple[bool, str]:
    """
    Validate that the dataset exists and has the required fields.

    Returns:
        Tuple of (is_valid, error_message)
    """
    try:
        # Check if dataset name has correct format (should be org/name or just name)
        if not dataset_name or dataset_name.strip() == "":
            return False, "❌ Dataset name cannot be empty"

        dataset_name = dataset_name.strip()

        # Try to get dataset info from HuggingFace API
        hf_token = os.environ.get("HF_TOKEN", None)
        headers = {}
        if hf_token:
            headers["Authorization"] = f"Bearer {hf_token}"

        # Check if dataset exists on HuggingFace Hub
        api_url = f"https://huggingface.co/api/datasets/{dataset_name}"
        response = requests.get(api_url, headers=headers, timeout=10)

        if response.status_code == 404:
            return False, f"❌ Dataset '{dataset_name}' not found on HuggingFace Hub. Please use the full dataset name (e.g., 'stanfordnlp/imdb' or 'gsm8k')"
        elif response.status_code != 200:
            # Try to load anyway - might be a private dataset or API issue
            print(f"Warning: Could not verify dataset via API (status {response.status_code}), attempting to load...")

        # Try to load a small sample to verify it works and check fields
        print(f"Loading dataset {dataset_name} with split {split}...")

        # First, check if the split exists
        try:
            available_splits = get_dataset_split_names(dataset_name)
            if split not in available_splits:
                return False, f"❌ Split '{split}' not found. Available splits: {', '.join(available_splits)}"
        except Exception as e:
            print(f"Could not get split names: {e}. Will try to load anyway...")

        # Load a small sample to check fields
        # Try loading with just dataset name first
        try:
            dataset = load_dataset(dataset_name, split=split, streaming=True)
        except ValueError as e:
            # If it fails with config error, try with "main" config (common for datasets like gsm8k)
            if "config" in str(e).lower() or "Config name is missing" in str(e):
                print(f"Dataset requires config, trying with 'main' config...")
                try:
                    dataset = load_dataset(dataset_name, "main", split=split, streaming=True)
                except:
                    # If "main" doesn't work, raise the original error
                    raise e
            else:
                raise

        # Get first example to check fields
        first_example = next(iter(dataset))
        available_fields = list(first_example.keys())

        # Check if input field exists
        if input_field not in available_fields:
            return False, f"❌ Input field '{input_field}' not found. Available fields: {', '.join(available_fields)}"

        # Check if target field exists
        if target_field not in available_fields:
            return False, f"❌ Target field '{target_field}' not found. Available fields: {', '.join(available_fields)}"

        # All validations passed
        return True, f"✅ Dataset validated successfully! Fields '{input_field}' and '{target_field}' found."

    except Exception as e:
        error_msg = str(e)
        if "404" in error_msg or "not found" in error_msg.lower():
            return False, f"❌ Dataset '{dataset_name}' not found. Please check the dataset name (use format: org/dataset-name)"
        return False, f"❌ Error validating dataset: {error_msg}"


def validate_inputs(dataset_name: str, split: str, input_field: str, target_field: str,
                   initial_prompt: str) -> Tuple[bool, str]:
    """
    Validate all inputs before starting optimization.

    Returns:
        Tuple of (is_valid, message)
    """
    # Check API key
    api_key = os.environ.get("OPENAI_API_KEY")
    if not api_key:
        return False, "❌ OPENAI_API_KEY environment variable not set. Please set it in the Space secrets."

    # Check prompt contains {input} placeholder
    if "{input}" not in initial_prompt:
        return False, "❌ Prompt must contain '{input}' placeholder for dataset inputs"

    # Check dataset name format
    dataset_name = dataset_name.strip()
    if not dataset_name:
        return False, "❌ Dataset name cannot be empty"

    # Validate dataset and fields
    is_valid, message = validate_dataset(dataset_name, split, input_field, target_field)
    if not is_valid:
        return False, message

    return True, message


def evaluate_prompt(prompt: str, dataset_name: str, split: str, num_samples: int,
                    model: str, input_field: str, target_field: str) -> Dict:
    """Evaluate a prompt on a dataset using the selected model."""
    try:
        # Get API key from environment
        api_key = os.environ.get("OPENAI_API_KEY")
        if not api_key:
            return {
                "error": "OPENAI_API_KEY not set in environment",
                "accuracy": 0,
                "correct": 0,
                "total": 0,
                "results": []
            }

        # Load dataset
        # Try loading with just dataset name first
        try:
            dataset = load_dataset(dataset_name, split=split, streaming=False)
        except ValueError as e:
            # If it fails with config error, try with "main" config (common for datasets like gsm8k)
            if "config" in str(e).lower() or "Config name is missing" in str(e):
                dataset = load_dataset(dataset_name, "main", split=split, streaming=False)
            else:
                raise

        # Sample random examples
        if len(dataset) > num_samples:
            indices = random.sample(range(len(dataset)), num_samples)
            samples = [dataset[i] for i in indices]
        else:
            samples = list(dataset)[:num_samples]

        # Initialize OpenAI client with OpenRouter
        client = OpenAI(
            base_url="https://openrouter.ai/api/v1",
            api_key=api_key,
        )

        correct = 0
        total = 0
        results = []
        errors = []

        for idx, sample in enumerate(samples):
            try:
                # Get input and target
                input_text = sample.get(input_field, "")
                if isinstance(input_text, dict):
                    input_text = str(input_text)

                target = sample.get(target_field, "")
                if isinstance(target, dict):
                    target = str(target)

                # Format the prompt with the input
                formatted_prompt = prompt.replace("{input}", str(input_text))

                # Call the model
                response = client.chat.completions.create(
                    model=model,
                    messages=[
                        {"role": "system", "content": "You are a helpful assistant."},
                        {"role": "user", "content": formatted_prompt}
                    ],
                    temperature=0.1,
                    max_tokens=500,
                )

                prediction = response.choices[0].message.content.strip()

                # Smart evaluation - handle both math and text answers
                target_str = str(target).strip()
                pred_str = prediction.strip()

                def extract_answer(text):
                    """Extract answer from text - handles GSM8K format and general text"""
                    # GSM8K format: "#### NUMBER" at the end
                    if "####" in text:
                        parts = text.split("####")
                        if len(parts) > 1:
                            answer_part = parts[-1].strip()
                            # Remove comma separators (1,000 -> 1000)
                            answer_part = answer_part.replace(',', '')
                            return answer_part

                    # Try to extract last number from free-form text
                    numbers = re.findall(r'-?\d+(?:,\d{3})*(?:\.\d+)?', text)
                    if numbers:
                        # Return the last number found (usually the final answer)
                        return numbers[-1].replace(',', '')

                    return text

                def is_mathematically_equal(str1, str2):
                    """Check if two strings represent the same mathematical value"""
                    try:
                        # Try to convert both to floats and compare
                        num1 = float(str1.replace(',', ''))
                        num2 = float(str2.replace(',', ''))
                        # Use small epsilon for float comparison
                        return abs(num1 - num2) < 1e-6
                    except (ValueError, AttributeError):
                        # If conversion fails, do string comparison
                        return str1.lower().strip() == str2.lower().strip()

                # Extract answers
                target_answer = extract_answer(target_str)
                pred_answer = extract_answer(pred_str)

                # Check if answers match mathematically or textually
                is_correct = is_mathematically_equal(target_answer, pred_answer)

                # Fallback: check for semantic equivalents for sentiment analysis
                if not is_correct:
                    target_lower = target_answer.lower()
                    pred_lower = pred_answer.lower()

                    # Sentiment mappings
                    if target_lower in ["1", "positive", "pos"]:
                        is_correct = any(word in pred_lower for word in ["positive", "good", "great", "1"])
                    elif target_lower in ["0", "negative", "neg"]:
                        is_correct = any(word in pred_lower for word in ["negative", "bad", "poor", "0"])

                if is_correct:
                    correct += 1
                total += 1

                results.append({
                    "input": str(input_text)[:100] + "..." if len(str(input_text)) > 100 else str(input_text),
                    "target": str(target),
                    "prediction": prediction[:100] + "..." if len(prediction) > 100 else prediction,
                    "correct": is_correct
                })

            except Exception as e:
                error_msg = f"Sample {idx+1}: {str(e)}"
                print(f"Error evaluating sample {idx+1}: {e}")
                errors.append(error_msg)
                # Only continue if we haven't failed on all samples
                if len(errors) > len(samples) // 2:  # More than half failed
                    print(f"Too many errors ({len(errors)} out of {len(samples)}), stopping evaluation")
                    break
                continue

        accuracy = (correct / total * 100) if total > 0 else 0

        result_dict = {
            "accuracy": accuracy,
            "correct": correct,
            "total": total,
            "results": results
        }

        # Add errors if any occurred
        if errors:
            result_dict["errors"] = errors
            if total == 0:
                # All samples failed - create a helpful error message
                result_dict["error"] = f"All {len(samples)} samples failed to evaluate. First few errors:\n" + "\n".join(errors[:3])

        return result_dict

    except Exception as e:
        return {
            "error": str(e),
            "accuracy": 0,
            "correct": 0,
            "total": 0,
            "results": []
        }


def collect_prompt_history(output_dir: str, initial_score: float = 0.0) -> List[Dict]:
    """
    Collect only the prompts that were "best" at some point during evolution.
    Returns only programs that improved upon the initial score (deduplicated).

    Args:
        output_dir: Directory containing checkpoint data
        initial_score: Score of the initial prompt (baseline to beat)

    Returns a list of dicts with: {prompt, score, iteration, id}
    """
    try:
        all_programs = []
        seen_prompts = set()  # Track unique prompts

        # OpenEvolve saves programs in checkpoint directories as JSON files
        # Structure: output_dir/checkpoints/checkpoint_{iteration}/programs/{program_id}.json
        checkpoints_dir = os.path.join(output_dir, "checkpoints")

        if not os.path.exists(checkpoints_dir):
            return []

        # Find all checkpoint directories
        checkpoint_dirs = sorted(glob.glob(os.path.join(checkpoints_dir, "checkpoint_*")))

        # Collect all programs from all checkpoints
        for checkpoint_dir in checkpoint_dirs:
            programs_dir = os.path.join(checkpoint_dir, "programs")
            if not os.path.exists(programs_dir):
                continue

            # Read all program JSON files
            program_files = glob.glob(os.path.join(programs_dir, "*.json"))

            for pfile in program_files:
                try:
                    with open(pfile, 'r') as f:
                        program_data = json.load(f)

                    # Extract the code (prompt) from the program data
                    prompt_content = program_data.get("code", "").strip()
                    prog_id = program_data.get("id", os.path.basename(pfile).replace(".json", ""))
                    iteration = program_data.get("iteration_found", 0)
                    metrics = program_data.get("metrics", {})

                    # Get combined score for comparison
                    combined_score = metrics.get("combined_score", 0.0)

                    all_programs.append({
                        "prompt": prompt_content,
                        "id": prog_id,
                        "file": pfile,
                        "iteration": iteration,
                        "metrics": metrics,
                        "score": combined_score
                    })
                except Exception as e:
                    print(f"Error reading program file {pfile}: {e}")
                    continue

        # Sort all programs by iteration (chronological order)
        all_programs.sort(key=lambda x: x.get("iteration", 0))

        # Filter to keep only programs that improved the best score
        # Start from the initial score as the baseline
        best_programs = []
        current_best_score = initial_score

        for program in all_programs:
            prompt_content = program["prompt"]
            score = program["score"]
            iteration = program["iteration"]

            # Skip iteration 0 (that's the initial prompt, already added separately)
            if iteration == 0:
                continue

            # Create a normalized version for duplicate detection (ignore whitespace differences)
            normalized_prompt = " ".join(prompt_content.split())

            # Skip duplicates
            if normalized_prompt in seen_prompts:
                continue

            # Only keep if this program improved the best score
            if score > current_best_score:
                seen_prompts.add(normalized_prompt)
                best_programs.append(program)
                improvement = score - current_best_score
                print(f"  ✓ Best program at iteration {iteration}: score={score:.2%} (improved by +{improvement:.2%})")
                current_best_score = score

        return best_programs

    except Exception as e:
        print(f"Error collecting prompt history: {e}")
        return []


def parse_evolution_history(output_dir: str) -> str:
    """
    Parse evolution history from OpenEvolve output directory.

    Returns a markdown string with visualization of the evolution process.
    """
    try:
        evolution_viz = "## 🧬 Evolution Progress\n\n"

        # Look for generation files or logs
        generation_files = sorted(glob.glob(os.path.join(output_dir, "generation_*.txt")))
        log_file = os.path.join(output_dir, "evolution.log")

        # Try to parse generation files if they exist
        if generation_files:
            evolution_viz += "### Generation-by-Generation Progress\n\n"
            for gen_file in generation_files:
                gen_num = os.path.basename(gen_file).replace("generation_", "").replace(".txt", "")
                try:
                    with open(gen_file, 'r') as f:
                        content = f.read()
                    evolution_viz += f"**Generation {gen_num}:**\n```\n{content[:200]}{'...' if len(content) > 200 else ''}\n```\n\n"
                except:
                    pass

        # Try to parse log file
        elif os.path.exists(log_file):
            evolution_viz += "### Evolution Log\n\n"
            try:
                with open(log_file, 'r') as f:
                    log_content = f.read()
                evolution_viz += f"```\n{log_content[-1000:]}\n```\n\n"
            except:
                pass

        # Look for scores or history file
        scores_file = os.path.join(output_dir, "scores.json")
        if os.path.exists(scores_file):
            try:
                with open(scores_file, 'r') as f:
                    scores = json.load(f)

                evolution_viz += "### Score Progression\n\n"
                evolution_viz += "| Generation | Best Score | Avg Score | Population |\n"
                evolution_viz += "|------------|-----------|-----------|------------|\n"

                for gen in scores:
                    evolution_viz += f"| {gen['generation']} | {gen['best']:.3f} | {gen['avg']:.3f} | {gen['population']} |\n"

                evolution_viz += "\n"
            except:
                pass

        # Look for all program variants
        program_files = sorted(glob.glob(os.path.join(output_dir, "program_*.txt")))
        if program_files:
            evolution_viz += f"### Explored Variants\n\n"
            evolution_viz += f"OpenEvolve explored {len(program_files)} different prompt variants during evolution.\n\n"

            # Show a few intermediate prompts
            if len(program_files) > 3:
                sample_files = [program_files[0], program_files[len(program_files)//2], program_files[-2]]
                evolution_viz += "**Sample Intermediate Prompts:**\n\n"
                for idx, pfile in enumerate(sample_files, 1):
                    try:
                        with open(pfile, 'r') as f:
                            prompt_content = f.read()
                        evolution_viz += f"**Variant {idx}:**\n```\n{prompt_content[:150]}{'...' if len(prompt_content) > 150 else ''}\n```\n\n"
                    except:
                        pass

        # If no specific files found, show directory contents
        if not generation_files and not os.path.exists(log_file) and not os.path.exists(scores_file):
            evolution_viz += "### Evolution Complete\n\n"
            evolution_viz += "OpenEvolve ran 10 iterations of evolutionary optimization using:\n"
            evolution_viz += "- **Population Size**: 10 prompts per generation\n"
            evolution_viz += "- **Selection Strategy**: 10% elite, 30% explore, 60% exploit\n"
            evolution_viz += "- **Islands**: 1 population with mutation and crossover\n"
            evolution_viz += "- **Evaluation**: 100 samples per prompt variant\n\n"

            # Count files in output directory
            all_files = os.listdir(output_dir)
            evolution_viz += f"Generated {len(all_files)} files during evolution process.\n\n"

        return evolution_viz

    except Exception as e:
        return f"## 🧬 Evolution Progress\n\nEvolution completed successfully. Unable to parse detailed history: {str(e)}\n\n"


def create_evaluator_file(dataset_name: str, split: str, model: str,
                         input_field: str, target_field: str, work_dir: str):
    """Create an evaluator.py file for OpenEvolve with staged/cascading evaluation."""
    evaluator_code = f'''
import os
import random
from datasets import load_dataset
from openai import OpenAI

def evaluate(prompt: str) -> dict:
    """
    Evaluate a prompt using 2-stage cascading evaluation to save API calls.

    Stage 1: Evaluate with 20 samples
    - If accuracy >= 0.5, proceed to Stage 2
    - If accuracy < 0.5, return early (no point wasting 80 more samples)

    Stage 2: Evaluate with 80 more samples (total 100)
    - Combine results for final score

    Returns dict with combined_score (0-1), accuracy, correct, and total.
    """
    try:
        # Load dataset
        # Try loading with just dataset name first
        try:
            dataset = load_dataset("{dataset_name}", split="{split}", streaming=False)
        except ValueError as e:
            # If it fails with config error, try with "main" config (common for datasets like gsm8k)
            if "config" in str(e).lower() or "Config name is missing" in str(e):
                dataset = load_dataset("{dataset_name}", "main", split="{split}", streaming=False)
            else:
                raise

        # Initialize OpenAI client
        api_key = os.environ.get("OPENAI_API_KEY")
        client = OpenAI(
            base_url="https://openrouter.ai/api/v1",
            api_key=api_key,
        )

        def evaluate_samples(samples, correct_so_far=0, total_so_far=0):
            """Helper function to evaluate a batch of samples."""
            correct = correct_so_far
            total = total_so_far

            for sample in samples:
                try:
                    # Get input and target
                    input_text = sample.get("{input_field}", "")
                    if isinstance(input_text, dict):
                        input_text = str(input_text)

                    target = sample.get("{target_field}", "")
                    if isinstance(target, dict):
                        target = str(target)

                    # Format the prompt
                    formatted_prompt = prompt.replace("{{input}}", str(input_text))

                    # Call the model
                    response = client.chat.completions.create(
                        model="{model}",
                        messages=[
                            {{"role": "system", "content": "You are a helpful assistant."}},
                            {{"role": "user", "content": formatted_prompt}}
                        ],
                        temperature=0.1,
                        max_tokens=500,
                    )

                    prediction = response.choices[0].message.content.strip()

                    # Smart evaluation - handle both math and text answers
                    target_str = str(target).strip()
                    pred_str = prediction.strip()

                    def extract_answer(text):
                        """Extract answer from text - handles GSM8K format and general text"""
                        import re

                        # GSM8K format: "#### NUMBER" at the end
                        if "####" in text:
                            parts = text.split("####")
                            if len(parts) > 1:
                                answer_part = parts[-1].strip()
                                # Remove comma separators (1,000 -> 1000)
                                answer_part = answer_part.replace(',', '')
                                return answer_part

                        # Try to extract last number from free-form text
                        numbers = re.findall(r'-?\\d+(?:,\\d{{3}})*(?:\\.\\d+)?', text)
                        if numbers:
                            # Return the last number found (usually the final answer)
                            return numbers[-1].replace(',', '')

                        return text

                    def is_mathematically_equal(str1, str2):
                        """Check if two strings represent the same mathematical value"""
                        try:
                            # Try to convert both to floats and compare
                            num1 = float(str1.replace(',', ''))
                            num2 = float(str2.replace(',', ''))
                            # Use small epsilon for float comparison
                            return abs(num1 - num2) < 1e-6
                        except (ValueError, AttributeError):
                            # If conversion fails, do string comparison
                            return str1.lower().strip() == str2.lower().strip()

                    # Extract answers
                    target_answer = extract_answer(target_str)
                    pred_answer = extract_answer(pred_str)

                    # Check if answers match mathematically or textually
                    is_correct = is_mathematically_equal(target_answer, pred_answer)

                    # Fallback: check for semantic equivalents for sentiment analysis
                    if not is_correct:
                        target_lower = target_answer.lower()
                        pred_lower = pred_answer.lower()

                        # Sentiment mappings
                        if target_lower in ["1", "positive", "pos"]:
                            is_correct = any(word in pred_lower for word in ["positive", "good", "great", "1"])
                        elif target_lower in ["0", "negative", "neg"]:
                            is_correct = any(word in pred_lower for word in ["negative", "bad", "poor", "0"])

                    if is_correct:
                        correct += 1
                    total += 1

                except Exception as e:
                    print(f"Error evaluating sample: {{e}}")
                    continue

            return correct, total

        # STAGE 1: Evaluate with 20 samples first
        stage1_size = 20
        stage1_samples_count = min(stage1_size, len(dataset))

        if len(dataset) > stage1_samples_count:
            stage1_indices = random.sample(range(len(dataset)), stage1_samples_count)
            stage1_samples = [dataset[i] for i in stage1_indices]
        else:
            stage1_samples = list(dataset)[:stage1_samples_count]

        print(f"[Stage 1/2] Evaluating with {{len(stage1_samples)}} samples...")
        correct, total = evaluate_samples(stage1_samples)
        stage1_score = (correct / total) if total > 0 else 0.0

        print(f"[Stage 1/2] Score: {{stage1_score:.3f}} ({{correct}}/{{total}})")

        # Early exit if Stage 1 score is below threshold
        if stage1_score < 0.5:
            print(f"[Stage 1/2] Score below 0.5 threshold - skipping Stage 2 (saved 80 API calls)")
            return {{
                "combined_score": stage1_score,
                "accuracy": stage1_score,
                "correct": correct,
                "total": total,
                "stage": "stage1_early_exit"
            }}

        # STAGE 2: Continue with 80 more samples
        print(f"[Stage 2/2] Score >= 0.5 - proceeding with 80 more samples...")
        stage2_size = 80
        stage2_samples_count = min(stage2_size, max(0, len(dataset) - stage1_samples_count))

        if stage2_samples_count > 0:
            # Get different samples from Stage 1
            remaining_indices = list(set(range(len(dataset))) - set(stage1_indices if 'stage1_indices' in locals() else []))

            if len(remaining_indices) >= stage2_samples_count:
                stage2_indices = random.sample(remaining_indices, stage2_samples_count)
                stage2_samples = [dataset[i] for i in stage2_indices]
            else:
                stage2_samples = [dataset[i] for i in remaining_indices[:stage2_samples_count]]

            correct, total = evaluate_samples(stage2_samples, correct, total)
            final_score = (correct / total) if total > 0 else stage1_score

            print(f"[Stage 2/2] Final score: {{final_score:.3f}} ({{correct}}/{{total}})")
            return {{
                "combined_score": final_score,
                "accuracy": final_score,
                "correct": correct,
                "total": total,
                "stage": "stage2_complete"
            }}
        else:
            print(f"[Stage 2/2] Not enough samples in dataset for Stage 2")
            return {{
                "combined_score": stage1_score,
                "accuracy": stage1_score,
                "correct": correct,
                "total": total,
                "stage": "stage1_complete"
            }}

    except Exception as e:
        print(f"Error in evaluation: {{e}}")
        return {{
            "combined_score": 0.0,
            "accuracy": 0.0,
            "correct": 0,
            "total": 0,
            "error": str(e)
        }}
'''

    evaluator_path = os.path.join(work_dir, "evaluator.py")
    with open(evaluator_path, "w") as f:
        f.write(evaluator_code)

    return evaluator_path


def create_config_file(model: str, work_dir: str):
    """Create a config.yaml file for OpenEvolve."""

    # Create custom templates directory for prompt optimization
    templates_dir = os.path.join(work_dir, "templates")
    os.makedirs(templates_dir, exist_ok=True)

    # Create custom system template for PROMPT optimization (not code)
    system_template = """You are an expert prompt engineer tasked with iteratively improving prompts for language models.
Your job is to analyze the current prompt and suggest improvements based on performance feedback.
Focus on making the prompt clearer, more specific, and more effective at achieving its goal.
Consider:
- Clarity and specificity of instructions
- Examples and demonstrations that guide the model
- Formatting that makes the prompt easier to follow
- Edge cases and error handling in the instructions
"""

    with open(os.path.join(templates_dir, "system_message.txt"), "w") as f:
        f.write(system_template)

    # Create custom user template for prompt rewriting
    user_template = """# Current Prompt Performance
- Current metrics: {metrics}
- Areas for improvement: {improvement_areas}

{artifacts}

# Prompt Evolution History
{evolution_history}

# Current Prompt
```text
{current_program}
```

# Task
Rewrite the prompt above to improve its performance on the specified metrics.
Provide a complete new version of the prompt that:
1. Maintains the same input/output format (keep placeholders like {{input}}, {{text}}, etc.)
2. Improves clarity and effectiveness
3. Adds helpful examples or instructions if beneficial
4. Is more likely to get correct results

Output ONLY the new prompt text between ```text markers:

```text
Your improved prompt here
```
"""

    with open(os.path.join(templates_dir, "full_rewrite_user.txt"), "w") as f:
        f.write(user_template)

    config = {
        "llm": {
            "primary_model": model,
            "api_base": "https://openrouter.ai/api/v1",  # Use OpenRouter endpoint
            "temperature": 0.7,
        },
        "max_iterations": 10,
        "checkpoint_interval": 2,  # Save checkpoints every 2 iterations to preserve prompt history
        "diff_based_evolution": False,  # Use full rewrite mode for prompts (not diff/patch mode)
        "language": "text",  # CRITICAL: Optimize text/prompts, not Python code!
        "max_code_length": 40000,  # Allow long prompts (default 10000 is too short)
        "prompt": {
            "template_dir": templates_dir,  # Use our custom prompt engineering templates
        },
        "evolution": {
            "population_size": 10,
            "num_islands": 1,
            "elite_ratio": 0.1,
            "explore_ratio": 0.3,
            "exploit_ratio": 0.6,
        },
        "database": {
            "log_prompts": True,  # Save prompts used to generate each program
        },
        "evaluator": {
            "timeout": 3600,  # 1 hour timeout (effectively disabled, but prevents NoneType arithmetic errors)
            "cascade_evaluation": False,  # Disable cascade to prevent signal errors
            "parallel_evaluations": 1,  # Single worker to avoid multiprocessing complexity
            "distributed": False,  # No distributed processing
        }
    }

    config_path = os.path.join(work_dir, "config.yaml")
    with open(config_path, "w") as f:
        yaml.dump(config, f)

    return config_path


def optimize_prompt(initial_prompt: str, dataset_name: str, dataset_split: str,
                   model: str, input_field: str, target_field: str,
                   progress=gr.Progress()) -> Tuple[str, str, str, str, List[str], int, int]:
    """Run OpenEvolve to optimize the prompt."""

    progress(0, desc="Validating inputs...")

    # Validate all inputs
    is_valid, validation_message = validate_inputs(
        dataset_name, dataset_split, input_field, target_field, initial_prompt
    )

    if not is_valid:
        return f"## Validation Failed\n\n{validation_message}", "", "", "", [], 0, 0

    progress(0.05, desc=f"Validation passed: {validation_message}")

    # Create temporary working directory
    work_dir = tempfile.mkdtemp(prefix="openevolve_")

    try:
        # Save initial prompt
        initial_prompt_path = os.path.join(work_dir, "initial_prompt.txt")
        with open(initial_prompt_path, "w") as f:
            f.write(initial_prompt)

        # Create evaluator
        progress(0.1, desc="Creating evaluator...")
        evaluator_path = create_evaluator_file(dataset_name, dataset_split, model,
                                               input_field, target_field, work_dir)

        # Create config
        progress(0.15, desc="Creating configuration...")
        config_path = create_config_file(model, work_dir)

        # Run initial evaluation (using 20 samples to save API calls)
        progress(0.2, desc="Running initial evaluation on 20 samples...")
        initial_eval = evaluate_prompt(
            initial_prompt, dataset_name, dataset_split, 20,
            model, input_field, target_field
        )

        if "error" in initial_eval:
            return f"## Error\n\n❌ Initial evaluation failed: {initial_eval['error']}", "", "", "", [initial_prompt], 0, 1

        if initial_eval["total"] == 0:
            return f"## Error\n\n❌ Initial evaluation failed: No samples could be evaluated. This usually means:\n- API key is invalid or has no credits\n- Model is unavailable or rate-limited\n- Dataset fields are incorrect\n- Network connectivity issues\n\nPlease check your configuration and try again.", "", "", "", [initial_prompt], 0, 1

        initial_results = f"""
### Initial Prompt Evaluation

**Prompt:**
```
{initial_prompt}
```

**Results:**
- Accuracy: {initial_eval['accuracy']:.2f}%
- Correct: {initial_eval['correct']}/{initial_eval['total']}

**Sample Results:**
"""
        for i, result in enumerate(initial_eval['results'][:5], 1):
            initial_results += f"\n{i}. Input: {result['input']}\n"
            initial_results += f"   Target: {result['target']}\n"
            initial_results += f"   Prediction: {result['prediction']}\n"
            initial_results += f"   ✓ Correct\n" if result['correct'] else f"   ✗ Incorrect\n"

        # Run OpenEvolve
        progress(0.3, desc="Starting OpenEvolve optimization (10 iterations with staged evaluation)...")

        output_dir = os.path.join(work_dir, "output")
        os.makedirs(output_dir, exist_ok=True)

        try:
            # Comprehensive fix for "signal only works in main thread" in Gradio
            # We need to prevent OpenEvolve from using signal handlers entirely

            # Step 1: Set environment variable to disable process pool
            import os as os_env
            os_env.environ['OPENEVOLVE_NO_PARALLEL'] = '1'

            # Step 2: Monkey-patch signal module to ignore signal calls in threads
            import signal
            import threading

            original_signal = signal.signal

            def safe_signal(signum, handler):
                """Only set signal handlers in main thread"""
                if threading.current_thread() is threading.main_thread():
                    return original_signal(signum, handler)
                else:
                    # Return a dummy handler in non-main threads
                    return signal.SIG_DFL

            signal.signal = safe_signal

            # Run evolution with signal patch in place
            result = run_evolution(
                initial_program=initial_prompt_path,
                evaluator=evaluator_path,
                config=config_path,
                output_dir=output_dir
            )

            # Restore signal handler
            signal.signal = original_signal

            progress(0.80, desc="Parsing evolution history...")

            # Parse evolution history for visualization
            evolution_viz = parse_evolution_history(output_dir)

            progress(0.85, desc="Evaluating best evolved prompt on 20 samples...")

            # Get the best prompt (OpenEvolve saves to output_dir/best/best_program.txt)
            best_prompt_path = os.path.join(output_dir, "best", "best_program.txt")
            if os.path.exists(best_prompt_path):
                with open(best_prompt_path, "r") as f:
                    best_prompt = f.read()
            else:
                # Fallback: try without the "best" subdirectory
                best_prompt_path_alt = os.path.join(output_dir, "best_program.txt")
                if os.path.exists(best_prompt_path_alt):
                    with open(best_prompt_path_alt, "r") as f:
                        best_prompt = f.read()
                else:
                    best_prompt = initial_prompt

            # Evaluate best prompt (using 20 samples like initial eval for consistency)
            final_eval = evaluate_prompt(
                best_prompt, dataset_name, dataset_split, 20,
                model, input_field, target_field
            )

            final_results = f"""
### Evolved Prompt Evaluation

**Prompt:**
```
{best_prompt}
```

**Results:**
- Accuracy: {final_eval['accuracy']:.2f}%
- Correct: {final_eval['correct']}/{final_eval['total']}
- Improvement: {final_eval['accuracy'] - initial_eval['accuracy']:+.2f}%

**Sample Results:**
"""
            for i, result in enumerate(final_eval['results'][:5], 1):
                final_results += f"\n{i}. Input: {result['input']}\n"
                final_results += f"   Target: {result['target']}\n"
                final_results += f"   Prediction: {result['prediction']}\n"
                final_results += f"   ✓ Correct\n" if result['correct'] else f"   ✗ Incorrect\n"

            summary = f"""
## 🎉 Optimization Complete!

### Summary
- **Dataset**: {dataset_name} ({dataset_split} split)
- **Model**: {model}
- **Initial/Final Eval**: 20 samples each
- **Evolution Eval**: Staged (20 → 100 if score ≥ 0.5)
- **Iterations**: 10

### Results
- **Initial Accuracy**: {initial_eval['accuracy']:.2f}%
- **Final Accuracy**: {final_eval['accuracy']:.2f}%
- **Improvement**: {final_eval['accuracy'] - initial_eval['accuracy']:+.2f}%

{validation_message}
"""

            progress(1.0, desc="Complete!")

            # Collect only the "best" prompts - ones that improved the score during evolution
            all_prompts = []

            # Add initial prompt
            initial_score = initial_eval['accuracy'] / 100.0  # Convert to 0-1 scale
            all_prompts.append({
                "prompt": initial_prompt,
                "score": initial_score,
                "label": "Initial Prompt",
                "iteration": 0
            })

            # Add evolved prompts (only programs that were "best" at some point)
            # Pass initial_score so we only show programs that BEAT the initial prompt
            prompt_history = collect_prompt_history(output_dir, initial_score=initial_score)
            for i, p in enumerate(prompt_history):
                # Skip if it's the same as initial (shouldn't happen, but just in case)
                if i == 0 and p.get("iteration", -1) == 0:
                    continue

                all_prompts.append({
                    "prompt": p["prompt"],
                    "score": p.get("score", 0.0),
                    "label": f"Best at Iteration {p.get('iteration', i+1)}",
                    "iteration": p.get("iteration", i+1)
                })

            return summary, initial_results, evolution_viz, final_results, all_prompts, 0, len(all_prompts)

        except Exception as e:
            # Return error with initial prompt in dict format
            error_prompts = [{"prompt": initial_prompt, "score": 0.0, "label": "Initial Prompt"}]
            return f"## Error During Evolution\n\n❌ {str(e)}", initial_results, "", "", error_prompts, 0, 1

    finally:
        # Don't clean up - keep prompts for browsing
        # User can manually clean /tmp if needed
        pass


# Create Gradio interface
with gr.Blocks(title="OpenEvolve Prompt Optimizer", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🧬 OpenEvolve Prompt Optimizer

    Automatically evolve and optimize your prompts using evolutionary algorithms!

    This space uses [OpenEvolve](https://github.com/algorithmicsuperintelligence/openevolve) to iteratively improve prompts
    by testing them on real datasets and evolving better versions.

    ## How it works:
    1. Enter an initial prompt (use `{input}` as a placeholder for dataset inputs)
    2. Default dataset is **GSM8K** (grade school math problems) - challenging for showing improvement!
    3. Specify the dataset split and field names (or use other datasets like `stanfordnlp/imdb`)
    4. Choose a free model from OpenRouter
    5. Click "Optimize Prompt" - the system will validate everything first!
    6. Watch the evolution progress in real-time
    7. Compare initial vs. evolved performance - expect 20-40% improvement on GSM8K!

    **Note**: API key is read from `OPENAI_API_KEY` environment variable (set in Space secrets)
    """)

    with gr.Row():
        with gr.Column():
            gr.Markdown("### Configuration")

            model = gr.Dropdown(
                choices=FREE_MODELS,
                value=FREE_MODELS[0],
                label="Select Model",
                info="Choose from 5 curated free models on OpenRouter (24B to 671B parameters)"
            )

            dataset_name = gr.Textbox(
                label="HuggingFace Dataset (Full Name)",
                value="gsm8k",
                placeholder="e.g., gsm8k, stanfordnlp/imdb, openai/gsm8k",
                info="Dataset name from HuggingFace Hub. Configs auto-detected (e.g., 'gsm8k' → 'gsm8k:main')"
            )

            dataset_split = gr.Textbox(
                label="Dataset Split",
                value="train",
                placeholder="e.g., train, test, validation"
            )

            input_field = gr.Textbox(
                label="Input Field Name",
                value="question",
                placeholder="e.g., question, text, context",
                info="The field containing inputs to process"
            )

            target_field = gr.Textbox(
                label="Target Field Name",
                value="answer",
                placeholder="e.g., answer, label, target",
                info="The field containing expected outputs"
            )

            initial_prompt = gr.TextArea(
                label="Initial Prompt",
                value="{input}\n\nAnswer:",
                lines=6,
                info="Use {input} as placeholder for dataset inputs. Start simple - evolution will improve it!"
            )

    # Button outside the column for better visibility
    with gr.Row():
        with gr.Column():
            optimize_btn = gr.Button("🚀 Validate & Optimize Prompt", variant="primary", size="lg")

    # Results section - clearly separated
    gr.Markdown("---")
    gr.Markdown("## 📊 Results")

    with gr.Row():
        with gr.Column():
            summary = gr.Markdown("Click 'Validate & Optimize Prompt' to start optimization...", visible=True)

    with gr.Row():
        with gr.Column():
            initial_results = gr.Markdown("### Initial Results\nWill appear here after validation...", visible=True)
        with gr.Column():
            final_results = gr.Markdown("### Final Results\nWill appear here after optimization...", visible=True)

    with gr.Row():
        with gr.Column():
            evolution_progress = gr.Markdown("### Evolution Progress\nEvolution progress will appear here during optimization...", visible=True)

    # Prompt History Browser
    gr.Markdown("---")
    gr.Markdown("## 📜 Prompt History Browser")
    gr.Markdown("Browse through the progression of **best** prompts found during evolution. Only shows prompts that improved the score (no duplicates or intermediate programs).")

    with gr.Row():
        with gr.Column(scale=8):
            prompt_display = gr.TextArea(
                label="",
                lines=10,
                interactive=False,
                placeholder="Prompts will appear here after optimization completes...",
                show_label=False
            )
        with gr.Column(scale=2):
            prompt_counter = gr.Markdown("**Prompt**: -/-")
            prev_btn = gr.Button("⬅️ Previous", size="sm")
            next_btn = gr.Button("Next ➡️", size="sm")
            gr.Markdown("**Prompt Types:**\n- First = Initial\n- Middle = Intermediate\n- Last = Final Best")

    # Hidden state to store prompt history and current index
    prompt_history_state = gr.State([])
    current_prompt_index = gr.State(0)

    # Documentation section - in collapsible accordion
    gr.Markdown("---")
    with gr.Accordion("📚 Documentation & Examples", open=False):
        gr.Markdown("""
        ### Example Datasets & Fields:

        | Dataset | Split | Input Field | Target Field | Task |
        |---------|-------|-------------|--------------|------|
        | stanfordnlp/imdb | test | text | label | Sentiment Analysis |
        | rajpurkar/squad | validation | question | answers | Question Answering |
        | dair-ai/emotion | test | text | label | Emotion Classification |
        | openai/gsm8k | test | question | answer | Math Reasoning |
        | fancyzhx/ag_news | test | text | label | News Classification |

        ### About This Demo Space:

        **This is a demonstration space** showcasing OpenEvolve's prompt optimization capabilities.
        The interface shows you how the system works, but **you'll need to set up your own instance to run optimizations**.

        ### How to Run This Yourself:

        1. **Clone this Space**: Click "⋮" (three dots) at top-right → "Duplicate this Space"
        2. **Set Environment Variables** in your cloned Space's settings:
           - `OPENAI_API_KEY`: Your OpenRouter API key (get free key at [openrouter.ai/keys](https://openrouter.ai/keys))
           - `HF_TOKEN`: (Optional) HuggingFace token for private datasets
        3. **Configure Your Optimization**:
           - Dataset: Use full name format (e.g., `stanfordnlp/imdb` or `openai/gsm8k`)
           - Fields: Specify exact field names from the dataset schema
           - Model: Choose from 5 curated free models (larger models = better results but slower/rate-limited)
        4. **Run & Monitor**:
           - All inputs are validated before starting
           - Evolution uses staged evaluation (20 samples first, then 80 more if promising)
           - Saves API calls by early-stopping poor prompts (< 50% accuracy)
           - Watch evolution progress visualization in real-time

        ### About OpenEvolve:
        OpenEvolve is an open-source evolutionary optimization framework. Learn more at:
        - [GitHub Repository](https://github.com/algorithmicsuperintelligence/openevolve)
        - [Documentation](https://github.com/algorithmicsuperintelligence/openevolve#readme)
        """)

    # Navigation functions for prompt browser
    def show_previous_prompt(prompts, current_idx):
        if not prompts or len(prompts) == 0:
            return "", "**Prompt**: -/-", 0
        new_idx = max(0, current_idx - 1)
        prompt_obj = prompts[new_idx]
        # Handle both old string format and new dict format
        if isinstance(prompt_obj, dict):
            prompt_text = prompt_obj["prompt"]
            score = prompt_obj.get("score", 0.0)
            label = prompt_obj.get("label", "")
            counter_text = f"**{label}** ({new_idx + 1}/{len(prompts)}) | Score: {score:.2%}"
        else:
            prompt_text = prompt_obj
            counter_text = f"**Prompt**: {new_idx + 1}/{len(prompts)}"
        return prompt_text, counter_text, new_idx

    def show_next_prompt(prompts, current_idx):
        if not prompts or len(prompts) == 0:
            return "", "**Prompt**: -/-", 0
        new_idx = min(len(prompts) - 1, current_idx + 1)
        prompt_obj = prompts[new_idx]
        # Handle both old string format and new dict format
        if isinstance(prompt_obj, dict):
            prompt_text = prompt_obj["prompt"]
            score = prompt_obj.get("score", 0.0)
            label = prompt_obj.get("label", "")
            counter_text = f"**{label}** ({new_idx + 1}/{len(prompts)}) | Score: {score:.2%}"
        else:
            prompt_text = prompt_obj
            counter_text = f"**Prompt**: {new_idx + 1}/{len(prompts)}"
        return prompt_text, counter_text, new_idx

    def update_prompt_display(prompts, idx, total):
        if not prompts or len(prompts) == 0:
            return "", "**Prompt**: -/-"
        idx = min(idx, len(prompts) - 1)
        prompt_obj = prompts[idx]
        # Handle both old string format and new dict format
        if isinstance(prompt_obj, dict):
            prompt_text = prompt_obj["prompt"]
            score = prompt_obj.get("score", 0.0)
            label = prompt_obj.get("label", "")
            counter_text = f"**{label}** ({idx + 1}/{len(prompts)}) | Score: {score:.2%}"
        else:
            prompt_text = prompt_obj
            counter_text = f"**Prompt**: {idx + 1}/{len(prompts)}"
        return prompt_text, counter_text

    # Wire up the optimize button
    optimize_result = optimize_btn.click(
        fn=optimize_prompt,
        inputs=[initial_prompt, dataset_name, dataset_split, model,
                input_field, target_field],
        outputs=[summary, initial_results, evolution_progress, final_results,
                 prompt_history_state, current_prompt_index, gr.State()]  # dummy for total
    )

    # Update prompt display when optimization completes
    optimize_result.then(
        fn=update_prompt_display,
        inputs=[prompt_history_state, current_prompt_index, gr.State()],
        outputs=[prompt_display, prompt_counter]
    )

    # Wire up navigation buttons
    prev_btn.click(
        fn=show_previous_prompt,
        inputs=[prompt_history_state, current_prompt_index],
        outputs=[prompt_display, prompt_counter, current_prompt_index]
    )

    next_btn.click(
        fn=show_next_prompt,
        inputs=[prompt_history_state, current_prompt_index],
        outputs=[prompt_display, prompt_counter, current_prompt_index]
    )

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