import os
import json
import glob
import re
import csv
from collections import Counter, defaultdict
from pathlib import Path

# Fallback acronym mappings used only when canonical_map.json is absent
COMMON_ACRONYM_MAP = {
    "wdi": "World Development Indicators",
    "dhs": "Demographic and Health Survey",
    "lsms": "Living Standards Measurement Study",
    "pwt": "Penn World Table",
    "mics": "Multiple Indicator Cluster Survey",
    "wvs": "World Values Survey",
    "lfs": "Labour Force Survey",
    "hces": "Household Consumption and Expenditure Survey",
    "mxfls": "Mexican Family Life Survey",
    "psid": "Panel Study of Income Dynamics",
    "bhps": "British Household Panel Survey",
    "soep": "German Socio-Economic Panel",
    "hilda": "Household, Income and Labour Dynamics in Australia",
    "gsoep": "German Socio-Economic Panel Study",
    "ipums": "Integrated Public Use Microdata Series",
}


def load_canonical_map() -> dict:
    """
    Load the harmonization-produced canonical_map.json if available.
    Keys are normalized variant strings; values are the formal canonical names
    (e.g. 'Demographic and Health Survey (DHS)').
    Returns an empty dict if the file does not yet exist.
    """
    canonical_map_path = Path(__file__).parent / "canonical_map.json"
    if canonical_map_path.exists():
        with open(canonical_map_path, "r", encoding="utf-8") as f:
            mapping = json.load(f)
        print(f"  Loaded harmonization canonical map: {len(mapping)} variant -> canonical entries.")
        return mapping
    else:
        print("  canonical_map.json not found – falling back to basic acronym lookup.")
        return {}


def clean_name(name):
    """Normalize dataset name for string matching."""
    if not name:
        return ""
    s = name.lower().strip()
    s = re.sub(r'^(the|a|an|our)\s+', '', s)
    s = re.sub(r'[^\w\s]', ' ', s)
    s = re.sub(r'\s+', ' ', s).strip()
    return s


def clean_acronym(acronym):
    """Normalize acronym."""
    if not acronym:
        return ""
    s = acronym.upper().strip()
    s = re.sub(r'[^\w]', '', s)
    return s


def sanitize_author_id(name):
    """Generate a safe ID from an author's name."""
    s = name.strip()
    s = re.sub(r'[^\w\s-]', '', s)
    s = re.sub(r'[\s]+', '_', s)
    return s.lower()

def main():
    base_dir = Path(__file__).parent.parent
    standardized_base = Path("/Users/rafaelmacalaba/WBG/fetch_prwp/data/standardized_outputs")
    output_json_path = base_dir / "data" / "dashboard_data.json"
    output_csv_path = base_dir / "data" / "deduplicated_datasets.csv"
    graph_base = base_dir / "data" / "graph_database"

    # Load the harmonization canonical map (produced by run_harmonization.py)
    print("Step 0: Loading harmonization canonical map...")
    harmonization_map = load_canonical_map()

    print("Step 1: Reading all standardized JSON files...")
    all_json_files = glob.glob(str(standardized_base / "batch_*" / "*.json"))
    print(f"  Found {len(all_json_files)} standardized JSON files.")

    # Data structures to hold records
    papers = []

    # Named mentions: formal, identifiable dataset names → entity resolution + graph
    named_mentions = []
    # Descriptive mentions: category/type references → data practice breakdown
    descriptive_mentions = []
    # Vague mentions ("the data", "survey data")
    vague_mentions = []

    # Author structures
    all_authors = set()
    paper_to_authors = defaultdict(list)

    # Track mapping of acronym -> full names with frequencies (named only)
    acronym_to_names = defaultdict(Counter)

    for filepath in all_json_files:
        try:
            with open(filepath, "r", encoding="utf-8") as f:
                doc = json.load(f)
        except Exception as e:
            print(f"  [Error] Failed to read {os.path.basename(filepath)}: {e}")
            continue

        metadata = doc.get("metadata") or {}
        model_extractions = doc.get("model_extractions") or []

        # Parse paper metadata
        paper_id = metadata.get("id")
        title = metadata.get("display_title", "Untitled Document").strip()
        pdf_url = metadata.get("pdfurl", "").strip()
        
        # Resolve year from publication date
        doc_date = metadata.get("docdt") or metadata.get("last_modified_date") or ""
        year = "Unknown"
        match = re.search(r"\b(19\d{2}|20\d{2})\b", doc_date)
        if match:
            year = int(match.group(1))

        # Extract authors
        authors_dict = metadata.get("authors") or {}
        authors_list = []
        if isinstance(authors_dict, dict):
            for k, v in authors_dict.items():
                if isinstance(v, dict) and "author" in v:
                    authors_list.append(v["author"].strip())
                elif isinstance(v, str):
                    authors_list.append(v.strip())
        elif isinstance(authors_dict, list):
            authors_list = [a.get("author").strip() if isinstance(a, dict) else a.strip() for a in authors_dict]
            
        for author in authors_list:
            if author:
                all_authors.add(author)
                paper_to_authors[paper_id].append(author)
            
        authors_str = ", ".join(authors_list) if authors_list else "Unknown"

        # Track all mentions in this paper (any specificity) for the has_data flag
        paper_mention_count = 0

        for extraction in model_extractions:
            if extraction.get("classifier_skipped", False):
                continue

            page_num = extraction.get("page", 0) + 1
            datasets = extraction.get("datasets") or []

            for ds in datasets:
                mention = ds.get("mention_name", {}).get("text", "").strip()
                acronym = ds.get("acronym", {}).get("text", "").strip()
                producer = ds.get("producer", {}).get("text", "").strip()
                geography = ds.get("geography", {}).get("text", "").strip()
                typology = ds.get("typology_tag", {}).get("text", "").strip()
                specificity = (ds.get("specificity_tag") or {}).get("text", "").strip().lower()
                usage_context = ds.get("usage_context", {}).get("text", "").strip()
                is_used_val = ds.get("is_used", {}).get("text", "").strip()
                confidence = ds.get("mention_name", {}).get("confidence", 0.0)

                if not mention:
                    continue

                paper_mention_count += 1
                record = {
                    "paper_id": paper_id,
                    "paper_title": title,
                    "paper_year": year,
                    "page": page_num,
                    "mention": mention,
                    "acronym": acronym,
                    "producer": producer,
                    "geography": geography,
                    "typology": typology,
                    "specificity": specificity,
                    "usage_context": usage_context,
                    "is_used": is_used_val,
                    "confidence": confidence,
                }

                if specificity == "named":
                    named_mentions.append(record)
                    # Build acronym co-occurrence map for fallback canonical resolution
                    cleaned_ac = clean_acronym(acronym)
                    if cleaned_ac and len(mention) > len(acronym) and len(mention) > 5:
                        acronym_to_names[cleaned_ac][mention] += 1
                elif specificity == "descriptive":
                    descriptive_mentions.append(record)
                elif specificity == "vague":
                    vague_mentions.append(record)

        papers.append({
            "id": paper_id,
            "title": title,
            "year": year,
            "authors": authors_str,
            "pdf_url": pdf_url,
            # has_data = True if the paper has any named or descriptive mention (not vague)
            "has_data": paper_mention_count > 0,
            "mention_count": paper_mention_count,
        })

    print(f"  Named mentions: {len(named_mentions)} | Descriptive: {len(descriptive_mentions)} | Vague: {len(vague_mentions)}")
    print("Step 2: Resolving canonical names via harmonization map (named mentions only)...")

    # Build a fallback dynamic acronym map from co-occurrence patterns in corpus
    dynamic_acronym_map = {}
    for ac, name_counts in acronym_to_names.items():
        best_name = name_counts.most_common(1)[0][0]
        dynamic_acronym_map[ac.lower()] = best_name
    # Merge static fallback entries
    for ac, name in COMMON_ACRONYM_MAP.items():
        if ac.lower() not in dynamic_acronym_map:
            dynamic_acronym_map[ac.lower()] = name

    harmonization_hits = 0
    fallback_hits = 0

    # Map NAMED mentions to canonical names
    # Priority: (1) harmonization_map, (2) dynamic/static acronym map, (3) most-frequent raw string
    raw_to_canonical = {}
    cleaned_groups = defaultdict(list)
    for m in named_mentions:
        cleaned = clean_name(m["mention"])
        cleaned_groups[cleaned].append(m)

    for cleaned, group in cleaned_groups.items():
        raw_names = [item["mention"] for item in group]
        most_common_raw = Counter(raw_names).most_common(1)[0][0]
        acronyms = [clean_acronym(item["acronym"]).lower() for item in group if item["acronym"]]
        best_acronym = Counter(acronyms).most_common(1)[0][0] if acronyms else ""

        canonical_name = None
        for lookup_key in [cleaned, clean_name(most_common_raw), best_acronym.lower()]:
            if lookup_key and lookup_key in harmonization_map:
                canonical_name = harmonization_map[lookup_key]
                harmonization_hits += 1
                break

        if canonical_name is None:
            if cleaned in dynamic_acronym_map:
                canonical_name = dynamic_acronym_map[cleaned]
                fallback_hits += 1
            elif best_acronym and best_acronym in dynamic_acronym_map:
                canonical_name = dynamic_acronym_map[best_acronym]
                fallback_hits += 1

        if canonical_name is None:
            canonical_name = most_common_raw

        for item in group:
            raw_to_canonical[item["mention"]] = canonical_name

    print(f"  Harmonization map resolved: {harmonization_hits} groups")
    print(f"  Fallback acronym map resolved: {fallback_hits} groups")

    # Apply canonical mapping to named mentions only
    for m in named_mentions:
        m["canonical"] = raw_to_canonical[m["mention"]]
        
    def standardize_typology(typ: str) -> str:
        typ = typ.lower()
        if "survey" in typ or "microdata" in typ:
            return "Survey"
        elif "admin" in typ or "registry" in typ or "records" in typ:
            return "Administrative Data"
        elif "census" in typ:
            return "Census"
        elif "satellite" in typ or "spatial" in typ or "remote" in typ:
            return "Geospatial/Satellite"
        elif "indicators" in typ or "macro" in typ or "aggregate" in typ:
            return "Macro/Aggregate Indicators"
        elif not typ:
            return "Unknown"
        else:
            return typ.title()

    for m in named_mentions:
        m["typology"] = standardize_typology(m["typology"])
    for m in descriptive_mentions:
        m["typology"] = standardize_typology(m["typology"])
    for m in vague_mentions:
        m["typology"] = standardize_typology(m["typology"])

    print("Step 3: Calculating frequencies and trends...")

    # Calculate frequencies per canonical dataset
    dataset_papers = defaultdict(set)
    dataset_mentions_count = Counter()
    dataset_attributes = defaultdict(lambda: {
        "acronyms": Counter(),
        "producers": Counter(),
        "geographies": Counter(),
        "typologies": Counter(),
    })

    # Group papers details by dataset
    paper_lookup = {p["id"]: p for p in papers}

    for m in named_mentions:
        canonical = m["canonical"]
        paper_id = m["paper_id"]

        dataset_papers[canonical].add(paper_id)
        dataset_mentions_count[canonical] += 1

        if m["acronym"]:
            dataset_attributes[canonical]["acronyms"][m["acronym"]] += 1
        if m["producer"]:
            dataset_attributes[canonical]["producers"][m["producer"]] += 1
        if m["geography"]:
            dataset_attributes[canonical]["geographies"][m["geography"]] += 1
        if m["typology"]:
            dataset_attributes[canonical]["typologies"][m["typology"]] += 1

    # Pre-group named mentions by canonical for O(M) paper-page lookup
    mentions_by_canonical = defaultdict(list)
    for m in named_mentions:
        mentions_by_canonical[m["canonical"]].append(m)

    # Format the top datasets list
    top_datasets_list = []
    for canonical, paper_ids in dataset_papers.items():
        df = len(paper_ids)
        mf = dataset_mentions_count[canonical]
        
        attrs = dataset_attributes[canonical]
        best_acronym = attrs["acronyms"].most_common(1)[0][0] if attrs["acronyms"] else ""
        best_producer = attrs["producers"].most_common(1)[0][0] if attrs["producers"] else "Unknown"
        best_geography = attrs["geographies"].most_common(1)[0][0] if attrs["geographies"] else "Global / Multiple"
        best_typology = attrs["typologies"].most_common(1)[0][0] if attrs["typologies"] else "Unknown"

        # Collect paper references (uses pre-grouped O(M) lists)
        referencing_papers = []
        paper_pages = defaultdict(list)
        for m in mentions_by_canonical[canonical]:
            paper_pages[m["paper_id"]].append(m["page"])

        for p_id in paper_ids:
            p_info = paper_lookup[p_id]
            referencing_papers.append({
                "id": p_id,
                "title": p_info["title"],
                "year": p_info["year"],
                "authors": p_info["authors"],
                "pdf_url": p_info.get("pdf_url", ""),
                "pages": sorted(list(set(paper_pages[p_id])))
            })
        
        referencing_papers.sort(key=lambda x: x["year"] if isinstance(x["year"], int) else 0, reverse=True)

        # Collect all raw variants and their frequencies that resolved to this canonical
        variant_counter = Counter(m["mention"] for m in mentions_by_canonical[canonical])
        sorted_variants = [{"name": name, "count": count} for name, count in variant_counter.most_common()]

        top_datasets_list.append({
            "canonical_name": canonical,
            "acronym": best_acronym,
            "typology": best_typology,
            "producer": best_producer,
            "geography": best_geography,
            "document_frequency": df,
            "mention_frequency": mf,
            "variants": sorted_variants,
            "papers": referencing_papers
        })

    top_datasets_list.sort(key=lambda x: x["document_frequency"], reverse=True)

    # Calculate Annual Trends
    papers_by_year = defaultdict(list)
    for p in papers:
        if isinstance(p["year"], int):
            papers_by_year[p["year"]].append(p)
            
    annual_trends = []
    valid_years = sorted([y for y in papers_by_year.keys() if 2000 <= y <= 2026])
    
    for y in valid_years:
        year_papers = papers_by_year[y]
        total = len(year_papers)
        with_data = sum(1 for p in year_papers if p["has_data"])
        pct = (with_data / total * 100) if total > 0 else 0
        annual_trends.append({
            "year": y,
            "total_papers": total,
            "papers_with_data": with_data,
            "percentage": round(pct, 1)
        })

    # ── Named dataset distributions (for top-datasets chart and typology donut) ──
    typology_counts = Counter()
    producer_counts = Counter()
    geography_counts = Counter()

    for ds in top_datasets_list:
        df = ds["document_frequency"]
        typology_counts[ds["typology"]] += df
        if ds["producer"] != "Unknown":
            producer_counts[ds["producer"]] += df
        if ds["geography"] != "Global / Multiple":
            geography_counts[ds["geography"]] += df

    # ── Data Practice Signal breakdown (descriptive and vague) ──
    practice_papers = defaultdict(set)
    practice_mentions = Counter()
    for m in descriptive_mentions + vague_mentions:
        spec = m["specificity"].lower()
        key = (m["typology"], spec)
        practice_papers[key].add(m["paper_id"])
        practice_mentions[key] += 1

    descriptive_breakdown = [
        {
            "typology": key[0],
            "specificity": key[1].upper(),
            "document_frequency": len(paper_ids),
            "mention_frequency": practice_mentions[key],
        }
        for key, paper_ids in sorted(
            practice_papers.items(),
            key=lambda x: len(x[1]),
            reverse=True
        )
    ]

    papers_with_named = len({m["paper_id"] for m in named_mentions})
    papers_with_descriptive_only = len(
        {m["paper_id"] for m in descriptive_mentions}
        - {m["paper_id"] for m in named_mentions}
    )

    # ── Specificity, Usage Context, and Is Used distributions ──
    all_mentions = named_mentions + descriptive_mentions + vague_mentions
    specificity_counts = Counter()
    usage_context_counts = Counter()
    is_used_counts = Counter()

    for m in all_mentions:
        # 1. Specificity (Named vs Descriptive vs Vague)
        spec = m.get("specificity", "unknown").strip().lower()
        if spec == "named":
            specificity_counts["Named"] += 1
        elif spec == "descriptive":
            specificity_counts["Descriptive"] += 1
        elif spec == "vague":
            specificity_counts["Vague"] += 1
        else:
            specificity_counts[spec.capitalize()] += 1

        # 2. Usage Context
        ctx = m.get("usage_context")
        if not ctx:
            usage_context_counts["Unknown"] += 1
        elif ctx.lower() == "primary":
            usage_context_counts["Primary Use"] += 1
        elif ctx.lower() == "supporting":
            usage_context_counts["Supporting Use"] += 1
        elif ctx.lower() == "background":
            usage_context_counts["Background / Citation"] += 1
        else:
            usage_context_counts[ctx.replace("_", " ").title()] += 1

        # 3. Is Used
        used_val = m.get("is_used")
        if not used_val:
            is_used_counts["Unknown"] += 1
        elif used_val == "True":
            is_used_counts["Used in Analysis"] += 1
        elif used_val == "False":
            is_used_counts["Not Used (Citation Only)"] += 1
        else:
            is_used_counts[used_val.replace("_", " ").title()] += 1

    # ── Build final dashboard payload ──
    papers_with_data = sum(1 for p in papers if p["has_data"])
    payload = {
        "summary": {
            "total_papers": len(papers),
            "papers_with_data": papers_with_data,
            "data_adoption_rate": round(papers_with_data / len(papers) * 100, 1),
            # Named-only counts (for dataset entity explorer)
            "named_mentions": len(named_mentions),
            "unique_canonical_datasets": len(top_datasets_list),
            "papers_with_named_datasets": papers_with_named,
            # Descriptive-only counts (for data practice section)
            "descriptive_mentions": len(descriptive_mentions),
            "papers_with_descriptive_only": papers_with_descriptive_only,
            # Vague counts
            "vague_mentions": len(vague_mentions),
        },
        "annual_trends": annual_trends,
        # Named dataset typology distribution
        "typology_distribution": [
            {"name": name, "value": val} for name, val in typology_counts.most_common(10)
        ],
        "specificity_distribution": [
            {"name": name, "value": val} for name, val in specificity_counts.most_common()
        ],
        "usage_context_distribution": [
            {"name": name, "value": val} for name, val in usage_context_counts.most_common()
        ],
        "is_used_distribution": [
            {"name": name, "value": val} for name, val in is_used_counts.most_common()
        ],
        "top_producers": [
            {"name": name, "value": val} for name, val in producer_counts.most_common(10)
        ],
        "top_geographies": [
            {"name": name, "value": val} for name, val in geography_counts.most_common(10)
        ],
        # Named dataset entities (entity explorer + knowledge graph)
        "datasets": top_datasets_list,
        # Descriptive mention breakdown (data-practice signal)
        "descriptive_breakdown": descriptive_breakdown,
    }

    # Save to data/dashboard_data.json
    print(f"Step 4: Writing output dashboard JSON and JS...")
    os.makedirs(os.path.dirname(output_json_path), exist_ok=True)
    with open(output_json_path, "w", encoding="utf-8") as f:
        json.dump(payload, f, indent=2, ensure_ascii=False)
    print(f"  Saved dashboard JSON to: {output_json_path}")

    # Also save to data/dashboard_data.js for visual dashboard import
    output_js_path = base_dir / "data" / "dashboard_data.js"
    with open(output_js_path, "w", encoding="utf-8") as f:
        f.write("const DASHBOARD_DATA = ")
        json.dump(payload, f, ensure_ascii=False)
        f.write(";")
    print(f"  Saved dashboard JS to: {output_js_path}")

    # Export flat deduplicated mentions CSV
    print(f"Step 5: Exporting deduplicated datasets CSV...")
    with open(output_csv_path, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow([
            "canonical_name", "acronym", "typology", "producer", 
            "geography", "document_frequency", "mention_frequency"
        ])
        for ds in top_datasets_list:
            writer.writerow([
                ds["canonical_name"], ds["acronym"], ds["typology"], 
                ds["producer"], ds["geography"], ds["document_frequency"], ds["mention_frequency"]
            ])
    print(f"  Saved CSV to: {output_csv_path}")

    print("Step 6: Generating Graph Database Neo4j Import CSVs...")
    os.makedirs(graph_base, exist_ok=True)

    # 6.1 Nodes: Papers
    with open(graph_base / "nodes_papers.csv", "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["id", "title", "year"])
        for p in papers:
            writer.writerow([p["id"], p["title"], p["year"]])
            
    # 6.2 Nodes: Datasets
    # Assign each canonical dataset a unique slug/ID
    dataset_slugs = {}
    with open(graph_base / "nodes_datasets.csv", "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["id", "name", "acronym", "typology", "producer", "geography"])
        for i, ds in enumerate(top_datasets_list):
            slug = sanitize_author_id(ds["canonical_name"])
            # Ensure unique ID
            if slug in dataset_slugs.values():
                slug = f"{slug}_{i}"
            dataset_slugs[ds["canonical_name"]] = slug
            writer.writerow([
                slug, ds["canonical_name"], ds["acronym"], 
                ds["typology"], ds["producer"], ds["geography"]
            ])

    # 6.3 Nodes: Authors
    author_ids = {}
    with open(graph_base / "nodes_authors.csv", "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["id", "name"])
        for i, author in enumerate(sorted(all_authors)):
            a_id = sanitize_author_id(author)
            if a_id in author_ids.values():
                a_id = f"{a_id}_{i}"
            author_ids[author] = a_id
            writer.writerow([a_id, author])

    # 6.4 Edges: Authored (Author -> Paper)
    with open(graph_base / "edges_authored.csv", "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["author_id", "paper_id"])
        for paper_id, authors_list in paper_to_authors.items():
            for author in authors_list:
                writer.writerow([author_ids[author], paper_id])

    # 6.5 Edges: Mentions (Paper -> Dataset)
    # Collect relationship properties
    # A paper can mention a dataset multiple times on different pages
    paper_dataset_edges = defaultdict(lambda: {
        "pages": set(),
        "contexts": set(),
        "confidences": []
    })

    for m in named_mentions:
        edge_key = (m["paper_id"], m["canonical"])
        paper_dataset_edges[edge_key]["pages"].add(m["page"])
        if m["usage_context"]:
            paper_dataset_edges[edge_key]["contexts"].add(m["usage_context"])
        paper_dataset_edges[edge_key]["confidences"].append(m["confidence"])

    with open(graph_base / "edges_mentions.csv", "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["paper_id", "dataset_id", "pages", "context", "confidence"])
        for (paper_id, canonical), data in paper_dataset_edges.items():
            dataset_id = dataset_slugs[canonical]
            pages_str = ";".join(map(str, sorted(list(data["pages"]))))
            context = "|".join(data["contexts"]) if data["contexts"] else "Unknown"
            avg_conf = round(sum(data["confidences"]) / len(data["confidences"]), 3) if data["confidences"] else 0.0
            writer.writerow([paper_id, dataset_id, pages_str, context, avg_conf])

    # Create README_GRAPH.md
    print("Step 7: Creating Cypher Import Guide...")
    with open(graph_base / "README_GRAPH.md", "w", encoding="utf-8") as f:
        f.write("""# Neo4j Graph Database Import Guide

This folder contains Neo4j-import-ready CSV files containing:
* **Nodes**: Papers, Authors, and deduplicated Datasets.
* **Relationships**: Authorship (`:AUTHORED`) and dataset citations (`:MENTIONS`).

---

## CSV File List

1. **`nodes_papers.csv`**: Contains Policy Research Working Papers.
2. **`nodes_datasets.csv`**: Contains canonicalized datasets.
3. **`nodes_authors.csv`**: Contains unique authors.
4. **`edges_authored.csv`**: Maps Authors to Papers.
5. **`edges_mentions.csv`**: Maps Papers to Datasets with page numbers, context, and model confidence scores.

---

## Import Cypher Queries

To import these files into your Neo4j instance, place the CSV files in your Neo4j project's `import/` directory, open the Neo4j Browser, and execute the following queries:

### 1. Create Constraints
```cypher
CREATE CONSTRAINT UNIQUE_paper FOR (p:Paper) REQUIRE p.id IS UNIQUE;
CREATE CONSTRAINT UNIQUE_dataset FOR (d:Dataset) REQUIRE d.id IS UNIQUE;
CREATE CONSTRAINT UNIQUE_author FOR (a:Author) REQUIRE a.id IS UNIQUE;
```

### 2. Load Nodes
```cypher
// Load Papers
LOAD CSV WITH HEADERS FROM 'file:///nodes_papers.csv' AS row
MERGE (p:Paper {id: row.id})
SET p.title = row.title,
    p.year = toInteger(row.year);

// Load Datasets
LOAD CSV WITH HEADERS FROM 'file:///nodes_datasets.csv' AS row
MERGE (d:Dataset {id: row.id})
SET d.name = row.name,
    d.acronym = row.acronym,
    d.typology = row.typology,
    d.producer = row.producer,
    d.geography = row.geography;

// Load Authors
LOAD CSV WITH HEADERS FROM 'file:///nodes_authors.csv' AS row
MERGE (a:Author {id: row.id})
SET a.name = row.name;
```

### 3. Load Relationships
```cypher
// Load AUTHORED
LOAD CSV WITH HEADERS FROM 'file:///edges_authored.csv' AS row
MATCH (a:Author {id: row.author_id})
MATCH (p:Paper {id: row.paper_id})
MERGE (a)-[:AUTHORED]->(p);

// Load MENTIONS
LOAD CSV WITH HEADERS FROM 'file:///edges_mentions.csv' AS row
MATCH (p:Paper {id: row.paper_id})
MATCH (d:Dataset {id: row.dataset_id})
MERGE (p)-[:MENTIONS {
    pages: split(row.pages, ';'),
    context: row.context,
    confidence: toFloat(row.confidence)
}]->(d);
```
""")

    print(f"  Saved Cypher guide to: {graph_base / 'README_GRAPH.md'}")
    print("\nAll pipeline files generated successfully!")

if __name__ == "__main__":
    main()