"""
Graph builder module for converting GDELT data to graph formats
"""
import pandas as pd
import networkx as nx
import json

class GraphBuilder:
    """Base class for building graph from GDELT data"""
    def process_entities(self, row):
        """Process entities from a row and return nodes and relationships"""
        nodes = []
        relationships = []
        event_id = row["GKGRECORDID"]
        event_date = row["DATE"]
        event_source = row["SourceCommonName"]
        event_document_id = row["DocumentIdentifier"]
        # event_image = row["V2.1SharingImage"] if pd.notna(row["V2.1SharingImage"]) else ""
        event_quotations = row["V2.1Quotations"] if pd.notna(row["V2.1Quotations"]) else ""
        event_tone = float(row["tone"]) if pd.notna(row["tone"]) else 0.0

        # Add event node
        nodes.append({
            "id": event_id,
            "type": "event",
            "properties": {
                "date": event_date,
                "source": event_source,
                "document": event_document_id,
                # "image": event_image,
                "quotations": event_quotations,
                "tone": event_tone
            }
        })

        # Process each entity type
        entity_mappings = {
            "V2EnhancedPersons": ("Person", "MENTIONED_IN"),
            "V2EnhancedOrganizations": ("Organization", "MENTIONED_IN"),
            "V2EnhancedLocations": ("Location", "LOCATED_IN"),
            "V2EnhancedThemes": ("Theme", "CATEGORIZED_AS"),
            "V2.1AllNames": ("Name", "MENTIONED_IN"),
            "V2.1Counts": ("Count", "MENTIONED_IN"),
            "V2.1Amounts": ("Amount", "MENTIONED_IN"),
        }

        for field, (label, relationship) in entity_mappings.items():
            if pd.notna(row[field]):
                entities = [e.strip() for e in row[field].split(';') if e.strip()]
                for entity in entities:
                    nodes.append({
                        "id": entity,
                        "type": label.lower(),
                        "properties": {"name": entity}
                    })
                    relationships.append({
                        "from": entity,
                        "to": event_id,
                        "type": relationship,
                        "properties": {"created_at": event_date}
                    })

        return nodes, relationships

class NetworkXBuilder(GraphBuilder):
    """Builder for NetworkX graphs"""
    def build_graph(self, df):
        G = nx.Graph()

        for _, row in df.iterrows():
            nodes, relationships = self.process_entities(row)

            # Add nodes
            for node in nodes:
                G.add_node(node["id"],
                          type=node["type"],
                          **node["properties"])

            # Add relationships
            for rel in relationships:
                G.add_edge(rel["from"],
                          rel["to"],
                          relationship=rel["type"],
                          **rel["properties"])

        return G

class Neo4jBuilder(GraphBuilder):
    def __init__(self, uri, user, password):
        self.driver = GraphDatabase.driver(uri, auth=(user, password))
        self.logger = logging.getLogger(__name__)

    def close(self):
        self.driver.close()

    def build_graph(self, df):
        with self.driver.session() as session:
            for _, row in df.iterrows():
                nodes, relationships = self.process_entities(row)

                # Create nodes and relationships in Neo4j
                try:
                    session.execute_write(self._create_graph_elements,
                                        nodes, relationships)
                except Exception as e:
                    self.logger.error(f"Error processing row {row['GKGRECORDID']}: {str(e)}")

    def _create_graph_elements(self, tx, nodes, relationships):
        # Create nodes
        for node in nodes:
            query = f"""
            MERGE (n:{node['type']} {{id: $id}})
            SET n += $properties
            """
            tx.run(query, id=node["id"], properties=node["properties"])

        # Create relationships
        for rel in relationships:
            query = f"""
            MATCH (a {{id: $from_id}})
            MATCH (b {{id: $to_id}})
            MERGE (a)-[r:{rel['type']}]->(b)
            SET r += $properties
            """
            tx.run(query,
                  from_id=rel["from"],
                  to_id=rel["to"],
                  properties=rel["properties"])

class StreamlitGraphBuilder:
    """Adapted graph builder for Streamlit visualization"""
    def __init__(self):
        self.G = nx.Graph()
        
    def process_row(self, row):
        """Process a single row of data"""
        event_id = row["GKGRECORDID"]
        event_props = {
            "type": "event",  # already in lowercase
            "date": row["DATE"],
            "source": row["SourceCommonName"],
            "document": row["DocumentIdentifier"],
            "tone": row["tone"],
            # Store display name in its original format if needed.
            "name": row["SourceCommonName"]
        }
        
        self.G.add_node(event_id, **event_props)
        
        # Use lowercase node types for consistency in lookups.
        entity_types = {
            "V2EnhancedPersons": ("person", "MENTIONED_IN"),
            "V2EnhancedOrganizations": ("organization", "MENTIONED_IN"),
            "V2EnhancedLocations": ("location", "LOCATED_IN"),
            "V2EnhancedThemes": ("theme", "CATEGORIZED_AS"),
            "V2.1AllNames": ("name", "MENTIONED_IN"),
            "V2.1Counts": ("count", "MENTIONED_IN"),
            "V2.1Amounts": ("amount", "MENTIONED_IN"),
        }
        
        for col, (node_type, rel_type) in entity_types.items():
            if pd.notna(row[col]):
                # The actual display value (which may be in Parent Case) is preserved in the "name" attribute.
                entities = [e.strip() for e in row[col].split(';') if e.strip()]
                for entity in entities:
                    self.G.add_node(entity, type=node_type, name=entity)
                    self.G.add_edge(entity, event_id, 
                                    relationship=rel_type, 
                                    date=row["DATE"])

class StLinkBuilder(GraphBuilder):
    """Builder for st-link-analysis compatible graphs"""
    def build_graph(self, df):
        """Build graph in st-link-analysis format"""
        all_nodes = []
        all_edges = []
        edge_counter = 0

        # Track nodes we've already added to avoid duplicates
        added_nodes = set()
        
        for _, row in df.iterrows():
            nodes, relationships = self.process_entities(row)
            
            # Process nodes
            for node in nodes:
                if node["id"] not in added_nodes:
                    stlink_node = {
                        "data": {
                            "id": str(node["id"]),
                            "label": node["type"].upper(),
                            **node["properties"]
                        }
                    }
                    all_nodes.append(stlink_node)
                    added_nodes.add(node["id"])
            
            # Process relationships/edges
            for rel in relationships:
                edge_counter += 1
                stlink_edge = {
                    "data": {
                        "id": f"e{edge_counter}",
                        "source": str(rel["from"]),
                        "target": str(rel["to"]),
                        "label": rel["type"],
                        **rel["properties"]
                    }
                }
                all_edges.append(stlink_edge)
        
        return {
            "nodes": all_nodes,
            "edges": all_edges
        }

    def write_json(self, graph_data, filename):
        """Write graph to JSON file"""
        with open(filename, 'w') as f:
            json.dump(graph_data, f, indent=2)