application_neo4j/app_algorithms.py

# Importation des bibliothèques nécessaires
from graphdatascience import GraphDataScience
from typing import Dict, List, Any
import pandas as pd
from translations import t
from neo4j import Query

def run_gds_bfs(
    gds: GraphDataScience,
    natural_graph_name: str,
    reverse_graph_name: str,
    source_name: str,
    max_depth: int = None,
    expert=False,
    source_labels=None,
    job_id: str = None,
    progress_callback=None,
    neo4j_driver=None,
    cancel_check=None,
) -> Dict[str, Any]:
    """
    Parcourt les descendants et ascendants avec le fork OpenGDS.

    Le plugin personnalisé ajoute au résultat standard ``nodeIds`` une liste
    ``depths`` alignée. La profondeur minimale de chaque nœud est ainsi calculée
    pendant le BFS, sans second parcours Cypher.

    Returns:
        L'identifiant et le label de la source, ainsi que les résultats GDS
        des parcours descendant et ascendant.
    """
    requested_source_labels = [
        label
        for label in (source_labels or [])
        if label in ("Model", "Dataset", "Author")
    ]
    try:
        source_id_result = gds.run_cypher(
            """
            MATCH (n {name: $source_name})
            WHERE size($source_labels) = 0
               OR any(label IN labels(n) WHERE label IN $source_labels)
            RETURN id(n) AS id, labels(n) AS label
            """,
            {
                "source_name": source_name,
                "source_labels": requested_source_labels,
            },
        )

        if source_id_result.empty:
            print(f"Le modèle ou dataset avec le nom '{source_name}' n'a pas été trouvé.")
            return None

        label_preferences = requested_source_labels or [
            "Model",
            "Dataset",
            "Author",
        ]
        selected_source = None
        source_label = None
        for preferred_label in label_preferences:
            matching_sources = source_id_result[
                source_id_result["label"].apply(
                    lambda node_labels: preferred_label in node_labels
                )
            ]
            if not matching_sources.empty:
                selected_source = matching_sources.iloc[0]
                source_label = preferred_label
                break
        if selected_source is None:
            selected_source = source_id_result.iloc[0]
            source_label = selected_source["label"][0]

        if source_label == "Author" and not expert:
            print(f"Le modèle ou dataset avec le nom '{source_name}' n'a pas été trouvé.")
            return None

        source_node_id = int(selected_source["id"])
    except Exception as e:
        print(f"Erreur lors de la recherche de l'ID du nœud source pour '{source_name}': {e}")
        return None

    bfs_params = {"sourceNode": source_node_id}
    if max_depth is not None:
        bfs_params["maxDepth"] = max_depth

    def run_bfs(graph_name, params):
        if cancel_check:
            cancel_check()
        if neo4j_driver is None:
            graph = gds.graph.get(graph_name)
            result = gds.bfs.stream(graph, **params)
        else:
            query = Query(
                """
                CALL gds.bfs.stream($graph_name, $configuration)
                YIELD nodeIds, depths
                RETURN nodeIds, depths
                """,
                metadata={"search_job_id": job_id},
            )
            with neo4j_driver.session() as neo4j_session:
                records = neo4j_session.run(
                    query,
                    {
                        "graph_name": graph_name,
                        "configuration": params,
                    },
                )
                result = pd.DataFrame(
                    record.data() for record in records
                )
        if cancel_check:
            cancel_check()
        return result

    desc_job_id = f"{job_id}-descendants" if job_id else None
    if desc_job_id:
        bfs_params["jobId"] = desc_job_id
    if progress_callback:
        progress_callback("searching_descendants", desc_job_id)
    desc_df = run_bfs(natural_graph_name, bfs_params)
    _validate_depths_result(desc_df)
    print("BFS descendants terminé avec les profondeurs.")

    asc_job_id = f"{job_id}-ancestors" if job_id else None
    if asc_job_id:
        bfs_params["jobId"] = asc_job_id
    elif "jobId" in bfs_params:
        del bfs_params["jobId"]
    if progress_callback:
        progress_callback("searching_ancestors", asc_job_id)
    asc_df = run_bfs(reverse_graph_name, bfs_params)
    _validate_depths_result(asc_df)
    print("BFS ascendants terminé avec les profondeurs.")

    return {
        "source_node": source_node_id,
        "source_label": source_label,
        "descendant": desc_df,
        "ascendant": asc_df,
    }


def _validate_depths_result(result: pd.DataFrame) -> None:
    """Fail explicitly if Neo4j did not load the custom OpenGDS plugin."""
    if result.empty:
        return
    if "depths" not in result.columns:
        raise RuntimeError(
            "Le plugin OpenGDS personnalisé n'est pas chargé : "
            "gds.bfs.stream ne renvoie pas la colonne depths."
        )
    node_ids = result["nodeIds"].iloc[0]
    depths = result["depths"].iloc[0]
    if len(node_ids) != len(depths):
        raise RuntimeError(
            "Résultat BFS invalide : nodeIds et depths n'ont pas la même taille."
        )





def get_genealogy_highlights(gds: "GraphDataScience", model_name: str, num_highlights: int = 2, lang: str = "fr") -> Dict:
    """
    Trouve les modèles clés dans l'ascendance et la descendance (1er/2e plus cités/téléchargés).
    
    Args:
        gds: L'instance de GraphDataScience.
        model_name: Le nom du modèle de départ.
        num_highlights: Le nombre de modèles à récupérer pour chaque catégorie (par défaut 2).

    Returns:
        Un dictionnaire contenant les listes de modèles unifiés pour l'affichage.
    """
    highlights = {
        "desc_unique_models": [],
        "asc_unique_models": []
    }

    # --- DÉFINITION CENTRALE DES BADGES ---
    # Centraliser les badges ici rend le code beaucoup plus facile à modifier.
    # Les classes CSS sont directement des classes Bootstrap 5 pour simplifier le rendu dans le template HTML.
    badges_info = {
    'desc_cited_1': {
        'text_key': 'badge.desc_cited_1.text',
        'class': 'bg-success',
        'title_key': 'badge.desc_cited_1.title'
    },
    'desc_cited_2': {
        'text_key': 'badge.desc_cited_2.text',
        'class': 'bg-success bg-opacity-75',
        'title_key': 'badge.desc_cited_2.title'
    },
    'desc_downloaded_1': {
        'text_key': 'badge.desc_downloaded_1.text',
        'class': 'beta',
        'title_key': 'badge.desc_downloaded_1.title'
    },
    'desc_downloaded_2': {
        'text_key': 'badge.desc_downloaded_2.text',
        'class': 'alpha',
        'title_key': 'badge.desc_downloaded_2.title'
    },
    
    'asc_foundation': {
        'text_key': 'badge.asc_foundation.text',
        'class': 'bg-warning text-dark',
        'title_key': 'badge.asc_foundation.title'
    },
    'asc_cited_1': {
        'text_key': 'badge.asc_cited_1.text',
        'class': 'bg-success',
        'title_key': 'badge.asc_cited_1.title'
    },
    'asc_cited_2': {
        'text_key': 'badge.asc_cited_2.text',
        'class': 'bg-success bg-opacity-75',
        'title_key': 'badge.asc_cited_2.title'
    },
    'asc_downloaded_1': {
        'text_key': 'badge.asc_downloaded_1.text',
        'class': 'beta',
        'title_key': 'badge.asc_downloaded_1.title'
    },
    'asc_downloaded_2': {
        'text_key': 'badge.asc_downloaded_2.text',
        'class': 'alpha',
        'title_key': 'badge.asc_downloaded_2.title'
    },
    }
    

    def process_and_assign_badges(
        unified_dict: Dict, 
        model_list: List[Dict], 
        badge_keys: List[str]
    ):
        """
        Fonction utilitaire pour ajouter des modèles et leurs badges à un dictionnaire unifié.
        Cela évite la duplication de code pour chaque catégorie (cité, téléchargé, etc.).
        """
        for i, model in enumerate(model_list):
            if i < len(badge_keys): # S'assurer qu'on a un badge défini pour ce rang
                model_name_key = model['name']
                badge_key = badge_keys[i]
                
                # Ajouter le modèle au dictionnaire s'il n'y est pas déjà
                if model_name_key not in unified_dict:
                    unified_dict[model_name_key] = model.copy()
                    unified_dict[model_name_key]['badges'] = []
                
                # Ajouter le badge correspondant
                badge_to_add = badges_info[badge_key]
                if badge_to_add not in unified_dict[model_name_key]['badges']:
                    unified_dict[model_name_key]['badges'].append(badge_to_add)

    # ==========================================================================
    # 1. GESTION DE LA DESCENDANCE
    # ==========================================================================
    desc_downloads_query = """
        MATCH (start:Model {name: $model_name})-[:USED_IN*1..]->(descendant:Model)
        WHERE start <> descendant
        WITH descendant, size([(m:Model)<-[:USED_IN]-(descendant) | m]) AS citation_count
        RETURN descendant.name AS name, citation_count, descendant.downloads AS downloads, descendant.task AS task, descendant.license AS license, descendant.likes AS likes, descendant.createdAt AS createdAt
        ORDER BY descendant.downloads DESC, descendant.name ASC
        LIMIT $limit
    """
    desc_cited_query = """
        MATCH (start:Model {name: $model_name})-[:USED_IN*1..]->(descendant:Model)
        WHERE start <> descendant
        WITH descendant, size([(m:Model)<-[:USED_IN]-(descendant) | m]) AS citation_count
        RETURN descendant.name AS name, citation_count, descendant.task AS task, descendant.downloads AS downloads, descendant.license AS license, descendant.likes AS likes, descendant.createdAt AS createdAt
        ORDER BY citation_count DESC, descendant.name ASC
        LIMIT $limit
    """
    try:
        params = {"model_name": model_name, "limit": num_highlights}
        desc_downloaded_list = gds.run_cypher(desc_downloads_query, params).to_dict('records')
        desc_cited_list = gds.run_cypher(desc_cited_query, params).to_dict('records')

        desc_unified_models = {}
        process_and_assign_badges(desc_unified_models, desc_cited_list, ['desc_cited_1', 'desc_cited_2'])
        process_and_assign_badges(desc_unified_models, desc_downloaded_list, ['desc_downloaded_1', 'desc_downloaded_2'])
        
        highlights["desc_unique_models"] = list(desc_unified_models.values())

    except Exception as e:
        print(f"Erreur lors de la recherche des descendants: {e}")

    # ==========================================================================
    # 2. GESTION DE L'ASCENDANCE
    # ==========================================================================
    asc_downloads_query = """
        MATCH (ascendant:Model)-[:USED_IN*1..]->(start:Model {name: $model_name})
        WHERE start <> ascendant
        WITH ascendant, size([(m:Model)<-[:USED_IN]-(ascendant) | m]) AS citation_count
        RETURN ascendant.name AS name, citation_count, ascendant.downloads AS downloads, ascendant.task AS task, 
        ascendant.license AS license, ascendant.likes AS likes, ascendant.createdAt AS createdAt
        ORDER BY ascendant.downloads DESC
        LIMIT 1 // On ne veut que LE plus téléchargé
    """
    asc_cited_query = """
        MATCH (ascendant:Model)-[:USED_IN*1..]->(start:Model {name: $model_name})
        WHERE start <> ascendant
        WITH ascendant, size([(m:Model)<-[:USED_IN]-(ascendant) | m]) AS citation_count
        RETURN ascendant.name AS name, citation_count, ascendant.downloads AS downloads, ascendant.task AS task, 
        ascendant.license AS license, ascendant.likes AS likes, ascendant.createdAt AS createdAt
        ORDER BY citation_count DESC
        LIMIT 1 // On ne veut que LE plus cité
    """

    foundation_query = """
        MATCH (foundation:Model)-[:USED_IN*1..]->(start:Model {name: $model_name})
        WHERE NOT EXISTS( (:Model)-[:USED_IN]->(foundation) )
        WITH foundation, size([(m:Model)<-[:USED_IN]-(foundation) | m]) AS citation_count
        RETURN DISTINCT foundation.name AS name, citation_count, foundation.downloads AS downloads, foundation.task AS task, 
        foundation.license AS license, foundation.likes AS likes, foundation.createdAt AS createdAt
        LIMIT $limit
    """

    try:
        params = {"model_name": model_name, "limit": num_highlights}
        asc_foundation_list = gds.run_cypher(foundation_query, params).to_dict('records')
        asc_downloaded_list = gds.run_cypher(asc_downloads_query, params).to_dict('records')
        asc_cited_list = gds.run_cypher(asc_cited_query, params).to_dict('records')

        asc_unified_models = {}
        # Ordre de priorité : Racine > Cité > Téléchargé
        process_and_assign_badges(asc_unified_models, asc_foundation_list, ['asc_foundation'] * num_highlights) # Le badge racine s'applique à tous
        process_and_assign_badges(asc_unified_models, asc_cited_list, ['asc_cited_1', 'asc_cited_2'])
        process_and_assign_badges(asc_unified_models, asc_downloaded_list, ['asc_downloaded_1', 'asc_downloaded_2'])

        highlights["asc_unique_models"] = list(asc_unified_models.values())

    except Exception as e:
        print(f"Erreur lors de la recherche des ascendants: {e}")

    return highlights


def create_node_data(node_props, label):
    """
    Construit un dictionnaire de données pour chaque noeud
    à afficher dans le graphe front-end.
    """
    base_data = {
        "id": node_props.get("name", "")
    }
    
    if label == "Author":
        return {
            **base_data,
            "label": node_props.get("type", "Unknown"),
            "followers": node_props.get("followers", 1)
        }
    elif label == "Model":
        licens_ =str(node_props.get("license", t("node.unknown", "fr"))).strip("[]")
        if licens_ =="\'other\'" or pd.isna(licens_) or licens_ =="nan":
            licens_ = t("node.other", "fr")
        
        tache = node_props.get("task", "")
        if tache =="unknown": 
            tache = t("node.unknown", "fr")
        return {
            **base_data,
            "label": "Modèle",
            "downloads": node_props.get("downloads", 1),
            "likes": node_props.get("likes", 0),
            "license": licens_,
            "createdAt": node_props.get("createdAt", "inconnue"),
            "createdAt_dataset": node_props.get("createdAt_dataset", "inconnue"),
            "task": tache,
            "author": node_props.get("author", ""),"dataset": node_props.get("dataset", ""),
                "ascendantsCount": node_props.get("ascendantsCount", 0),"descendantsCount": node_props.get("descendantsCount", 0),
                "citationCount": node_props.get("citationCount", 0), "distance":node_props.get("distance", 0)
        }
    else:  # Dataset or other
        return {
            **base_data,
            "label": "Dataset",
            "downloads": node_props.get("downloads", 1),
            "createdAt_dataset": node_props.get("createdAt_dataset", "inconnue")
        }

    return { "id": node_props['name'], "label": label, **node_props }