Create app.py

app.py

@@ -0,0 +1,156 @@
+import os
+import io
+import tempfile
+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, GroqClient
+)
+
+st.set_page_config(page_title="Knowledge Graph Maker", layout="wide")
+
+st.title("Knowledge Graph from Text/PDF (Docker Space)")
+st.caption("Uses knowledge-graph-maker with OpenAI or Groq. Paste text or upload a PDF; view the interactive graph below.")
+
+# Choose LLM client based on available env vars
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
+GROQ_API_KEY = os.getenv("GROQ_API_KEY", "")
+
+with st.sidebar:
+    st.subheader("Model Settings")
+    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)
+    provider = st.radio("Provider", ["OpenAI", "Groq"], index=0 if OPENAI_API_KEY else 1 if GROQ_API_KEY else 0)
+    if provider == "OpenAI":
+        oai_model = st.text_input("OpenAI model", value="gpt-3.5-turbo")
+    else:
+        groq_model = st.text_input("Groq model", value="mixtral-8x7b-32768")
+
+    st.markdown("### Ontology (labels)")
+    default_labels = [
+        {"Person": "Person name without adjectives (may appear as name or pronoun)"},
+        {"Object": "Avoid the definite article 'the' in name"},
+        {"Event": "Events involving multiple people; no verbs like gives/leaves"},
+        "Place", "Document", "Organisation", "Action",
+        {"Miscellanous": "Important concept that fits none of the above"}
+    ]
+    labels_text = st.text_area("Labels (JSON or comma-separated)", value=", ".join(
+        [lbl if isinstance(lbl, str) else list(lbl.keys())[0] for lbl in default_labels]
+    ), height=80)
+    st.markdown("### Relationships focus")
+    relationships_text = st.text_input("Relationships (comma-separated)", value="Relation between any pair of Entities")
+
+def parse_labels(text):
+    # Allow simple "A, B, C" input; fall back to defaults above if empty
+    if not text.strip():
+        return [ "Person","Object","Event","Place","Document","Organisation","Action","Miscellanous" ]
+    return [lbl.strip() for lbl in text.split(",") if lbl.strip()]
+
+def split_pdf(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 build_graph_documents(text: str) -> list[Document]:
+    # Simple chunking: ~900-1000 tokens ≈ ~3000-4000 chars heuristic
+    # Adjust if needed.
+    CHARS = 3500
+    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):
+    net = Network(height="700px", width="100%", bgcolor="#ffffff", font_color="#222222", notebook=False, directed=False)
+    # Simple map to keep unique node IDs
+    node_ids = {}
+
+    def node_key(label, name): return f"{label}:{name}"
+
+    for e in edges:
+        n1 = node_key(e.node_1.label, e.node_1.name)
+        n2 = node_key(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
+
+        rel = e.relationship or ""
+        net.add_edge(n1, n2, title=rel, value=1)
+
+    net.toggle_physics(True)
+    return net
+
+# Input UI
+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 = split_pdf(pdf_file)
+
+if st.button("Generate Knowledge Graph", type="primary"):
+    if not input_text.strip():
+        st.warning("Please provide text or a PDF.")
+        st.stop()
+
+    # Prepare LLM client
+    if provider == "OpenAI":
+        if not OPENAI_API_KEY:
+            st.error("OPENAI_API_KEY is not set in the Space Secrets.")
+            st.stop()
+        llm = OpenAIClient(model=oai_model, temperature=temperature, top_p=top_p)
+    else:
+        if not GROQ_API_KEY:
+            st.error("GROQ_API_KEY is not set in the Space Secrets.")
+            st.stop()
+        llm = GroqClient(model=groq_model, temperature=temperature, top_p=top_p)
+
+    # 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… this may take a bit for longer texts.")
+
+    gm = GraphMaker(ontology=ontology, llm_client=llm, verbose=False)
+    docs = build_graph_documents(input_text)
+
+    edges = gm.from_documents(docs, delay_s_between=0)  # tune delay for rate limits
+    st.success(f"Graph built with {len(edges)} edges.")
+
+    # Show edges 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 with PyVis inside Streamlit
+    net = edges_to_pyvis(edges)
+    with tempfile.TemporaryDirectory() as td:
+        html_path = os.path.join(td, "graph.html")
+        net.save_graph(html_path)
+        html_content = open(html_path, "r", encoding="utf-8").read()
+        st.components.v1.html(html_content, height=750, scrolling=True)
+
+st.markdown("---")
+st.caption("Built with [knowledge-graph-maker](https://github.com/rahulnyk/knowledge_graph_maker).")