app.py

import os
import streamlit as st
import pandas as pd
from pypdf import PdfReader
from pyvis.network import Network

from knowledge_graph_maker import (
    GraphMaker, Ontology, Document, OpenAIClient
)

# ── Page setup ──────────────────────────────────────────────────────────────────
st.set_page_config(page_title="Knowledge Graph (OpenRouter)", layout="wide")
st.title("Knowledge Graph from Text/PDF — OpenRouter")
st.caption("Builds a knowledge graph with knowledge-graph-maker via OpenRouter. Paste text or upload a PDF; choose a model.")

# ── Secrets / env ───────────────────────────────────────────────────────────────
OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY", "")

# Preset OpenRouter models (you can add more)
OPENROUTER_MODELS = [
    "openai/gpt-oss-20b:free",
    "moonshotai/kimi-k2:free",
    "google/gemini-2.0-flash-exp:free",
    "google/gemma-3-27b-it:free",
]

# ── Sidebar controls ───────────────────────────────────────────────────────────
with st.sidebar:
    st.subheader("Model & Generation Settings")
    model_choice = st.selectbox("OpenRouter model", OPENROUTER_MODELS, index=0)
    custom_model = st.text_input("Custom model id (optional)", placeholder="e.g. meta-llama/llama-3.1-8b-instruct")
    temperature = st.slider("Temperature", 0.0, 1.0, 0.1, 0.05)
    top_p = st.slider("Top-p", 0.0, 1.0, 0.5, 0.05)

    st.markdown("### Ontology (labels)")
    labels_text = st.text_area(
        "Comma-separated labels",
        value="Person, Object, Event, Place, Document, Organisation, Action, Miscellanous",
        height=70,
    )
    relationships_text = st.text_input(
        "Relationships (comma-separated)",
        value="Relation between any pair of Entities",
    )

# ── Helpers ────────────────────────────────────────────────────────────────────
def parse_labels(text: str):
    return [lbl.strip() for lbl in text.split(",") if lbl.strip()] or [
        "Person", "Object", "Event", "Place", "Document", "Organisation", "Action", "Miscellanous"
    ]

def pdf_to_text(file) -> str:
    reader = PdfReader(file)
    parts = []
    for page in reader.pages:
        try:
            parts.append(page.extract_text() or "")
        except Exception:
            continue
    return "\n".join(parts)

def chunk_text(text: str, chars: int = 3500) -> list[Document]:
    docs = []
    for i in range(0, len(text), chars):
        chunk = text[i:i+chars].strip()
        if chunk:
            docs.append(Document(text=chunk, metadata={"chunk_id": i // chars}))
    return docs

def edges_to_pyvis(edges):
    # IMPORTANT: cdn_resources="in_line" prevents PyVis from creating a ./lib folder
    net = Network(
        height="700px",
        width="100%",
        bgcolor="#ffffff",
        font_color="#222222",
        notebook=False,
        directed=False,
        cdn_resources="in_line",
    )
    node_ids = {}
    for e in edges:
        n1 = f"{e.node_1.label}:{e.node_1.name}"
        n2 = f"{e.node_2.label}:{e.node_2.name}"
        if n1 not in node_ids:
            net.add_node(n1, label=e.node_1.name, title=e.node_1.label)
            node_ids[n1] = True
        if n2 not in node_ids:
            net.add_node(n2, label=e.node_2.name, title=e.node_2.label)
            node_ids[n2] = True
        net.add_edge(n1, n2, title=e.relationship or "", value=1)
    net.toggle_physics(True)
    return net

# ── Input tabs ─────────────────────────────────────────────────────────────────
tab_text, tab_pdf = st.tabs(["📝 Paste Text", "📄 Upload PDF"])
input_text = ""
with tab_text:
    input_text = st.text_area("Paste your text here", height=220, placeholder="Paste text…")
with tab_pdf:
    pdf_file = st.file_uploader("Upload a PDF", type=["pdf"])
    if pdf_file:
        input_text = pdf_to_text(pdf_file)

# ── Action ─────────────────────────────────────────────────────────────────────
if st.button("Generate Knowledge Graph", type="primary"):
    if not input_text.strip():
        st.warning("Please provide text or a PDF.")
        st.stop()
    if not OPENROUTER_API_KEY:
        st.error("OPENROUTER_API_KEY is not set in Space Secrets.")
        st.stop()

    # Route OpenAI SDK traffic through OpenRouter (OpenAI-compatible)
    os.environ["OPENAI_API_KEY"] = OPENROUTER_API_KEY
    os.environ["OPENAI_BASE_URL"] = "https://openrouter.ai/api/v1"
    # Optional attribution headers for OpenRouter analytics/ranking
    os.environ["OPENAI_DEFAULT_HEADERS"] = (
        '{"HTTP-Referer":"https://huggingface.co/spaces/blazingbunny/rahulnyk_knowledge_graph",'
        '"X-Title":"Knowledge Graph (OpenRouter)"}'
    )

    selected_model = custom_model.strip() if custom_model.strip() else model_choice

    # Ontology
    ontology = Ontology(
        labels=parse_labels(labels_text),
        relationships=[r.strip() for r in relationships_text.split(",") if r.strip()] or
                      ["Relation between any pair of Entities"],
    )

    st.info("Chunking input and building graph…")
    docs = chunk_text(input_text)

    # LLM client (OpenRouter via OpenAI client)
    llm = OpenAIClient(model=selected_model, temperature=temperature, top_p=top_p)

    gm = GraphMaker(ontology=ontology, llm_client=llm, verbose=False)
    edges = gm.from_documents(docs, delay_s_between=0)  # tweak delay for rate limits if needed

    st.success(f"Graph built with {len(edges)} edges.")

    # Show edge table
    df = pd.DataFrame([{
        "node_1_label": e.node_1.label, "node_1": e.node_1.name,
        "node_2_label": e.node_2.label, "node_2": e.node_2.name,
        "relationship": e.relationship
    } for e in edges])
    st.dataframe(df, use_container_width=True)

    # Render graph in-memory (no writes)
    net = edges_to_pyvis(edges)
    html = net.generate_html()      # <- avoids creating ./lib
    st.components.v1.html(html, height=750, scrolling=True)

st.markdown("---")
st.caption("Powered by knowledge-graph-maker via OpenRouter.")