Finalise graph visualisation
This commit is contained in:
@@ -2,3 +2,4 @@
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dist/
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node_modules/
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src/data.json
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src/titles.json
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@@ -3,6 +3,7 @@ import ForceGraph2D from "react-force-graph-2d";
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import * as d3 from "d3-force-3d";
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import data from "./data.json";
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import titlesData from "./titles.json";
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function drawRoundedRect(ctx, x, y, width, height, radius) {
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const r = Math.min(radius, width / 2, height / 2);
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@@ -24,10 +25,13 @@ function buildGraph(data) {
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const nodes = [];
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const links = [];
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// Create a lookup map for quick access
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const titleMap = new Map(titlesData.map(t => [t.cluster_id, t.title]));
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data.claim_clusters.forEach((cluster) => {
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nodes.push({
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id: cluster.cluster_id,
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label: cluster.title || "Unnamed Claim Cluster",
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label: titleMap.get(cluster.cluster_id) || cluster.title || "Unnamed Claim Cluster",
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type: "claim_cluster",
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members: cluster.members
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});
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@@ -36,7 +40,7 @@ function buildGraph(data) {
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data.event_clusters.forEach((cluster) => {
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nodes.push({
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id: cluster.cluster_id,
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label: cluster.title || "Unnamed Event Cluster",
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label: titleMap.get(cluster.cluster_id) || cluster.title || "Unnamed Event Cluster",
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type: "event_cluster",
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members: cluster.members
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});
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@@ -124,13 +128,15 @@ export function App() {
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// Stronger repulsion
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fgRef.current.d3Force(
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"charge",
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d3.forceManyBody().strength(-3000)
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d3.forceManyBody().strength(-10000)
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);
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// Link distance
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fgRef.current.d3Force(
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"link",
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d3.forceLink().distance(240)
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d3.forceLink().distance(140)
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);
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// Collision based on dynamic box size
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@@ -138,7 +144,7 @@ export function App() {
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"collision",
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d3.forceCollide((node) => {
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const dims = node.__bckgDimensions;
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return dims ? Math.max(dims[0], dims[1]) / 2 + 16 : 20;
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return dims ? Math.max(dims[0], dims[1]) / 2 + 32 : 40;
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})
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);
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@@ -224,7 +230,7 @@ export function App() {
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<br />
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<input
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type="range"
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min="8"
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min="9"
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max="49"
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value={minGraphSize}
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onChange={(e) => setMinGraphSize(Number(e.target.value))}
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@@ -4,9 +4,14 @@ from openai import OpenAI
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from tqdm import tqdm
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from dotenv import load_dotenv
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import os
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from concurrent.futures import ThreadPoolExecutor, as_completed
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# -------------------------------
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# Load environment and OpenAI client
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# -------------------------------
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load_dotenv() # Load environment variables from .env file
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client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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# -------------------------------
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# CONFIG
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# -------------------------------
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@@ -14,7 +19,6 @@ INPUT_FILE = "../../data/clustered_output.json" # Your original JSON
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OUTPUT_FILE = "../../data/clustered_output2.json" # Output JSON file
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OPENAI_MODEL = "gpt-5-nano"
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# -------------------------------
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# Load data
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# -------------------------------
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@@ -62,8 +66,8 @@ for node in graph:
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queue.append(neighbor)
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components.append(component)
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# Filter components with size > 8
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large_components = [c for c in components if len(c) > 8 and len(c) < 50]
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# Filter components with size > 8 and < 50
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large_components = [c for c in components if 8 < len(c) < 50]
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print("Connected components (size > 8):", len(large_components))
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print("Total clusters in those components:", sum(len(c) for c in large_components))
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@@ -94,13 +98,14 @@ def generate_title(texts):
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"\n\nTitle:"
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)
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try:
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response = client.chat.completions.create(model=OPENAI_MODEL,
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response = client.chat.completions.create(
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model=OPENAI_MODEL,
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messages=[
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{"role": "system", "content": "You are a helpful assistant who creates short, meaningful titles."},
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{"role": "user", "content": prompt}
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])
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]
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)
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title = response.choices[0].message.content.strip()
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if title.lower().startswith("title:"):
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title = title[6:].strip()
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return title
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@@ -109,19 +114,31 @@ def generate_title(texts):
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return "Untitled Cluster"
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# -------------------------------
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# Generate title per cluster with progress bar
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# Wrapper for parallel execution
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# -------------------------------
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def generate_title_for_cluster(cluster_id):
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texts = extract_texts_for_cluster(cluster_id)
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title = generate_title(texts)
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return {"cluster_id": cluster_id, "title": title}
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# -------------------------------
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# Generate titles in parallel
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# -------------------------------
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clusters_in_large_components = [cid for comp in large_components for cid in comp]
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output = []
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print("\nGenerating GPT titles for clusters...")
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for cluster_id in tqdm(clusters_in_large_components, desc="Clusters", ncols=100):
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texts = extract_texts_for_cluster(cluster_id)
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title = generate_title(texts)
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output.append({
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"cluster_id": cluster_id,
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"title": title
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})
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print("\nGenerating GPT titles for clusters (parallel)...")
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with ThreadPoolExecutor(max_workers=10) as executor:
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future_to_cluster = {executor.submit(generate_title_for_cluster, cid): cid for cid in clusters_in_large_components}
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for future in tqdm(as_completed(future_to_cluster), total=len(clusters_in_large_components), desc="Clusters", ncols=100):
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try:
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result = future.result()
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output.append(result)
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except Exception as e:
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cid = future_to_cluster[future]
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print(f"Error processing cluster {cid}: {e}")
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output.append({"cluster_id": cid, "title": "Untitled Cluster"})
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# -------------------------------
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# Save JSON
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