Graphs.py

# -*- coding: utf-8 -*-
"""Learn with Knowledge Graphs.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/16UX6wbUmaLG6YBJKzH5YouNYYnw2mL8H
"""

# app.py
import streamlit as st
import wikipediaapi
import requests, json
import networkx as nx
import matplotlib.pyplot as plt
from neo4j import GraphDatabase

# ---------------------------
# CONFIGURATION
# ---------------------------
# API Key for Perplexity
PPLX_API_KEY = "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"

# Optional Neo4j credentials (leave empty if not using Neo4j)
NEO4J_URI = "neo4j+s://1a780c1e.databases.neo4j.io"
NEO4J_USER = "neo4j"
NEO4J_PASSWORD = "Xaabk9z1r5J-DPK6JPOH5QuOHL_MrTeFytx2c4sxjN4"

driver = None
if NEO4J_URI and NEO4J_USER and NEO4J_PASSWORD:
    driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD))


# ---------------------------
# FUNCTIONS
# ---------------------------
def perplexity_chat(prompt, model="sonar-medium-online"):
    url = "https://api.perplexity.ai/chat/completions"
    headers = {
        "Authorization": f"Bearer {PPLX_API_KEY}",
        "Content-Type": "application/json",
    }
    data = {
        "model": model,
        "messages": [{"role": "user", "content": prompt}],
        "temperature": 0,
    }
    resp = requests.post(url, headers=headers, data=json.dumps(data))
    if resp.status_code != 200:
        return f"❌ Error {resp.status_code}: {resp.text}"
    return resp.json()["choices"][0]["message"]["content"]


def extract_triples_from_chunk(text, max_triples=5):
    prompt = f"""Extract up to {max_triples} subject-predicate-object triples
from the text below. Return only triples in the format (subject, predicate, object).

Text: {text}"""

    content = perplexity_chat(prompt)
    triples = []
    for line in content.splitlines():
        line = line.strip(" ()[]{}")
        if not line:
            continue
        parts = [p.strip() for p in line.split(",")]
        if len(parts) == 3:
            triples.append(tuple(parts))
    return triples


def build_kg_from_wiki_title(title, lang="en", chunk_chars=800, max_triples_per_chunk=5):
    wiki = wikipediaapi.Wikipedia(lang, user_agent = "MyKGApp/1.0 (contact: your_email@example.com)")
    page = wiki.page(title)
    if not page.exists():
        return []

    text = page.text
    chunks = [text[i:i+chunk_chars] for i in range(0, len(text), chunk_chars)]

    triples = []
    for chunk in chunks:
        chunk_triples = extract_triples_from_chunk(chunk, max_triples=max_triples_per_chunk)
        triples.extend(chunk_triples)

    return triples


def insert_triple(tx, subject, predicate, obj):
    tx.run(
        """
        MERGE (s:Entity {name: $subject})
        MERGE (o:Entity {name: $object})
        MERGE (s)-[:RELATION {type: $predicate}]->(o)
        """,
        subject=subject, predicate=predicate, object=obj
    )

def insert_triples(triples):
    if not driver:
        return
    with driver.session() as session:
        for s, p, o in triples:
            session.execute_write(insert_triple, s, p, o)


def answer_with_kg(question, triples, top_k=10, model="sonar-medium-online"):
    context_triples = triples[:top_k]
    context_str = "\n".join([f"({s}, {p}, {o})" for s, p, o in context_triples])

    prompt = f"""
    You are a QA assistant.
    Use the following knowledge graph triples as context to answer the question.

    Knowledge Graph Triples:
    {context_str}

    Question: {question}

    Answer in a clear, concise way. If you don't find enough info in triples,
    say 'Not found in knowledge graph'.
    """
    return perplexity_chat(prompt, model=model)


# ---------------------------
# STREAMLIT APP
# ---------------------------
st.title("📚 Knowledge Graph Chatbot (Wikipedia + Perplexity)")

# Input for Wikipedia Title
title = st.text_input("Enter a Wikipedia Title (e.g., Harry Potter):")

if title:
    st.write(f"🔍 Building Knowledge Graph for: **{title}** ...")
    triples = build_kg_from_wiki_title(title)

    if triples:
        st.success(f"Extracted {len(triples)} triples ✅")

        # Save in Neo4j if configured
        if driver:
            insert_triples(triples)
            st.info("📡 Triples also stored in Neo4j.")

        # Show sample triples
        st.subheader("Sample Triples")
        st.json(triples[:10])

        # Visualization inside Streamlit
        st.subheader("Graph Visualization")
        G = nx.DiGraph()
        for s, p, o in triples[:30]:
            G.add_edge(s, o, label=p)

        plt.figure(figsize=(12, 8))
        pos = nx.spring_layout(G, k=0.5)
        nx.draw(G, pos, with_labels=True, node_size=2500, node_color="lightblue",
                font_size=10, font_weight="bold", arrows=True)
        edge_labels = nx.get_edge_attributes(G, 'label')
        nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=8)
        st.pyplot(plt)

        # Chat interface
        st.subheader("💬 Ask Questions")
        user_question = st.text_input("Your question:")
        if user_question:
            answer = answer_with_kg(user_question, triples)
            st.write("🤖", answer)
    else:
        st.error("Page not found or no triples extracted.")