Scholar Evaluation

#!/usr/bin/env python3
"""
ScholarEval Score Calculator

Calculate aggregate evaluation scores from dimension-level ratings.
Supports weighted averaging, threshold analysis, and score visualization.

Usage:
    python calculate_scores.py --scores <dimension_scores.json> --output <report.txt>
    python calculate_scores.py --scores <dimension_scores.json> --weights <weights.json>
    python calculate_scores.py --interactive

Author: ScholarEval Framework
License: MIT
"""

import json
import argparse
import sys
from typing import Dict, List, Optional
from pathlib import Path


# Default dimension weights (total = 100%)
DEFAULT_WEIGHTS = {
    "problem_formulation": 0.15,
    "literature_review": 0.15,
    "methodology": 0.20,
    "data_collection": 0.10,
    "analysis": 0.15,
    "results": 0.10,
    "writing": 0.10,
    "citations": 0.05
}

# Quality level definitions
QUALITY_LEVELS = {
    (4.5, 5.0): ("Exceptional", "Ready for top-tier publication"),
    (4.0, 4.4): ("Strong", "Publication-ready with minor revisions"),
    (3.5, 3.9): ("Good", "Major revisions required, promising work"),
    (3.0, 3.4): ("Acceptable", "Significant revisions needed"),
    (2.0, 2.9): ("Weak", "Fundamental issues, major rework required"),
    (0.0, 1.9): ("Poor", "Not suitable without complete revision")
}


def load_scores(filepath: Path) -> Dict[str, float]:
    """Load dimension scores from JSON file."""
    try:
        with open(filepath, 'r') as f:
            scores = json.load(f)

        # Validate scores
        for dim, score in scores.items():
            if not 1 <= score <= 5:
                raise ValueError(f"Score for {dim} must be between 1 and 5, got {score}")

        return scores
    except FileNotFoundError:
        print(f"Error: File not found: {filepath}")
        sys.exit(1)
    except json.JSONDecodeError:
        print(f"Error: Invalid JSON in {filepath}")
        sys.exit(1)
    except ValueError as e:
        print(f"Error: {e}")
        sys.exit(1)


def load_weights(filepath: Optional[Path] = None) -> Dict[str, float]:
    """Load dimension weights from JSON file or return defaults."""
    if filepath is None:
        return DEFAULT_WEIGHTS

    try:
        with open(filepath, 'r') as f:
            weights = json.load(f)

        # Validate weights sum to 1.0
        total = sum(weights.values())
        if not 0.99 <= total <= 1.01:  # Allow small floating point errors
            raise ValueError(f"Weights must sum to 1.0, got {total}")

        return weights
    except FileNotFoundError:
        print(f"Error: File not found: {filepath}")
        sys.exit(1)
    except json.JSONDecodeError:
        print(f"Error: Invalid JSON in {filepath}")
        sys.exit(1)
    except ValueError as e:
        print(f"Error: {e}")
        sys.exit(1)


def calculate_weighted_average(scores: Dict[str, float], weights: Dict[str, float]) -> float:
    """Calculate weighted average score."""
    total_score = 0.0
    total_weight = 0.0

    for dimension, score in scores.items():
        # Handle dimension name variations (e.g., "problem_formulation" vs "problem-formulation")
        dim_key = dimension.replace('-', '_').lower()
        weight = weights.get(dim_key, 0.0)

        total_score += score * weight
        total_weight += weight

    # Normalize if not all dimensions were scored
    if total_weight > 0:
        return total_score / total_weight * (sum(weights.values()) / total_weight)
    return 0.0


def get_quality_level(score: float) -> tuple:
    """Get quality level description for a given score."""
    for (low, high), (level, description) in QUALITY_LEVELS.items():
        if low <= score <= high:
            return level, description
    return "Unknown", "Score out of expected range"


def generate_bar_chart(scores: Dict[str, float], max_width: int = 50) -> str:
    """Generate ASCII bar chart of dimension scores."""
    lines = []
    max_name_len = max(len(name) for name in scores.keys())

    for dimension, score in sorted(scores.items(), key=lambda x: x[1], reverse=True):
        bar_length = int((score / 5.0) * max_width)
        bar = '█' * bar_length
        padding = ' ' * (max_name_len - len(dimension))
        lines.append(f"  {dimension}{padding} │ {bar} {score:.2f}")

    return '\n'.join(lines)


def identify_strengths_weaknesses(scores: Dict[str, float]) -> tuple:
    """Identify top strengths and areas for improvement."""
    sorted_scores = sorted(scores.items(), key=lambda x: x[1], reverse=True)

    strengths = [dim for dim, score in sorted_scores[:3] if score >= 4.0]
    weaknesses = [dim for dim, score in sorted_scores[-3:] if score < 3.5]

    return strengths, weaknesses


def generate_report(scores: Dict[str, float], weights: Dict[str, float],
                   output_file: Optional[Path] = None) -> str:
    """Generate comprehensive evaluation report."""
    overall_score = calculate_weighted_average(scores, weights)
    quality_level, quality_desc = get_quality_level(overall_score)
    strengths, weaknesses = identify_strengths_weaknesses(scores)

    report_lines = [
        "="*70,
        "SCHOLAREVAL SCORE REPORT",
        "="*70,
        "",
        f"Overall Score: {overall_score:.2f} / 5.00",
        f"Quality Level: {quality_level}",
        f"Assessment: {quality_desc}",
        "",
        "="*70,
        "DIMENSION SCORES",
        "="*70,
        "",
        generate_bar_chart(scores),
        "",
        "="*70,
        "DETAILED BREAKDOWN",
        "="*70,
        ""
    ]

    # Add detailed scores with weights
    for dimension, score in sorted(scores.items()):
        dim_key = dimension.replace('-', '_').lower()
        weight = weights.get(dim_key, 0.0)
        weighted_contribution = score * weight
        percentage = weight * 100

        report_lines.append(
            f"  {dimension:25s} {score:.2f}/5.00  "
            f"(weight: {percentage:4.1f}%, contribution: {weighted_contribution:.3f})"
        )

    report_lines.extend([
        "",
        "="*70,
        "ASSESSMENT SUMMARY",
        "="*70,
        ""
    ])

    if strengths:
        report_lines.append("Top Strengths:")
        for dim in strengths:
            report_lines.append(f"  • {dim}: {scores[dim]:.2f}/5.00")
        report_lines.append("")

    if weaknesses:
        report_lines.append("Areas for Improvement:")
        for dim in weaknesses:
            report_lines.append(f"  • {dim}: {scores[dim]:.2f}/5.00")
        report_lines.append("")

    # Add recommendations based on score
    report_lines.extend([
        "="*70,
        "RECOMMENDATIONS",
        "="*70,
        ""
    ])

    if overall_score >= 4.5:
        report_lines.append("  Excellent work! Ready for submission to top-tier venues.")
    elif overall_score >= 4.0:
        report_lines.append("  Strong work. Address minor issues identified in weaknesses.")
    elif overall_score >= 3.5:
        report_lines.append("  Good foundation. Focus on major revisions in weak dimensions.")
    elif overall_score >= 3.0:
        report_lines.append("  Significant revisions needed. Prioritize weakest dimensions.")
    elif overall_score >= 2.0:
        report_lines.append("  Major rework required. Consider restructuring approach.")
    else:
        report_lines.append("  Fundamental revision needed across multiple dimensions.")

    report_lines.append("")
    report_lines.append("="*70)

    report = '\n'.join(report_lines)

    # Write to file if specified
    if output_file:
        try:
            with open(output_file, 'w') as f:
                f.write(report)
            print(f"\nReport saved to: {output_file}")
        except IOError as e:
            print(f"Error writing to {output_file}: {e}")

    return report


def interactive_mode():
    """Run interactive score entry mode."""
    print("ScholarEval Interactive Score Calculator")
    print("="*50)
    print("\nEnter scores for each dimension (1-5):")
    print("(Press Enter to skip a dimension)\n")

    scores = {}
    dimensions = [
        "problem_formulation",
        "literature_review",
        "methodology",
        "data_collection",
        "analysis",
        "results",
        "writing",
        "citations"
    ]

    for dim in dimensions:
        while True:
            dim_display = dim.replace('_', ' ').title()
            user_input = input(f"{dim_display}: ").strip()

            if not user_input:
                break

            try:
                score = float(user_input)
                if 1 <= score <= 5:
                    scores[dim] = score
                    break
                else:
                    print("  Score must be between 1 and 5")
            except ValueError:
                print("  Invalid input. Please enter a number between 1 and 5")

    if not scores:
        print("\nNo scores entered. Exiting.")
        return

    print("\n" + "="*50)
    print("SCORES ENTERED:")
    for dim, score in scores.items():
        print(f"  {dim.replace('_', ' ').title()}: {score}")

    print("\nCalculating overall assessment...\n")

    report = generate_report(scores, DEFAULT_WEIGHTS)
    print(report)

    # Ask if user wants to save
    save = input("\nSave report to file? (y/n): ").strip().lower()
    if save == 'y':
        filename = input("Enter filename [scholareval_report.txt]: ").strip()
        if not filename:
            filename = "scholareval_report.txt"
        generate_report(scores, DEFAULT_WEIGHTS, Path(filename))


def main():
    parser = argparse.ArgumentParser(
        description="Calculate aggregate ScholarEval scores from dimension ratings",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  # Calculate from JSON file
  python calculate_scores.py --scores my_scores.json

  # Calculate with custom weights
  python calculate_scores.py --scores my_scores.json --weights custom_weights.json

  # Save report to file
  python calculate_scores.py --scores my_scores.json --output report.txt

  # Interactive mode
  python calculate_scores.py --interactive

Score JSON Format:
  {
    "problem_formulation": 4.5,
    "literature_review": 4.0,
    "methodology": 3.5,
    "data_collection": 4.0,
    "analysis": 3.5,
    "results": 4.0,
    "writing": 4.5,
    "citations": 4.0
  }

Weights JSON Format:
  {
    "problem_formulation": 0.15,
    "literature_review": 0.15,
    "methodology": 0.20,
    "data_collection": 0.10,
    "analysis": 0.15,
    "results": 0.10,
    "writing": 0.10,
    "citations": 0.05
  }
        """
    )

    parser.add_argument('--scores', type=Path, help='Path to JSON file with dimension scores')
    parser.add_argument('--weights', type=Path, help='Path to JSON file with dimension weights (optional)')
    parser.add_argument('--output', type=Path, help='Path to output report file (optional)')
    parser.add_argument('--interactive', '-i', action='store_true', help='Run in interactive mode')

    args = parser.parse_args()

    # Interactive mode
    if args.interactive:
        interactive_mode()
        return

    # File mode
    if not args.scores:
        parser.print_help()
        print("\nError: --scores is required (or use --interactive)")
        sys.exit(1)

    scores = load_scores(args.scores)
    weights = load_weights(args.weights)

    report = generate_report(scores, weights, args.output)

    # Print to stdout if no output file specified
    if not args.output:
        print(report)


if __name__ == '__main__':
    main()

#!/usr/bin/env python3
"""
AI-powered scientific schematic generation using Nano Banana 2.

This script uses a smart iterative refinement approach:
1. Generate initial image with Nano Banana 2
2. AI quality review using Gemini 3.1 Pro Preview for scientific critique
3. Only regenerate if quality is below threshold for document type
4. Repeat until quality meets standards (max iterations)

Requirements:
    - OPENROUTER_API_KEY environment variable
    - requests library

Usage:
    python generate_schematic_ai.py "Create a flowchart showing CONSORT participant flow" -o flowchart.png
    python generate_schematic_ai.py "Neural network architecture diagram" -o architecture.png --iterations 2
    python generate_schematic_ai.py "Simple block diagram" -o diagram.png --doc-type poster
"""

import argparse
import base64
import json
import os
import sys
import time
from pathlib import Path
from typing import Optional, Dict, Any, List, Tuple

try:
    import requests
except ImportError:
    print("Error: requests library not found. Install with: pip install requests")
    sys.exit(1)

# Try to load .env file from multiple potential locations
def _load_env_file():
    """Load .env file from current directory or script directory only."""
    try:
        from dotenv import load_dotenv
    except ImportError:
        return False

    for candidate in [Path.cwd() / ".env", Path(__file__).resolve().parent / ".env"]:
        if candidate.exists():
            load_dotenv(dotenv_path=candidate, override=False)
            return True

    return False


class ScientificSchematicGenerator:
    """Generate scientific schematics using AI with smart iterative refinement.
    
    Uses Gemini 3.1 Pro Preview for quality review to determine if regeneration is needed.
    Multiple passes only occur if the generated schematic doesn't meet the
    quality threshold for the target document type.
    """
    
    # Quality thresholds by document type (score out of 10)
    # Higher thresholds for more formal publications
    QUALITY_THRESHOLDS = {
        "journal": 8.5,      # Nature, Science, etc. - highest standards
        "conference": 8.0,   # Conference papers - high standards
        "poster": 7.0,       # Academic posters - good quality
        "presentation": 6.5, # Slides/talks - clear but less formal
        "report": 7.5,       # Technical reports - professional
        "grant": 8.0,        # Grant proposals - must be compelling
        "thesis": 8.0,       # Dissertations - formal academic
        "preprint": 7.5,     # arXiv, etc. - good quality
        "default": 7.5,      # Default threshold
    }
    
    # Scientific diagram best practices prompt template
    SCIENTIFIC_DIAGRAM_GUIDELINES = """
Create a high-quality scientific diagram with these requirements:

VISUAL QUALITY:
- Clean white or light background (no textures or gradients)
- High contrast for readability and printing
- Professional, publication-ready appearance
- Sharp, clear lines and text
- Adequate spacing between elements to prevent crowding

TYPOGRAPHY:
- Clear, readable sans-serif fonts (Arial, Helvetica style)
- Minimum 10pt font size for all labels
- Consistent font sizes throughout
- All text horizontal or clearly readable
- No overlapping text

SCIENTIFIC STANDARDS:
- Accurate representation of concepts
- Clear labels for all components
- Include scale bars, legends, or axes where appropriate
- Use standard scientific notation and symbols
- Include units where applicable

ACCESSIBILITY:
- Colorblind-friendly color palette (use Okabe-Ito colors if using color)
- High contrast between elements
- Redundant encoding (shapes + colors, not just colors)
- Works well in grayscale

LAYOUT:
- Logical flow (left-to-right or top-to-bottom)
- Clear visual hierarchy
- Balanced composition
- Appropriate use of whitespace
- No clutter or unnecessary decorative elements

IMPORTANT - NO FIGURE NUMBERS:
- Do NOT include "Figure 1:", "Fig. 1", or any figure numbering in the image
- Do NOT add captions or titles like "Figure: ..." at the top or bottom
- Figure numbers and captions are added separately in the document/LaTeX
- The diagram should contain only the visual content itself
"""
    
    def __init__(self, api_key: Optional[str] = None, verbose: bool = False):
        """
        Initialize the generator.
        
        Args:
            api_key: OpenRouter API key (or use OPENROUTER_API_KEY env var)
            verbose: Print detailed progress information
        """
        # Priority: 1) explicit api_key param, 2) environment variable, 3) .env file
        self.api_key = api_key or os.getenv("OPENROUTER_API_KEY")
        
        # If not found in environment, try loading from .env file
        if not self.api_key:
            _load_env_file()
            self.api_key = os.getenv("OPENROUTER_API_KEY")
        
        if not self.api_key:
            raise ValueError(
                "OPENROUTER_API_KEY not found. Please either:\n"
                "  1. Set the OPENROUTER_API_KEY environment variable\n"
                "  2. Add OPENROUTER_API_KEY to your .env file\n"
                "  3. Pass api_key parameter to the constructor\n"
                "Get your API key from: https://openrouter.ai/keys"
            )
        
        self.verbose = verbose
        self._last_error = None  # Track last error for better reporting
        self.base_url = "https://openrouter.ai/api/v1"
        # Nano Banana 2 - Google's advanced image generation model
        # https://openrouter.ai/google/gemini-3-pro-image-preview
        self.image_model = "google/gemini-3.1-flash-image-preview"
        # Gemini 3.1 Pro Preview for quality review - excellent vision and reasoning
        self.review_model = "google/gemini-3.1-pro-preview"
        
    def _log(self, message: str):
        """Log message if verbose mode is enabled."""
        if self.verbose:
            print(f"[{time.strftime('%H:%M:%S')}] {message}")
    
    def _make_request(self, model: str, messages: List[Dict[str, Any]], 
                     modalities: Optional[List[str]] = None) -> Dict[str, Any]:
        """
        Make a request to OpenRouter API.
        
        Args:
            model: Model identifier
            messages: List of message dictionaries
            modalities: Optional list of modalities (e.g., ["image", "text"])
            
        Returns:
            API response as dictionary
        """
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json",
            "HTTP-Referer": "https://github.com/scientific-writer",
            "X-Title": "Scientific Schematic Generator"
        }
        
        payload = {
            "model": model,
            "messages": messages
        }
        
        if modalities:
            payload["modalities"] = modalities
        
        self._log(f"Making request to {model}...")
        
        try:
            response = requests.post(
                f"{self.base_url}/chat/completions",
                headers=headers,
                json=payload,
                timeout=120
            )
            
            # Try to get response body even on error
            try:
                response_json = response.json()
            except json.JSONDecodeError:
                response_json = {"raw_text": response.text[:500]}
            
            # Check for HTTP errors but include response body in error message
            if response.status_code != 200:
                error_detail = response_json.get("error", response_json)
                self._log(f"HTTP {response.status_code}: {error_detail}")
                raise RuntimeError(f"API request failed (HTTP {response.status_code}): {error_detail}")
            
            return response_json
        except requests.exceptions.Timeout:
            raise RuntimeError("API request timed out after 120 seconds")
        except requests.exceptions.RequestException as e:
            raise RuntimeError(f"API request failed: {str(e)}")
    
    def _extract_image_from_response(self, response: Dict[str, Any]) -> Optional[bytes]:
        """
        Extract base64-encoded image from API response.
        
        For Nano Banana 2, images are returned in the 'images' field of the message,
        not in the 'content' field.
        
        Args:
            response: API response dictionary
            
        Returns:
            Image bytes or None if not found
        """
        try:
            choices = response.get("choices", [])
            if not choices:
                self._log("No choices in response")
                return None
            
            message = choices[0].get("message", {})
            
            # IMPORTANT: Nano Banana 2 returns images in the 'images' field
            images = message.get("images", [])
            if images and len(images) > 0:
                self._log(f"Found {len(images)} image(s) in 'images' field")
                
                # Get first image
                first_image = images[0]
                if isinstance(first_image, dict):
                    # Extract image_url
                    if first_image.get("type") == "image_url":
                        url = first_image.get("image_url", {})
                        if isinstance(url, dict):
                            url = url.get("url", "")
                        
                        if url and url.startswith("data:image"):
                            # Extract base64 data after comma
                            if "," in url:
                                base64_str = url.split(",", 1)[1]
                                # Clean whitespace
                                base64_str = base64_str.replace('\n', '').replace('\r', '').replace(' ', '')
                                self._log(f"Extracted base64 data (length: {len(base64_str)})")
                                return base64.b64decode(base64_str)
            
            # Fallback: check content field (for other models or future changes)
            content = message.get("content", "")
            
            if self.verbose:
                self._log(f"Content type: {type(content)}, length: {len(str(content))}")
            
            # Handle string content
            if isinstance(content, str) and "data:image" in content:
                import re
                match = re.search(r'data:image/[^;]+;base64,([A-Za-z0-9+/=\n\r]+)', content, re.DOTALL)
                if match:
                    base64_str = match.group(1).replace('\n', '').replace('\r', '').replace(' ', '')
                    self._log(f"Found image in content field (length: {len(base64_str)})")
                    return base64.b64decode(base64_str)
            
            # Handle list content
            if isinstance(content, list):
                for i, block in enumerate(content):
                    if isinstance(block, dict) and block.get("type") == "image_url":
                        url = block.get("image_url", {})
                        if isinstance(url, dict):
                            url = url.get("url", "")
                        if url and url.startswith("data:image") and "," in url:
                            base64_str = url.split(",", 1)[1].replace('\n', '').replace('\r', '').replace(' ', '')
                            self._log(f"Found image in content block {i}")
                            return base64.b64decode(base64_str)
            
            self._log("No image data found in response")
            return None
            
        except Exception as e:
            self._log(f"Error extracting image: {str(e)}")
            import traceback
            if self.verbose:
                traceback.print_exc()
            return None
    
    def _image_to_base64(self, image_path: str) -> str:
        """
        Convert image file to base64 data URL.
        
        Args:
            image_path: Path to image file
            
        Returns:
            Base64 data URL string
        """
        with open(image_path, "rb") as f:
            image_data = f.read()
        
        # Determine image type from extension
        ext = Path(image_path).suffix.lower()
        mime_type = {
            ".png": "image/png",
            ".jpg": "image/jpeg",
            ".jpeg": "image/jpeg",
            ".gif": "image/gif",
            ".webp": "image/webp"
        }.get(ext, "image/png")
        
        base64_data = base64.b64encode(image_data).decode("utf-8")
        return f"data:{mime_type};base64,{base64_data}"
    
    def generate_image(self, prompt: str) -> Optional[bytes]:
        """
        Generate an image using Nano Banana 2.
        
        Args:
            prompt: Description of the diagram to generate
            
        Returns:
            Image bytes or None if generation failed
        """
        self._last_error = None  # Reset error
        
        messages = [
            {
                "role": "user",
                "content": prompt
            }
        ]
        
        try:
            response = self._make_request(
                model=self.image_model,
                messages=messages,
                modalities=["image", "text"]
            )
            
            # Debug: print response structure if verbose
            if self.verbose:
                self._log(f"Response keys: {response.keys()}")
                if "error" in response:
                    self._log(f"API Error: {response['error']}")
                if "choices" in response and response["choices"]:
                    msg = response["choices"][0].get("message", {})
                    self._log(f"Message keys: {msg.keys()}")
                    # Show content preview without printing huge base64 data
                    content = msg.get("content", "")
                    if isinstance(content, str):
                        preview = content[:200] + "..." if len(content) > 200 else content
                        self._log(f"Content preview: {preview}")
                    elif isinstance(content, list):
                        self._log(f"Content is list with {len(content)} items")
                        for i, item in enumerate(content[:3]):
                            if isinstance(item, dict):
                                self._log(f"  Item {i}: type={item.get('type')}")
            
            # Check for API errors in response
            if "error" in response:
                error_msg = response["error"]
                if isinstance(error_msg, dict):
                    error_msg = error_msg.get("message", str(error_msg))
                self._last_error = f"API Error: {error_msg}"
                print(f"✗ {self._last_error}")
                return None
            
            image_data = self._extract_image_from_response(response)
            if image_data:
                self._log(f"✓ Generated image ({len(image_data)} bytes)")
            else:
                self._last_error = "No image data in API response - model may not support image generation"
                self._log(f"✗ {self._last_error}")
                # Additional debug info when image extraction fails
                if self.verbose and "choices" in response:
                    msg = response["choices"][0].get("message", {})
                    self._log(f"Full message structure: {json.dumps({k: type(v).__name__ for k, v in msg.items()})}")
            
            return image_data
        except RuntimeError as e:
            self._last_error = str(e)
            self._log(f"✗ Generation failed: {self._last_error}")
            return None
        except Exception as e:
            self._last_error = f"Unexpected error: {str(e)}"
            self._log(f"✗ Generation failed: {self._last_error}")
            import traceback
            if self.verbose:
                traceback.print_exc()
            return None
    
    def review_image(self, image_path: str, original_prompt: str, 
                    iteration: int, doc_type: str = "default",
                    max_iterations: int = 2) -> Tuple[str, float, bool]:
        """
        Review generated image using Gemini 3.1 Pro Preview for quality analysis.
        
        Uses Gemini 3.1 Pro Preview's superior vision and reasoning capabilities to
        evaluate the schematic quality and determine if regeneration is needed.
        
        Args:
            image_path: Path to the generated image
            original_prompt: Original user prompt
            iteration: Current iteration number
            doc_type: Document type (journal, poster, presentation, etc.)
            max_iterations: Maximum iterations allowed
            
        Returns:
            Tuple of (critique text, quality score 0-10, needs_improvement bool)
        """
        # Use Gemini 3.1 Pro Preview for review - excellent vision and analysis
        image_data_url = self._image_to_base64(image_path)
        
        # Get quality threshold for this document type
        threshold = self.QUALITY_THRESHOLDS.get(doc_type.lower(), 
                                                 self.QUALITY_THRESHOLDS["default"])
        
        review_prompt = f"""You are an expert reviewer evaluating a scientific diagram for publication quality.

ORIGINAL REQUEST: {original_prompt}

DOCUMENT TYPE: {doc_type} (quality threshold: {threshold}/10)
ITERATION: {iteration}/{max_iterations}

Carefully evaluate this diagram on these criteria:

1. **Scientific Accuracy** (0-2 points)
   - Correct representation of concepts
   - Proper notation and symbols
   - Accurate relationships shown

2. **Clarity and Readability** (0-2 points)
   - Easy to understand at a glance
   - Clear visual hierarchy
   - No ambiguous elements

3. **Label Quality** (0-2 points)
   - All important elements labeled
   - Labels are readable (appropriate font size)
   - Consistent labeling style

4. **Layout and Composition** (0-2 points)
   - Logical flow (top-to-bottom or left-to-right)
   - Balanced use of space
   - No overlapping elements

5. **Professional Appearance** (0-2 points)
   - Publication-ready quality
   - Clean, crisp lines and shapes
   - Appropriate colors/contrast

RESPOND IN THIS EXACT FORMAT:
SCORE: [total score 0-10]

STRENGTHS:
- [strength 1]
- [strength 2]

ISSUES:
- [issue 1 if any]
- [issue 2 if any]

VERDICT: [ACCEPTABLE or NEEDS_IMPROVEMENT]

If score >= {threshold}, the diagram is ACCEPTABLE for {doc_type} publication.
If score < {threshold}, mark as NEEDS_IMPROVEMENT with specific suggestions."""

        messages = [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": review_prompt
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": image_data_url
                        }
                    }
                ]
            }
        ]
        
        try:
            # Use Gemini 3.1 Pro Preview for high-quality review
            response = self._make_request(
                model=self.review_model,
                messages=messages
            )
            
            # Extract text response
            choices = response.get("choices", [])
            if not choices:
                return "Image generated successfully", 8.0
            
            message = choices[0].get("message", {})
            content = message.get("content", "")
            
            # Check reasoning field (Nano Banana 2 puts analysis here)
            reasoning = message.get("reasoning", "")
            if reasoning and not content:
                content = reasoning
            
            if isinstance(content, list):
                # Extract text from content blocks
                text_parts = []
                for block in content:
                    if isinstance(block, dict) and block.get("type") == "text":
                        text_parts.append(block.get("text", ""))
                content = "\n".join(text_parts)
            
            # Try to extract score
            score = 7.5  # Default score if extraction fails
            import re
            
            # Look for SCORE: X or SCORE: X/10 format
            score_match = re.search(r'SCORE:\s*(\d+(?:\.\d+)?)', content, re.IGNORECASE)
            if score_match:
                score = float(score_match.group(1))
            else:
                # Fallback: look for any score pattern
                score_match = re.search(r'(?:score|rating|quality)[:\s]+(\d+(?:\.\d+)?)\s*(?:/\s*10)?', content, re.IGNORECASE)
                if score_match:
                    score = float(score_match.group(1))
            
            # Determine if improvement is needed based on verdict or score
            needs_improvement = False
            if "NEEDS_IMPROVEMENT" in content.upper():
                needs_improvement = True
            elif score < threshold:
                needs_improvement = True
            
            self._log(f"✓ Review complete (Score: {score}/10, Threshold: {threshold}/10)")
            self._log(f"  Verdict: {'Needs improvement' if needs_improvement else 'Acceptable'}")
            
            return (content if content else "Image generated successfully", 
                    score, 
                    needs_improvement)
        except Exception as e:
            self._log(f"Review skipped: {str(e)}")
            # Don't fail the whole process if review fails - assume acceptable
            return "Image generated successfully (review skipped)", 7.5, False
    
    def improve_prompt(self, original_prompt: str, critique: str, 
                      iteration: int) -> str:
        """
        Improve the generation prompt based on critique.
        
        Args:
            original_prompt: Original user prompt
            critique: Review critique from previous iteration
            iteration: Current iteration number
            
        Returns:
            Improved prompt for next generation
        """
        improved_prompt = f"""{self.SCIENTIFIC_DIAGRAM_GUIDELINES}

USER REQUEST: {original_prompt}

ITERATION {iteration}: Based on previous feedback, address these specific improvements:
{critique}

Generate an improved version that addresses all the critique points while maintaining scientific accuracy and professional quality."""
        
        return improved_prompt
    
    def generate_iterative(self, user_prompt: str, output_path: str,
                          iterations: int = 2, 
                          doc_type: str = "default") -> Dict[str, Any]:
        """
        Generate scientific schematic with smart iterative refinement.
        
        Only regenerates if the quality score is below the threshold for the
        specified document type. This saves API calls and time when the first
        generation is already good enough.
        
        Args:
            user_prompt: User's description of desired diagram
            output_path: Path to save final image
            iterations: Maximum refinement iterations (default: 2, max: 2)
            doc_type: Document type for quality threshold (journal, poster, etc.)
            
        Returns:
            Dictionary with generation results and metadata
        """
        output_path = Path(output_path)
        output_dir = output_path.parent
        output_dir.mkdir(parents=True, exist_ok=True)
        
        base_name = output_path.stem
        extension = output_path.suffix or ".png"
        
        # Get quality threshold for this document type
        threshold = self.QUALITY_THRESHOLDS.get(doc_type.lower(), 
                                                 self.QUALITY_THRESHOLDS["default"])
        
        results = {
            "user_prompt": user_prompt,
            "doc_type": doc_type,
            "quality_threshold": threshold,
            "iterations": [],
            "final_image": None,
            "final_score": 0.0,
            "success": False,
            "early_stop": False,
            "early_stop_reason": None
        }
        
        current_prompt = f"""{self.SCIENTIFIC_DIAGRAM_GUIDELINES}

USER REQUEST: {user_prompt}

Generate a publication-quality scientific diagram that meets all the guidelines above."""
        
        print(f"\n{'='*60}")
        print(f"Generating Scientific Schematic")
        print(f"{'='*60}")
        print(f"Description: {user_prompt}")
        print(f"Document Type: {doc_type}")
        print(f"Quality Threshold: {threshold}/10")
        print(f"Max Iterations: {iterations}")
        print(f"Output: {output_path}")
        print(f"{'='*60}\n")
        
        for i in range(1, iterations + 1):
            print(f"\n[Iteration {i}/{iterations}]")
            print("-" * 40)
            
            # Generate image
            print(f"Generating image...")
            image_data = self.generate_image(current_prompt)
            
            if not image_data:
                error_msg = getattr(self, '_last_error', 'Image generation failed - no image data returned')
                print(f"✗ Generation failed: {error_msg}")
                results["iterations"].append({
                    "iteration": i,
                    "success": False,
                    "error": error_msg
                })
                continue
            
            # Save iteration image
            iter_path = output_dir / f"{base_name}_v{i}{extension}"
            with open(iter_path, "wb") as f:
                f.write(image_data)
            print(f"✓ Saved: {iter_path}")
            
            # Review image using Gemini 3.1 Pro Preview
            print(f"Reviewing image with Gemini 3.1 Pro Preview...")
            critique, score, needs_improvement = self.review_image(
                str(iter_path), user_prompt, i, doc_type, iterations
            )
            print(f"✓ Score: {score}/10 (threshold: {threshold}/10)")
            
            # Save iteration results
            iteration_result = {
                "iteration": i,
                "image_path": str(iter_path),
                "prompt": current_prompt,
                "critique": critique,
                "score": score,
                "needs_improvement": needs_improvement,
                "success": True
            }
            results["iterations"].append(iteration_result)
            
            # Check if quality is acceptable - STOP EARLY if so
            if not needs_improvement:
                print(f"\n✓ Quality meets {doc_type} threshold ({score} >= {threshold})")
                print(f"  No further iterations needed!")
                results["final_image"] = str(iter_path)
                results["final_score"] = score
                results["success"] = True
                results["early_stop"] = True
                results["early_stop_reason"] = f"Quality score {score} meets threshold {threshold} for {doc_type}"
                break
            
            # If this is the last iteration, we're done regardless
            if i == iterations:
                print(f"\n⚠ Maximum iterations reached")
                results["final_image"] = str(iter_path)
                results["final_score"] = score
                results["success"] = True
                break
            
            # Quality below threshold - improve prompt for next iteration
            print(f"\n⚠ Quality below threshold ({score} < {threshold})")
            print(f"Improving prompt based on feedback...")
            current_prompt = self.improve_prompt(user_prompt, critique, i + 1)
        
        # Copy final version to output path
        if results["success"] and results["final_image"]:
            final_iter_path = Path(results["final_image"])
            if final_iter_path != output_path:
                import shutil
                shutil.copy(final_iter_path, output_path)
                print(f"\n✓ Final image: {output_path}")
        
        # Save review log
        log_path = output_dir / f"{base_name}_review_log.json"
        with open(log_path, "w") as f:
            json.dump(results, f, indent=2)
        print(f"✓ Review log: {log_path}")
        
        print(f"\n{'='*60}")
        print(f"Generation Complete!")
        print(f"Final Score: {results['final_score']}/10")
        if results["early_stop"]:
            print(f"Iterations Used: {len([r for r in results['iterations'] if r.get('success')])}/{iterations} (early stop)")
        print(f"{'='*60}\n")
        
        return results


def main():
    """Command-line interface."""
    parser = argparse.ArgumentParser(
        description="Generate scientific schematics using AI with smart iterative refinement",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  # Generate a flowchart for a journal paper
  python generate_schematic_ai.py "CONSORT participant flow diagram" -o flowchart.png --doc-type journal
  
  # Generate neural network architecture for presentation (lower threshold)
  python generate_schematic_ai.py "Transformer encoder-decoder architecture" -o transformer.png --doc-type presentation
  
  # Generate with custom max iterations for poster
  python generate_schematic_ai.py "Biological signaling pathway" -o pathway.png --iterations 2 --doc-type poster
  
  # Verbose output
  python generate_schematic_ai.py "Circuit diagram" -o circuit.png -v

Document Types (quality thresholds):
  journal      8.5/10  - Nature, Science, peer-reviewed journals
  conference   8.0/10  - Conference papers
  thesis       8.0/10  - Dissertations, theses
  grant        8.0/10  - Grant proposals
  preprint     7.5/10  - arXiv, bioRxiv, etc.
  report       7.5/10  - Technical reports
  poster       7.0/10  - Academic posters
  presentation 6.5/10  - Slides, talks
  default      7.5/10  - General purpose

Note: Multiple iterations only occur if quality is BELOW the threshold.
      If the first generation meets the threshold, no extra API calls are made.

Environment:
  OPENROUTER_API_KEY    OpenRouter API key (required)
        """
    )
    
    parser.add_argument("prompt", help="Description of the diagram to generate")
    parser.add_argument("-o", "--output", required=True, 
                       help="Output image path (e.g., diagram.png)")
    parser.add_argument("--iterations", type=int, default=2,
                       help="Maximum refinement iterations (default: 2, max: 2)")
    parser.add_argument("--doc-type", default="default",
                       choices=["journal", "conference", "poster", "presentation", 
                               "report", "grant", "thesis", "preprint", "default"],
                       help="Document type for quality threshold (default: default)")
    parser.add_argument("--api-key", help="OpenRouter API key (or set OPENROUTER_API_KEY)")
    parser.add_argument("-v", "--verbose", action="store_true",
                       help="Verbose output")
    
    args = parser.parse_args()
    
    # Check for API key
    api_key = args.api_key or os.getenv("OPENROUTER_API_KEY")
    if not api_key:
        print("Error: OPENROUTER_API_KEY environment variable not set")
        print("\nSet it with:")
        print("  export OPENROUTER_API_KEY='your_api_key'")
        print("\nOr provide via --api-key flag")
        sys.exit(1)
    
    # Validate iterations - enforce max of 2
    if args.iterations < 1 or args.iterations > 2:
        print("Error: Iterations must be between 1 and 2")
        sys.exit(1)
    
    try:
        generator = ScientificSchematicGenerator(api_key=api_key, verbose=args.verbose)
        results = generator.generate_iterative(
            user_prompt=args.prompt,
            output_path=args.output,
            iterations=args.iterations,
            doc_type=args.doc_type
        )
        
        if results["success"]:
            print(f"\n✓ Success! Image saved to: {args.output}")
            if results.get("early_stop"):
                print(f"  (Completed in {len([r for r in results['iterations'] if r.get('success')])} iteration(s) - quality threshold met)")
            sys.exit(0)
        else:
            print(f"\n✗ Generation failed. Check review log for details.")
            sys.exit(1)
    except Exception as e:
        print(f"\n✗ Error: {str(e)}")
        sys.exit(1)


if __name__ == "__main__":
    main()

#!/usr/bin/env python3
"""
Scientific schematic generation using Nano Banana 2.

Generate any scientific diagram by describing it in natural language.
Nano Banana 2 handles everything automatically with smart iterative refinement.

Smart iteration: Only regenerates if quality is below threshold for your document type.
Quality review: Uses Gemini 3.1 Pro Preview for professional scientific evaluation.

Usage:
    # Generate for journal paper (highest quality threshold)
    python generate_schematic.py "CONSORT flowchart" -o flowchart.png --doc-type journal
    
    # Generate for presentation (lower threshold, faster)
    python generate_schematic.py "Transformer architecture" -o transformer.png --doc-type presentation
    
    # Generate for poster
    python generate_schematic.py "MAPK signaling pathway" -o pathway.png --doc-type poster
"""

import argparse
import os
import subprocess
import sys
from pathlib import Path


def main():
    """Command-line interface."""
    parser = argparse.ArgumentParser(
        description="Generate scientific schematics using AI with smart iterative refinement",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
How it works:
  Simply describe your diagram in natural language
  Nano Banana 2 generates it automatically with:
  - Smart iteration (only regenerates if quality is below threshold)
  - Quality review by Gemini 3.1 Pro Preview
  - Document-type aware quality thresholds
  - Publication-ready output

Document Types (quality thresholds):
  journal      8.5/10  - Nature, Science, peer-reviewed journals
  conference   8.0/10  - Conference papers
  thesis       8.0/10  - Dissertations, theses
  grant        8.0/10  - Grant proposals
  preprint     7.5/10  - arXiv, bioRxiv, etc.
  report       7.5/10  - Technical reports
  poster       7.0/10  - Academic posters
  presentation 6.5/10  - Slides, talks
  default      7.5/10  - General purpose

Examples:
  # Generate for journal paper (strict quality)
  python generate_schematic.py "CONSORT participant flow" -o flowchart.png --doc-type journal
  
  # Generate for poster (moderate quality)
  python generate_schematic.py "Transformer architecture" -o arch.png --doc-type poster
  
  # Generate for slides (faster, lower threshold)
  python generate_schematic.py "System diagram" -o system.png --doc-type presentation
  
  # Custom max iterations
  python generate_schematic.py "Complex pathway" -o pathway.png --iterations 2
  
  # Verbose output
  python generate_schematic.py "Circuit diagram" -o circuit.png -v

Environment Variables:
  OPENROUTER_API_KEY    Required for AI generation
        """
    )
    
    parser.add_argument("prompt", 
                       help="Description of the diagram to generate")
    parser.add_argument("-o", "--output", required=True,
                       help="Output file path")
    parser.add_argument("--doc-type", default="default",
                       choices=["journal", "conference", "poster", "presentation",
                               "report", "grant", "thesis", "preprint", "default"],
                       help="Document type for quality threshold (default: default)")
    parser.add_argument("--iterations", type=int, default=2,
                       help="Maximum refinement iterations (default: 2, max: 2)")
    parser.add_argument("--api-key", 
                       help="OpenRouter API key (or use OPENROUTER_API_KEY env var)")
    parser.add_argument("-v", "--verbose", action="store_true",
                       help="Verbose output")
    
    args = parser.parse_args()
    
    # Check for API key
    api_key = args.api_key or os.getenv("OPENROUTER_API_KEY")
    if not api_key:
        print("Error: OPENROUTER_API_KEY environment variable not set")
        print("\nFor AI generation, you need an OpenRouter API key.")
        print("Get one at: https://openrouter.ai/keys")
        print("\nSet it with:")
        print("  export OPENROUTER_API_KEY='your_api_key'")
        print("\nOr use --api-key flag")
        sys.exit(1)
    
    # Find AI generation script
    script_dir = Path(__file__).parent
    ai_script = script_dir / "generate_schematic_ai.py"
    
    if not ai_script.exists():
        print(f"Error: AI generation script not found: {ai_script}")
        sys.exit(1)
    
    # Build command
    cmd = [sys.executable, str(ai_script), args.prompt, "-o", args.output]
    
    if args.doc_type != "default":
        cmd.extend(["--doc-type", args.doc_type])
    
    # Enforce max 2 iterations
    iterations = min(args.iterations, 2)
    if iterations != 2:
        cmd.extend(["--iterations", str(iterations)])
    
    if args.verbose:
        cmd.append("-v")
    
    # Execute — pass API key via environment to avoid exposure in process listings
    try:
        env = os.environ.copy()
        if api_key:
            env["OPENROUTER_API_KEY"] = api_key
        result = subprocess.run(cmd, check=False, env=env)
        sys.exit(result.returncode)
    except Exception as e:
        print(f"Error executing AI generation: {e}")
        sys.exit(1)


if __name__ == "__main__":
    main()