Initial commit

DockerFile.txt

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+# Use official Python 3.11 slim image
+FROM python:3.11-slim
+
+# Set working directory inside container
+WORKDIR /app
+
+# Copy requirements first (for caching)
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app code into container
+COPY Graphs.py .
+
+# Expose the default Streamlit port
+EXPOSE 8501
+
+# Run Streamlit app
+CMD ["streamlit", "run", "Graphs.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.headless=true"]

Graphs.py

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+# -*- 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 = st.secrets.get("PPLX_API_KEY", "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")
+
+# Optional Neo4j credentials (leave empty if not using Neo4j)
+NEO4J_URI = st.secrets.get("NEO4J_URI", "neo4j+s://1a780c1e.databases.neo4j.io")
+NEO4J_USER = st.secrets.get("NEO4J_USER", "neo4j")
+NEO4J_PASSWORD = st.secrets.get("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)
+    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.")
+

requirements.txt.txt

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+streamlit==1.36.0
+wikipedia-api==0.6.0
+requests==2.32.3
+neo4j==5.22.0
+networkx==3.3
+matplotlib==3.9.2