import spaces
import gradio as gr
from phi3_instruct_graph import MODEL_LIST, Phi3InstructGraph
import rapidjson
from pyvis.network import Network
import networkx as nx
import spacy
from spacy import displacy
from spacy.tokens import Span
import random
import os
import pickle

# Constants
TITLE = "🌐 GraphMind: Phi-3 Instruct Graph Explorer"
SUBTITLE = "✨ Extract and visualize knowledge graphs from any text in multiple languages"

# Basic CSS for styling
CUSTOM_CSS = """
.gradio-container {
    font-family: 'Segoe UI', Roboto, sans-serif;
}
"""

# Cache directory and file paths
CACHE_DIR = "cache"
EXAMPLE_CACHE_FILE = os.path.join(CACHE_DIR, "first_example_cache.pkl")

# Create cache directory if it doesn't exist
os.makedirs(CACHE_DIR, exist_ok=True)

# Color utilities
def get_random_light_color():
    r = random.randint(140, 255)
    g = random.randint(140, 255)
    b = random.randint(140, 255)
    return f"#{r:02x}{g:02x}{b:02x}"

# Text preprocessing
def handle_text(text):
    return " ".join(text.split())

# Main processing functions
@spaces.GPU
def extract(text, model):
    try:
        model = Phi3InstructGraph(model=model)    
        result = model.extract(text)
        return rapidjson.loads(result)
    except Exception as e:
        raise gr.Error(f"Extraction error: {str(e)}")

def find_token_indices(doc, substring, text):
    result = []
    start_index = text.find(substring)
    
    while start_index != -1:
        end_index = start_index + len(substring)
        start_token = None
        end_token = None

        for token in doc:
            if token.idx == start_index:
                start_token = token.i
            if token.idx + len(token) == end_index:
                end_token = token.i + 1

        if start_token is not None and end_token is not None:
            result.append({
                "start": start_token,
                "end": end_token
            })
        
        # Search for next occurrence
        start_index = text.find(substring, end_index)

    return result

def create_custom_entity_viz(data, full_text):
    nlp = spacy.blank("xx")
    doc = nlp(full_text)

    spans = []
    colors = {}
    for node in data["nodes"]:
        entity_spans = find_token_indices(doc, node["id"], full_text)
        for dataentity in entity_spans:
            start = dataentity["start"]
            end = dataentity["end"]
            
            if start < len(doc) and end <= len(doc):
                # Check for overlapping spans
                overlapping = any(s.start < end and start < s.end for s in spans)
                if not overlapping:                
                    node_type = node.get("type", "Entity")
                    span = Span(doc, start, end, label=node_type)
                    spans.append(span)
                    if node_type not in colors:
                        colors[node_type] = get_random_light_color()

    doc.set_ents(spans, default="unmodified")
    doc.spans["sc"] = spans

    options = {
        "colors": colors,
        "ents": list(colors.keys()),
        "style": "ent",
        "manual": True
    }

    html = displacy.render(doc, style="span", options=options)
    # Add custom styling to the entity visualization
    styled_html = f"""
    <div style="padding: 20px; border-radius: 12px; background-color: white; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);">
        {html}
    </div>
    """
    return styled_html

def create_graph(json_data):
    G = nx.Graph()

    # Add nodes with tooltips - with error handling for missing keys
    for node in json_data['nodes']:
        # Get node type with fallback
        node_type = node.get("type", "Entity")
        # Get detailed type with fallback
        detailed_type = node.get("detailed_type", node_type)
        
        # Use node ID and type info for the tooltip
        G.add_node(node['id'], title=f"{node_type}: {detailed_type}")

    # Add edges with labels
    for edge in json_data['edges']:
        # Check if the required keys exist
        if 'from' in edge and 'to' in edge:
            label = edge.get('label', 'related')
            G.add_edge(edge['from'], edge['to'], title=label, label=label)

    # Create network visualization
    nt = Network(
        width="100%",
        height="700px",
        directed=True,
        notebook=False,
        bgcolor="#f8fafc", 
        font_color="#1e293b"
    )
    
    # Configure network display
    nt.from_nx(G)
    nt.barnes_hut(
        gravity=-3000,
        central_gravity=0.3,
        spring_length=50,
        spring_strength=0.001,
        damping=0.09,
        overlap=0,
    )
    
    # Customize edge appearance
    for edge in nt.edges:
        edge['width'] = 2
        edge['arrows'] = {'to': {'enabled': True, 'type': 'arrow'}}
        edge['color'] = {'color': '#6366f1', 'highlight': '#4f46e5'}
        edge['font'] = {'size': 12, 'color': '#4b5563', 'face': 'Arial'}

    # Customize node appearance
    for node in nt.nodes:
        node['color'] = {'background': '#e0e7ff', 'border': '#6366f1', 'highlight': {'background': '#c7d2fe', 'border': '#4f46e5'}}
        node['font'] = {'size': 14, 'color': '#1e293b'}
        node['shape'] = 'dot'
        node['size'] = 25

    # Generate HTML with iframe to isolate styles
    html = nt.generate_html()
    html = html.replace("'", '"')

    return f"""<iframe style="width: 100%; height: 700px; margin: 0 auto; border-radius: 12px; box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.1), 0 4px 6px -4px rgba(0, 0, 0, 0.1);" 
        name="result" allow="midi; geolocation; microphone; camera; display-capture; encrypted-media;" 
        sandbox="allow-modals allow-forms allow-scripts allow-same-origin allow-popups 
        allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
        allowpaymentrequest="" frameborder="0" srcdoc='{html}'></iframe>"""

def process_and_visualize(text, model, progress=gr.Progress()):
    if not text or not model:
        raise gr.Error("⚠️ Both text and model must be provided.")
    
    # Check if we're processing the first example for caching
    is_first_example = text == EXAMPLES[0][0]
    
    # Try to load from cache if it's the first example
    if is_first_example and os.path.exists(EXAMPLE_CACHE_FILE):
        try:
            progress(0.3, desc="Loading from cache...")
            with open(EXAMPLE_CACHE_FILE, 'rb') as f:
                cache_data = pickle.load(f)
                
            progress(1.0, desc="Loaded from cache!")
            return cache_data["graph_html"], cache_data["entities_viz"], cache_data["json_data"], cache_data["stats"]
        except Exception as e:
            print(f"Cache loading error: {str(e)}")
            # Continue with normal processing if cache fails
    
    progress(0, desc="Starting extraction...")
    json_data = extract(text, model)
    
    progress(0.5, desc="Creating entity visualization...")
    entities_viz = create_custom_entity_viz(json_data, text)
    
    progress(0.8, desc="Building knowledge graph...")
    graph_html = create_graph(json_data)
    
    node_count = len(json_data["nodes"])
    edge_count = len(json_data["edges"])
    stats = f"📊 Extracted {node_count} entities and {edge_count} relationships"
    
    # Save to cache if it's the first example
    if is_first_example:
        try:
            cache_data = {
                "graph_html": graph_html,
                "entities_viz": entities_viz,
                "json_data": json_data,
                "stats": stats
            }
            with open(EXAMPLE_CACHE_FILE, 'wb') as f:
                pickle.dump(cache_data, f)
        except Exception as e:
            print(f"Cache saving error: {str(e)}")
    
    progress(1.0, desc="Complete!")
    return graph_html, entities_viz, json_data, stats

# Example texts in different languages
EXAMPLES = [
    [handle_text("""My son Tom, as my direct descendant and John's father, has built a long-standing, companion-like relationship with Brown, significantly influencing each other's growth within the family.
Mary, my maternal grandmother, has imparted unwavering wisdom and love not only to Brown and me but also to Tom and John, and she has maintained a deep familial bond with Jane's mother.
Brown, transcending a mere brotherly relationship, has forged a strong father-child bond with Daniel as his biological father, and with Jane, they have significantly impacted each other's lives through numerous family gatherings, forming a solid connection.
Jane, my cousin, contributes to the family's unity through her informal rapport with Tom, and with Lisa, she shares a special bond that goes beyond ordinary cousin relationships, mutually supporting each other.""")],
    
    [handle_text("""Pop star Justin Timberlake, 43, had his driver's license suspended by a New York judge during a virtual 
    court hearing on August 2, 2024. The suspension follows Timberlake's arrest for driving while intoxicated (DWI) 
    in Sag Harbor on June 18. Timberlake, who is currently on tour in Europe, 
    pleaded not guilty to the charges.""")],
    
    [handle_text("""내 아들 민수는 내 직계 후손이자 준호의 부친으로, 태현과는 오랜 우애를 쌓아온 동료 같은 관계를 유지하며, 가문 내에서 서로의 성장에 큰 영향을 주고 있다.  
순자는 나의 외조모로서, 나와 태현은 물론 민수와 준호에게 한결같은 지혜와 사랑을 전해주었으며, 지영의 어머니와도 깊은 가족적 유대를 공유해왔다.  
태현은 단순한 형제 관계를 넘어, 현우의 친부로서 그와 굳건한 부자 관계를 이루며, 지영과는 수많은 가족 모임에서 서로의 삶에 큰 영향을 주며 단단한 인연을 만들어왔다.  
지영은 나의 사촌으로서, 민수와는 비공식적 친분을 통해 가문의 화합에 기여하며, 수진과는 이종사촌 이상의 특별한 우애로 서로를 지탱하는 사이이다.
""")],
    
    [handle_text("""한국 영화 '기생충'은 2020년 아카데미 시상식에서 작품상, 감독상, 각본상, 국제영화상 등 4개 부문을 수상하며 역사를 새로 썼다. 
    봉준호 감독이 연출한 이 영화는 한국 영화 최초로 칸 영화제 황금종려상도 수상했으며, 전 세계적으로 엄청난 흥행과 
    평단의 호평을 받았다.""")]
]

# Function to preprocess the first example when the app starts
def generate_first_example_cache():
    """Generate cache for the first example if it doesn't exist"""
    if not os.path.exists(EXAMPLE_CACHE_FILE):
        print("Generating cache for first example...")
        try:
            text = EXAMPLES[0][0]
            model = MODEL_LIST[0] if MODEL_LIST else None
            
            if model:
                # Extract data
                json_data = extract(text, model)
                entities_viz = create_custom_entity_viz(json_data, text)
                graph_html = create_graph(json_data)
                
                node_count = len(json_data["nodes"])
                edge_count = len(json_data["edges"])
                stats = f"📊 Extracted {node_count} entities and {edge_count} relationships"
                
                # Save to cache
                cache_data = {
                    "graph_html": graph_html,
                    "entities_viz": entities_viz,
                    "json_data": json_data,
                    "stats": stats
                }
                with open(EXAMPLE_CACHE_FILE, 'wb') as f:
                    pickle.dump(cache_data, f)
                
                print("First example cache generated successfully")
                return cache_data
        except Exception as e:
            print(f"Error generating first example cache: {str(e)}")
    else:
        print("First example cache already exists")
        try:
            with open(EXAMPLE_CACHE_FILE, 'rb') as f:
                return pickle.load(f)
        except Exception as e:
            print(f"Error loading existing cache: {str(e)}")
    
    return None

def create_ui():
    # Try to generate/load the first example cache
    first_example_cache = generate_first_example_cache()
    
    with gr.Blocks(css=CUSTOM_CSS, title=TITLE) as demo:
        # Header 
        gr.Markdown(f"# {TITLE}")
        gr.Markdown(f"{SUBTITLE}")
        
        with gr.Row():
            gr.Markdown("🌍 **Multilingual Support Available**")
        
        # Main content area - redesigned layout
        with gr.Row():
            # Left panel - Input controls
            with gr.Column(scale=1):
                input_model = gr.Dropdown(
                    MODEL_LIST, 
                    label="🤖 Select Model",
                    info="Choose a model to process your text",
                    value=MODEL_LIST[0] if MODEL_LIST else None
                )
                
                input_text = gr.TextArea(
                    label="📝 Input Text", 
                    info="Enter text in any language to extract a knowledge graph",
                    placeholder="Enter text here...",
                    lines=8,
                    value=EXAMPLES[0][0]  # Pre-fill with first example
                )
                
                with gr.Row():
                    submit_button = gr.Button("🚀 Extract & Visualize", variant="primary", scale=2)
                    clear_button = gr.Button("🔄 Clear", variant="secondary", scale=1)
                
                # Statistics will appear here
                stats_output = gr.Markdown("", label="🔍 Analysis Results")
            
            # Right panel - Examples moved to right side
            with gr.Column(scale=1):
                gr.Markdown("## 📚 Example Texts")
                gr.Examples(
                    examples=EXAMPLES,
                    inputs=input_text,
                    label=""
                )
                
                # JSON output moved to right side as well
                with gr.Accordion("📊 JSON Data", open=False):
                    output_json = gr.JSON(label="")
        
        # Full width visualization area at the bottom
        with gr.Row():
            # Full width visualization area
            with gr.Tabs():
                with gr.TabItem("🧩 Knowledge Graph"):
                    output_graph = gr.HTML(label="")
                
                with gr.TabItem("🏷️ Entity Recognition"):
                    output_entity_viz = gr.HTML(label="")
        
        # Functionality
        submit_button.click(
            fn=process_and_visualize, 
            inputs=[input_text, input_model],
            outputs=[output_graph, output_entity_viz, output_json, stats_output]
        )
        
        clear_button.click(
            fn=lambda: [None, None, None, ""],
            inputs=[],
            outputs=[output_graph, output_entity_viz, output_json, stats_output]
        )
        
        # Set initial values from cache if available
        if first_example_cache:
            # Use this to set initial values when the app loads
            demo.load(
                lambda: [
                    first_example_cache["graph_html"], 
                    first_example_cache["entities_viz"], 
                    first_example_cache["json_data"], 
                    first_example_cache["stats"]
                ],
                inputs=None,
                outputs=[output_graph, output_entity_viz, output_json, stats_output]
            )
        
        # Footer
        gr.Markdown("---")
        gr.Markdown("📋 **Instructions:** Enter text in any language, select a model, and click 'Extract & Visualize' to generate a knowledge graph.")
        gr.Markdown("🛠️ Powered by Phi-3 Instruct Graph | Emergent Methods")
        
    return demo

demo = create_ui()
demo.launch(share=False)