diff --git a/latentscope/scripts/cluster.py b/latentscope/scripts/cluster.py index b9dcdb3..52f26ff 100644 --- a/latentscope/scripts/cluster.py +++ b/latentscope/scripts/cluster.py @@ -100,9 +100,11 @@ def clusterer(dataset_id, umap_id, samples, min_samples): # plot a convex hull around each cluster hulls = [] for label in non_noise_labels: - points = umap_embeddings[cluster_labels == label] + indices = np.where(cluster_labels == label)[0] + points = umap_embeddings[indices] + # points = umap_embeddings[cluster_labels == label] hull = ConvexHull(points) - hull_list = hull.vertices.tolist() + hull_list = [indices[s] for s in hull.vertices.tolist()] hulls.append(hull_list) for simplex in hull.simplices: plt.plot(points[simplex, 0], points[simplex, 1], 'k-') @@ -131,7 +133,7 @@ def clusterer(dataset_id, umap_id, samples, min_samples): for cluster, indices in cluster_indices.items(): label = f"Cluster {cluster}" description = f"This is cluster {cluster} with {len(indices)} items." - new_row = pd.DataFrame({'label': [label], 'description': [description], 'indices': [list(indices)], 'hulls': [hulls[cluster]]}) + new_row = pd.DataFrame({'label': [label], 'description': [description], 'indices': [list(indices)], 'hull': [hulls[cluster]]}) slides_df = pd.concat([slides_df, new_row], ignore_index=True) # write the df to parquet diff --git a/latentscope/server/search.py b/latentscope/server/search.py index 653b2e5..c384142 100644 --- a/latentscope/server/search.py +++ b/latentscope/server/search.py @@ -1,5 +1,5 @@ import os -import sys +import json import numpy as np from flask import Blueprint, jsonify, request @@ -11,7 +11,7 @@ # in memory cache of dataset metadata, embeddings, models and tokenizers DATASETS = {} -MODELS = {} +EMBEDDINGS = {} """ Returns nearest neighbors for a given query string @@ -20,29 +20,33 @@ @search_bp.route('/nn', methods=['GET']) def nn(): dataset = request.args.get('dataset') - model_id = request.args.get('model') + embedding_id = request.args.get('embedding_id') num = 150 - if model_id not in MODELS: - print("loading model", model_id) + if embedding_id not in EMBEDDINGS: + print("loading model", embedding_id) + with open(os.path.join(DATA_DIR, dataset, "embeddings", embedding_id + ".json"), 'r') as f: + metadata = json.load(f) + model_id = metadata.get('model_id') + print("Model ID:", model_id) model = get_embedding_model(model_id) model.load_model() - MODELS[model_id] = model + EMBEDDINGS[embedding_id] = model else: - model = MODELS[model_id] + model = EMBEDDINGS[embedding_id] - if dataset not in DATASETS or model_id not in DATASETS[dataset]: + if dataset not in DATASETS or embedding_id not in DATASETS[dataset]: # load the dataset embeddings - embeddings = np.load(os.path.join(DATA_DIR, dataset, "embeddings", model_id + ".npy")) + embeddings = np.load(os.path.join(DATA_DIR, dataset, "embeddings", embedding_id + ".npy")) print("fitting embeddings") from sklearn.neighbors import NearestNeighbors nne = NearestNeighbors(n_neighbors=num, metric="cosine") nne.fit(embeddings) if dataset not in DATASETS: DATASETS[dataset] = {} - DATASETS[dataset][model_id] = nne + DATASETS[dataset][embedding_id] = nne else: - nne = DATASETS[dataset][model_id] + nne = DATASETS[dataset][embedding_id] # embed the query string and find the nearest neighbor query = request.args.get('query') diff --git a/web/package-lock.json b/web/package-lock.json index e144ae9..5679361 100644 --- a/web/package-lock.json +++ b/web/package-lock.json @@ -8,9 +8,8 @@ "name": "client", "version": "0.0.0", "dependencies": { - "@duckdb/duckdb-wasm": "^1.28.0", - "apache-arrow": "^14.0.2", "d3-scale": "^4.0.2", + "d3-scale-chromatic": "^3.0.0", "react": "^18.2.0", "react-dom": "^18.2.0", "react-router-dom": "^6.20.1", @@ -30,26 +29,6 @@ "vite-plugin-wasm": "^3.3.0" } }, - "node_modules/@75lb/deep-merge": { - "version": "1.1.1", - "resolved": "https://registry.npmjs.org/@75lb/deep-merge/-/deep-merge-1.1.1.tgz", - "integrity": "sha512-xvgv6pkMGBA6GwdyJbNAnDmfAIR/DfWhrj9jgWh3TY7gRm3KO46x/GPjRg6wJ0nOepwqrNxFfojebh0Df4h4Tw==", - "dependencies": { - "lodash.assignwith": "^4.2.0", - 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"node_modules/tslib": { - "version": "2.6.2", - "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.6.2.tgz", - "integrity": "sha512-AEYxH93jGFPn/a2iVAwW87VuUIkR1FVUKB77NwMF7nBTDkDrrT/Hpt/IrCJ0QXhW27jTBDcf5ZY7w6RiqTMw2Q==" - }, "node_modules/tunnel-agent": { "version": "0.6.0", "resolved": "https://registry.npmjs.org/tunnel-agent/-/tunnel-agent-0.6.0.tgz", @@ -4904,14 +4603,6 @@ "url": "https://github.com/sponsors/ljharb" } }, - "node_modules/typical": { - "version": "4.0.0", - "resolved": "https://registry.npmjs.org/typical/-/typical-4.0.0.tgz", - "integrity": "sha512-VAH4IvQ7BDFYglMd7BPRDfLgxZZX4O4TFcRDA6EN5X7erNJJq+McIEp8np9aVtxrCJ6qx4GTYVfOWNjcqwZgRw==", - "engines": { - "node": ">=8" - } - }, "node_modules/unbox-primitive": { "version": "1.0.2", "resolved": "https://registry.npmjs.org/unbox-primitive/-/unbox-primitive-1.0.2.tgz", @@ -5169,14 +4860,6 @@ "url": "https://github.com/sponsors/ljharb" } }, - "node_modules/wordwrapjs": { - "version": "5.1.0", - "resolved": "https://registry.npmjs.org/wordwrapjs/-/wordwrapjs-5.1.0.tgz", - "integrity": "sha512-JNjcULU2e4KJwUNv6CHgI46UvDGitb6dGryHajXTDiLgg1/RiGoPSDw4kZfYnwGtEXf2ZMeIewDQgFGzkCB2Sg==", - "engines": { - "node": ">=12.17" - } - }, "node_modules/wrappy": { "version": "1.0.2", "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz", diff --git a/web/package.json b/web/package.json index e5f71eb..600e136 100644 --- a/web/package.json +++ b/web/package.json @@ -12,6 +12,7 @@ }, "dependencies": { "d3-scale": "^4.0.2", + "d3-scale-chromatic": "^3.0.0", "react": "^18.2.0", "react-dom": "^18.2.0", "react-router-dom": "^6.20.1", diff --git a/web/src/App.jsx b/web/src/App.jsx index bf0b892..c36669a 100644 --- a/web/src/App.jsx +++ b/web/src/App.jsx @@ -5,17 +5,19 @@ import DatasetSetup from './components/DatasetSetup'; import Nav from './components/Nav'; import './App.css'; +const env = import.meta.env; +console.log("ENV", env) +const readonly = import.meta.env.MODE == "read_only" + function App() { - const env = import.meta.env; - console.log("ENV", env) return (