Improve missing-file error and Windows instructions

README.md

@@ -13,6 +13,13 @@ python -m venv .venv
 source .venv/bin/activate
 ```
 
+On Windows (PowerShell):
+
+```powershell
+py -m venv .venv
+.\\.venv\\Scripts\\Activate.ps1
+```
+
 2) Install dependencies:
 
 ```bash
@@ -103,6 +110,14 @@ document-clusterer cluster \
   --reduction-dim 2
 ```
 
+On Windows, you can run the same commands in PowerShell. If `document-clusterer` is not on your `PATH`, use the module form instead:
+
+```powershell
+python -m document_clusterer.cli clean --stories-dir data\sample --output data\sample_cleaned.json
+python -m document_clusterer.cli clean --stories-dir data\\sample --output data\\sample.json
+python -m document_clusterer.cli cluster --input-file data\\sample_cleaned.json --stories-dir data\\sample --output-dir clusteredDocuments\\sample
+```
+
 Environment variable defaults:
 
 * `INPUT_JSON` – cleaned JSON input (default: `all_stories.json`)

document_clusterer/model.py

@@ -6,7 +6,7 @@
 import os
 from collections import Counter, defaultdict
 from pathlib import Path
-from typing import Dict, List, Sequence, cast
+from typing import Dict, List, Sequence, TypedDict, cast
 
 import numpy as np
 from numpy.typing import NDArray
@@ -20,6 +20,18 @@
 IntArray = NDArray[np.int_]
 
 
+class TermCount(TypedDict):
+    term: str
+    count: int
+
+
+class ClusterResult(TypedDict):
+    cluster: str
+    document_count: int
+    documents: list[str]
+    top_terms: list[TermCount]
+
+
 def env_path(var_name: str, default: str) -> Path:
     return Path(os.getenv(var_name, default))
 
@@ -28,7 +40,25 @@ def env_int(var_name: str, default: int) -> int:
     return int(os.getenv(var_name, str(default)))
 
 
+def _ensure_file_exists(path: Path, description: str) -> None:
+    if not path.exists():
+        raise FileNotFoundError(
+            f"{description} does not exist: {path.resolve()} (cwd: {Path.cwd()}) "
+            "If you are using the sample data, run the clean step to generate it."
+        )
+    if not path.is_file():
+        raise ValueError(f"{description} is not a file: {path}")
+
+
+def _ensure_dir_exists(path: Path, description: str) -> None:
+    if not path.exists():
+        raise FileNotFoundError(f"{description} does not exist: {path}")
+    if not path.is_dir():
+        raise NotADirectoryError(f"{description} is not a directory: {path}")
+
+
 def load_documents(data_path: Path) -> List[CleanedDocument]:
+    _ensure_file_exists(data_path, "Input data file")
     LOGGER.info("Loading documents from %s", data_path)
     with data_path.open("r", encoding="utf-8") as infile:
         loaded = json.load(infile)
@@ -223,6 +253,50 @@ def save_summaries(summaries: Dict[int, list[tuple[str, int]]], output_dir: Path
             outfile.write("\n")
 
 
+def save_cluster_results(
+    cluster_to_documents: Dict[str, List[str]],
+    summaries: Dict[int, list[tuple[str, int]]],
+    output_dir: Path,
+) -> None:
+    output_dir.mkdir(parents=True, exist_ok=True)
+    json_path = output_dir / "cluster_results.json"
+    csv_path = output_dir / "cluster_results.csv"
+
+    LOGGER.info("Writing cluster results to %s and %s", json_path, csv_path)
+    formatted: list[ClusterResult] = []
+    for label, documents in sorted(cluster_to_documents.items(), key=lambda item: item[0]):
+        try:
+            numeric_label = int(label)
+        except ValueError:
+            numeric_label = None
+        top_terms = summaries.get(numeric_label, []) if numeric_label is not None else []
+        formatted.append(
+            {
+                "cluster": label,
+                "document_count": len(documents),
+                "documents": documents,
+                "top_terms": [{"term": term, "count": count} for term, count in top_terms],
+            }
+        )
+
+    with json_path.open("w", encoding="utf-8") as outfile:
+        json.dump(formatted, outfile, ensure_ascii=False, indent=2)
+
+    with csv_path.open("w", encoding="utf-8", newline="") as csvfile:
+        fieldnames = ["cluster", "document_count", "documents", "top_terms"]
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+        writer.writeheader()
+        for entry in formatted:
+            writer.writerow(
+                {
+                    "cluster": entry["cluster"],
+                    "document_count": entry["document_count"],
+                    "documents": ";".join(entry["documents"]),
+                    "top_terms": ";".join(f'{term["term"]}:{term["count"]}' for term in entry["top_terms"]),
+                }
+            )
+
+
 def copy_clusters(document_clusters: Dict[str, List[str]], stories_dir: Path, output_dir: Path) -> None:
     output_dir.mkdir(parents=True, exist_ok=True)
 
@@ -245,11 +319,58 @@ def copy_if_exists(src: Path, dst: Path) -> None:
     dst.write_bytes(src.read_bytes())
 
 
+def write_cluster_directories(
+    cluster_to_documents: Dict[str, List[str]],
+    summaries: Dict[int, list[tuple[str, int]]],
+    stories_dir: Path,
+    output_dir: Path,
+    *,
+    copy_documents: bool,
+) -> None:
+    output_dir.mkdir(parents=True, exist_ok=True)
+    LOGGER.info("Writing per-cluster directories to %s (copy_documents=%s)", output_dir, copy_documents)
+
+    for cluster_label, documents in cluster_to_documents.items():
+        cluster_dir = output_dir / cluster_label
+        cluster_dir.mkdir(parents=True, exist_ok=True)
+
+        try:
+            numeric_label = int(cluster_label)
+        except ValueError:
+            numeric_label = None
+
+        summary_path = cluster_dir / "summary.json"
+        summary_content = {
+            "cluster": cluster_label,
+            "document_count": len(documents),
+            "documents": documents,
+            "top_terms": [
+                {"term": term, "count": count}
+                for term, count in (summaries.get(numeric_label, []) if numeric_label is not None else [])
+            ],
+        }
+        summary_path.write_text(json.dumps(summary_content, ensure_ascii=False, indent=2), encoding="utf-8")
+
+        if not copy_documents:
+            continue
+
+        for document_name in documents:
+            srcfile = stories_dir / document_name
+            dstfile = cluster_dir / document_name
+            LOGGER.debug("Copying %s -> %s", srcfile, dstfile)
+            copy_if_exists(srcfile, dstfile)
+
+
 def cluster_documents(
     data_path: Path,
     stories_dir: Path,
     output_dir: Path,
     *,
+    assignments_output_dir: Path | None = None,
+    results_output_dir: Path | None = None,
+    cluster_output_dir: Path | None = None,
+    create_cluster_dirs: bool = True,
+    copy_cluster_documents: bool = True,
     model_name: str = "all-MiniLM-L6-v2",
     cluster_method: str = "kmeans",
     cluster_count: int | None = 10,
@@ -264,6 +385,17 @@ def cluster_documents(
     summary_top_n: int = 10,
     assignments_basename: str = "cluster_assignments",
 ) -> Dict[str, List[str]]:
+    LOGGER.info("Preparing to cluster documents")
+    _ensure_file_exists(data_path, "Input data file")
+    _ensure_dir_exists(stories_dir, "Stories directory")
+
+    assignments_root = assignments_output_dir or output_dir
+    results_root = results_output_dir or output_dir
+    clusters_root = cluster_output_dir or output_dir
+
+    for destination in (assignments_root, results_root, clusters_root, output_dir):
+        destination.mkdir(parents=True, exist_ok=True)
+
     documents = load_documents(data_path)
     if not documents:
         raise ValueError("No documents found to cluster.")
@@ -290,18 +422,30 @@ def cluster_documents(
     save_assignments(
         documents,
         labels,
-        output_dir=output_dir,
+        output_dir=assignments_root,
         basename=assignments_basename,
         reduced_embeddings=reduced_embeddings,
     )
 
     summaries = summarize_clusters(documents, labels, top_n=summary_top_n)
-    save_summaries(summaries, output_dir)
+    save_summaries(summaries, results_root)
 
     cluster_to_documents: Dict[str, List[str]] = defaultdict(list)
     for document, label in zip(documents, labels):
         cluster_label = str(label) if label != -1 else "noise"
         cluster_to_documents[cluster_label].append(document["filename"])
 
-    copy_clusters(cluster_to_documents, stories_dir, output_dir)
+    save_cluster_results(cluster_to_documents, summaries, results_root)
+
+    if create_cluster_dirs:
+        write_cluster_directories(
+            cluster_to_documents,
+            summaries,
+            stories_dir,
+            clusters_root,
+            copy_documents=copy_cluster_documents,
+        )
+    else:
+        LOGGER.info("Skipping per-cluster directory creation")
+
     return cluster_to_documents