app.py

from fastapi import FastAPI
from fastapi.responses import JSONResponse, HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
import pandas as pd
import networkx as nx
import plotly.graph_objects as go
import re
import os

# ---------- CONFIG ----------
DATA_FILE = "База философских концептов - База философских концептов.csv"

# ---------- APP SETUP ----------
app = FastAPI(
    title="Philosophy Knowledge Graph",
    description="Онтологическая база философских концептов — Knowledge GraphRAG",
    version="3.3.0",
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# ---------- LOAD DATA ----------
if not os.path.exists(DATA_FILE):
    raise FileNotFoundError(f"Файл {DATA_FILE} не найден в корне проекта.")

df = pd.read_csv(DATA_FILE)
df = df.fillna("")

# ---------- BUILD GRAPH ----------
G = nx.DiGraph()

for _, row in df.iterrows():
    G.add_node(
        row["concept"],
        definition=row["definition"],
        needs=row["needs_level"],
        tech=row["tech_problem"]
    )

# регулярка для формата: «A» → «B»
for _, row in df.iterrows():
    relations = re.findall(r'«([^»]+)»\s*[↔→]\s*«([^»]+)»', str(row["relations"]))
    for a, b in relations:
        if a in G.nodes and b in G.nodes:
            G.add_edge(a, b, label=row["tech_problem"])

# Цвета Маслоу
color_map = {
    "Физиологические": "#f4a261",
    "Безопасность": "#2a9d8f",
    "Принадлежность": "#e9c46a",
    "Самоуважение": "#264653",
    "Самореализация": "#e76f51"
}

# ---------- PRECOMPUTE LAYOUT ----------
# БЫСТРЫЙ, ЛЁГКИЙ, НЕ ТРЕБУЕТ SCIPY
POS = nx.spring_layout(G, k=0.7, iterations=30, seed=42)

# ---------- API ----------
@app.get("/")
def root():
    return {"message": "Philosophy Knowledge Graph API — работает!"}


@app.get("/concepts")
def get_concepts():
    return JSONResponse([
        {"concept": n, **G.nodes[n]}
        for n in G.nodes()
    ])


@app.get("/relations")
def get_relations():
    return JSONResponse([
        {"source": u, "target": v, "label": d.get("label", "")}
        for u, v, d in G.edges(data=True)
    ])


@app.get("/jsonld")
def get_jsonld():
    jsonld = {
        "@context": {
            "concept": "http://example.org/concept",
            "definition": "http://example.org/definition",
            "relation": "http://example.org/relation"
        },
        "@graph": []
    }
    for node, data in G.nodes(data=True):
        jsonld["@graph"].append({
            "@id": f"http://example.org/{node.replace(' ', '_')}",
            "concept": node,
            "definition": data.get("definition", ""),
            "tech_problem": data.get("tech", ""),
            "needs_level": data.get("needs", "")
        })
    return JSONResponse(jsonld)


# ---------- INTERACTIVE GRAPH ----------
@app.get("/graph", response_class=HTMLResponse)
def get_graph():
    pos = POS

    # --- Рёбра ---
    edge_x, edge_y = [], []
    for u, v in G.edges():
        x0, y0 = pos[u]
        x1, y1 = pos[v]
        edge_x.extend([x0, x1, None])
        edge_y.extend([y0, y1, None])

    edge_trace = go.Scatter(
        x=edge_x,
        y=edge_y,
        mode="lines",
        line=dict(width=0.5, color="#888"),
        hoverinfo="none",
    )

    # --- Узлы ---
    node_x, node_y, node_color, node_text = [], [], [], []

    for node, data in G.nodes(data=True):
        x, y = pos[node]
        node_x.append(x)
        node_y.append(y)
        node_color.append(color_map.get(data["needs"], "#8d99ae"))
        node_text.append(
            f"<b>{node}</b><br>{data['definition']}<br><i>{data['tech']}</i>"
        )

    node_trace = go.Scatter(
        x=node_x,
        y=node_y,
        mode="markers",
        hoverinfo="text",
        text=node_text,
        marker=dict(
            size=12,
            color=node_color,
            line=dict(width=1)
        ),
    )

    fig = go.Figure(
        data=[edge_trace, node_trace],
        layout=go.Layout(
            title="Онтологическая база философских концептов",
            titlefont=dict(size=20),
            showlegend=False,
            hovermode="closest",
            margin=dict(b=0, l=0, r=0, t=40),
            xaxis=dict(showgrid=False, zeroline=False, visible=False),
            yaxis=dict(showgrid=False, zeroline=False, visible=False),
        )
    )

    return HTMLResponse(fig.to_html(full_html=False))


# ---------- RUN ----------
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)