import gradio as gr
import json
from graphviz import Digraph
import os
from tempfile import NamedTemporaryFile

def generate_concept_map(json_input: str) -> str:
    """
    Generate concept map from JSON and return as image file
    
    Args:
        json_input (str): JSON describing the concept map structure.
        
            REQUIRED FORMAT EXAMPLE:
            {
                "central_node": "AI",
                "nodes": [
                    {
                        "id": "ml",
                        "label": "Machine Learning",
                        "relationship": "subcategory",
                        "subnodes": [
                            {
                                "id": "dl",
                                "label": "Deep Learning",
                                "relationship": "type",
                                "subnodes": [
                                    {
                                        "id": "cnn",
                                        "label": "CNN",
                                        "relationship": "architecture"
                                    }
                                ]
                            }
                        ]
                    }
                ]
            }
    
    Returns:
        str: Path to generated PNG image file
    """
    try:
        if not json_input.strip():
            return "Error: Empty input"
            
        data = json.loads(json_input)
        
        if 'central_node' not in data or 'nodes' not in data:
            raise ValueError("Missing required fields: central_node or nodes")

        # Create graph
        dot = Digraph(
            name='ConceptMap',
            format='png',
            graph_attr={
                'rankdir': 'TB',
                'splines': 'ortho',
                'bgcolor': 'transparent'
            }
        )
        
        # Central node (ellipse)
        dot.node(
            'central',
            data['central_node'],
            shape='ellipse',
            style='filled',
            fillcolor='#2196F3',
            fontcolor='white',
            fontsize='14'
        )
        
        # Process nodes (rectangles)
        for node in data['nodes']:
            node_id = node.get('id')
            label = node.get('label')
            relationship = node.get('relationship')
            
            # Validate node
            if not all([node_id, label, relationship]):
                raise ValueError(f"Invalid node: {node}")
                
            # Create main node (rectangle)
            dot.node(
                node_id,
                label,
                shape='box',
                style='filled',
                fillcolor='#4CAF50',
                fontcolor='white',
                fontsize='12'
            )
            
            # Connect to central node
            dot.edge(
                'central',
                node_id,
                label=relationship,
                color='#9C27B0',
                fontsize='10'
            )
            
            # Process subnodes (rectangles with lighter fill)
            for subnode in node.get('subnodes', []):
                sub_id = subnode.get('id')
                sub_label = subnode.get('label')
                sub_rel = subnode.get('relationship')
                
                if not all([sub_id, sub_label, sub_rel]):
                    raise ValueError(f"Invalid subnode: {subnode}")
                    
                dot.node(
                    sub_id,
                    sub_label,
                    shape='box',
                    style='filled',
                    fillcolor='#FFA726',
                    fontcolor='white',
                    fontsize='10'
                )
                
                dot.edge(
                    node_id,
                    sub_id,
                    label=sub_rel,
                    color='#E91E63',
                    fontsize='8'
                )

        # Save to temporary file
        with NamedTemporaryFile(delete=False, suffix='.png') as tmp:
            dot.render(tmp.name, format='png', cleanup=True)
            return tmp.name + '.png'

    except json.JSONDecodeError:
        return "Error: Invalid JSON format"
    except Exception as e:
        return f"Error: {str(e)}"

if __name__ == "__main__":
    # Complex sample JSON
    sample_json = """
    {
      "central_node": "Artificial Intelligence (AI)",
      "nodes": [
        {
          "id": "ml",
          "label": "Machine Learning",
          "relationship": "Core Component",
          "subnodes": [
            {
              "id": "sl",
              "label": "Supervised Learning",
              "relationship": "Learning Type",
              "subnodes": [
                {
                  "id": "reg",
                  "label": "Regression",
                  "relationship": "Technique",
                  "subnodes": [
                    {"id": "lr", "label": "Linear Regression", "relationship": "Algorithm"}
                  ]
                },
                {
                  "id": "clf",
                  "label": "Classification",
                  "relationship": "Technique",
                  "subnodes": [
                    {"id": "svm", "label": "SVM", "relationship": "Algorithm"},
                    {"id": "rf", "label": "Random Forest", "relationship": "Algorithm"}
                  ]
                }
              ]
            },
            {
              "id": "ul",
              "label": "Unsupervised Learning",
              "relationship": "Learning Type",
              "subnodes": [
                {
                  "id": "clus",
                  "label": "Clustering",
                  "relationship": "Technique",
                  "subnodes": [
                    {"id": "kmeans", "label": "K-Means", "relationship": "Algorithm"}
                  ]
                }
              ]
            }
          ]
        },
        {
          "id": "nlp",
          "label": "NLP",
          "relationship": "Application Domain",
          "subnodes": [
            {
              "id": "sa",
              "label": "Sentiment Analysis",
              "relationship": "Task",
              "subnodes": [
                {"id": "tb", "label": "Transformer-Based", "relationship": "Approach"}
              ]
            }
          ]
        }
      ]
    }
    """
    
    demo = gr.Interface(
        fn=generate_concept_map,
        inputs=gr.Textbox(
            value=sample_json,
            placeholder="Paste JSON following the documented format",
            label="Structured JSON Input",
            lines=25
        ),
        outputs=gr.Image(
            label="Generated Concept Map",
            type="filepath",
            show_download_button=True
        ),
        title="Advanced Concept Map Generator",
        description="Create multi-level concept maps from properly formatted JSON"
    )
    
    demo.launch(
        mcp_server=True,
        share=False,
        server_port=7860,
        server_name="0.0.0.0"
    )