feat: text editor and blog post

api/pyproject.toml

@@ -10,6 +10,7 @@ readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
     "flask>=2.3.2,<3.0.0",
+    "flask-cors>=4.0.0,<5.0.0",
     "transformers>=4.30.2,<5.0.0",
     "nltk>=3.8.1,<4.0.0",
     "sentence-transformers>=2.2.2,<3.0.0",

api/salience/__init__.py

@@ -1,25 +1,53 @@
 from flask import Flask, request
+from flask_cors import CORS
 import numpy as np
 from .salience import extract, AVAILABLE_MODELS
 import json
 
 app = Flask(__name__)
+CORS(app, origins=["http://localhost:5173"])
 
+# Load default text from transcript.txt for GET requests
 with open('./transcript.txt', 'r') as file:
-    source_text = file.read().strip()
+    default_source_text = file.read().strip()
 
 @app.route("/models")
 def models_view():
     return json.dumps(list(AVAILABLE_MODELS.keys()))
 
-@app.route("/salience")
-def salience_view():
+@app.route("/salience", methods=['GET'])
+def salience_view_default():
+    """GET endpoint - processes default text from transcript.txt"""
     model_name = request.args.get('model', 'all-mpnet-base-v2')
 
     # Validate model name
     if model_name not in AVAILABLE_MODELS:
         return json.dumps({'error': f'Invalid model: {model_name}'}), 400
 
+    sentence_ranges, adjacency = extract(default_source_text, model_name)
+
+    return json.dumps({
+        'source': default_source_text,
+        'intervals': sentence_ranges,
+        'adjacency': np.nan_to_num(adjacency.numpy()).tolist(),
+        'model': model_name,
+    })
+
+@app.route("/salience", methods=['POST'])
+def salience_view_custom():
+    """POST endpoint - processes text from request body"""
+    model_name = request.args.get('model', 'all-mpnet-base-v2')
+
+    # Validate model name
+    if model_name not in AVAILABLE_MODELS:
+        return json.dumps({'error': f'Invalid model: {model_name}'}), 400
+
+    # Get document content from request body as plain text
+    source_text = request.data.decode('utf-8').strip()
+
+    if not source_text:
+        return json.dumps({'error': 'No text provided'}), 400
+
     sentence_ranges, adjacency = extract(source_text, model_name)
 
     return json.dumps({

api/salience/salience.py

@@ -83,16 +83,31 @@ def text_rank(sentences, model_name='all-mpnet-base-v2'):
     adjacency[adjacency < 0] = 0
     return normalized_adjacency(adjacency)
 
+def extract(source_text, model_name='all-mpnet-base-v2'):
+    """
+    Main API function that extracts sentence positions and computes normalized adjacency matrix.
+
+    Returns:
+        sentence_ranges: List of (start, end) tuples for each sentence's character position
+        adjacency: (N × N) normalized adjacency matrix where N is the number of sentences.
+                   Each entry (i,j) represents the normalized similarity between sentences i and j.
+                   This matrix is returned to the frontend, which raises it to a power and computes
+                   the final salience scores via random walk simulation.
+    """
+    sentences, sentence_ranges = get_sentences(source_text)
+    adjacency = text_rank(sentences, model_name)
+    return sentence_ranges, adjacency
+
+
+# =============================================================================
+# Unused/Debugging Code
+# =============================================================================
+
 def terminal_distr(adjacency, initial=None):
     sample = initial if initial is not None else torch.full((adjacency.shape[0],), 1.)
     scores = sample.matmul(torch.matrix_power(adjacency, 10)).numpy().tolist()
     return scores
 
-def extract(source_text, model_name='all-mpnet-base-v2'):
-    sentences, sentence_ranges = get_sentences(source_text)
-    adjacency = text_rank(sentences, model_name)
-    return sentence_ranges, adjacency
-
 def get_results(sentences, adjacency):
     scores = terminal_distr(adjacency)
     for score, sentence in sorted(zip(scores, sentences), key=lambda xs: xs[0]):

api/uv.lock

@@ -142,6 +142,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fd/56/26f0be8adc2b4257df20c1c4260ddd0aa396cf8e75d90ab2f7ff99bc34f9/flask-2.3.3-py3-none-any.whl", hash = "sha256:f69fcd559dc907ed196ab9df0e48471709175e696d6e698dd4dbe940f96ce66b", size = 96112 },
 ]
 
+[[package]]
+name = "flask-cors"
+version = "4.0.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "flask" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/1c/41/89ea5af8b9d647036237c528abb2fdf8bb10b23b3f750e8e2da07873b270/flask_cors-4.0.2.tar.gz", hash = "sha256:493b98e2d1e2f1a4720a7af25693ef2fe32fbafec09a2f72c59f3e475eda61d2", size = 30954 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e1/60/e941089faf4f50f2e0231d7f7af69308616a37e99da3ec75df60b8809db7/Flask_Cors-4.0.2-py2.py3-none-any.whl", hash = "sha256:38364faf1a7a5d0a55bd1d2e2f83ee9e359039182f5e6a029557e1f56d92c09a", size = 14467 },
+]
+
 [[package]]
 name = "fsspec"
 version = "2025.10.0"
@@ -789,6 +801,7 @@ version = "0.0.0"
 source = { editable = "." }
 dependencies = [
     { name = "flask" },
+    { name = "flask-cors" },
     { name = "nltk" },
     { name = "numpy" },
     { name = "sentence-transformers" },
@@ -798,6 +811,7 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "flask", specifier = ">=2.3.2,<3.0.0" },
+    { name = "flask-cors", specifier = ">=4.0.0,<5.0.0" },
     { name = "nltk", specifier = ">=3.8.1,<4.0.0" },
     { name = "numpy", specifier = ">=1.25.0,<2.0.0" },
     { name = "sentence-transformers", specifier = ">=2.2.2,<3.0.0" },