From 839234eb100ba3285b7470c2fdbc72c7f267543d Mon Sep 17 00:00:00 2001
From: madhvantyagi <randombytes1024@gmail.com>
Date: Fri, 15 May 2026 01:14:21 -0400
Subject: [PATCH 1/5] Add email status classifier model

---
 classifier_model/model.pkl                   | Bin 0 -> 837482 bytes
 classifier_model/model_use.md                | 100 ++++++
 engine/email_status_classifier/__init__.py   |  17 +
 engine/email_status_classifier/classifier.py | 276 +++++++++++++++
 requirements.txt                             |   3 +
 scripts/train_email_status_classifier.py     | 339 +++++++++++++++++++
 6 files changed, 735 insertions(+)
 create mode 100644 classifier_model/model.pkl
 create mode 100644 classifier_model/model_use.md
 create mode 100644 engine/email_status_classifier/__init__.py
 create mode 100644 engine/email_status_classifier/classifier.py
 create mode 100644 scripts/train_email_status_classifier.py

diff --git a/classifier_model/model.pkl b/classifier_model/model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..943493c0a9e7bfc88a8285a6e332139ac5fa7a3c
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diff --git a/classifier_model/model_use.md b/classifier_model/model_use.md
new file mode 100644
index 0000000..f961249
--- /dev/null
+++ b/classifier_model/model_use.md
@@ -0,0 +1,100 @@
+# Email Status Classifier
+
+A small model that reads an email and predicts where it belongs in the job-search pipeline.
+
+## Classes
+
+- `Not Relevant`
+  - Job alerts, recommendations, newsletters, hiring events, marketing emails, or generic recruiter spam.
+  - The key idea: it is not about a real application that was already submitted.
+
+- `Applied`
+  - The company or platform confirms the application was submitted or received.
+  - No interview, assessment, recruiter reply, or real next step yet.
+
+- `InProgress`
+  - Something happened after applying.
+  - Examples: interview scheduling, assessment invite, recruiter reply, availability request, next-step email.
+
+- `Rejected`
+  - The company clearly says they are not moving forward.
+  - Also covers role filled, not selected, hiring paused, or application path closed.
+
+## What The Model File Contains
+
+- Saved file: `classifier_model/model.pkl`
+- Wrapper class: `EmailStatusClassifier`
+- Inside it:
+  - TensorFlow `TextVectorization` layer
+  - scikit-learn `RandomForestClassifier`
+
+Flow:
+
+```text
+email text -> TF-IDF vectorizer -> RandomForest -> class label
+```
+
+There is no rule-based guardrail layer now. The prediction is model output only.
+
+## Usage
+
+Terminal:
+
+```bash
+.venv/bin/python scripts/train_email_status_classifier.py \
+  --model-path classifier_model/model.pkl \
+  --subject "Update on your application" \
+  --sender "recruiting@company.com" \
+  --body "Unfortunately, we will not be moving forward with your application at this time."
+```
+
+Python:
+
+```python
+from engine.email_status_classifier import EmailStatusClassifier
+
+clf = EmailStatusClassifier.load("classifier_model/model.pkl")
+
+label = clf.predict_email(
+    subject="Schedule your interview",
+    sender="recruiting@company.com",
+    body="Thanks for applying. We reviewed your application and would like to schedule your first interview.",
+)
+
+print(label)
+```
+
+Batch prediction:
+
+```python
+texts = [
+    "Subject: Jobs for you\nSender: LinkedIn\nBody: Recommended jobs based on your activity. View all jobs.",
+    "Subject: Application received\nSender: no-reply@company.com\nBody: Thank you for applying. Your application was submitted.",
+]
+
+labels = clf.predict_texts(texts)
+print(labels)
+```
+
+## Size And Latency
+
+- Model size: `820 KB`
+- Load time: about `4.2 seconds`
+- Predict 300 emails: about `0.064 seconds`
+- Per email in a batch: about `0.21 ms`
+- Single email after load: about `18 ms`
+
+The slow part is loading TensorFlow and the saved vectorizer. After loading, prediction is fast.
+
+Use it this way:
+
+- Good: load once, predict many emails.
+- Bad: load the model again for every single email.
+
+## Current Test Result
+
+- Accuracy: `1.0`
+- Macro F1: `1.0`
+- Postprocessing: `none`
+
+This score is on the synthetic test dataset, not real Gmail data. For real quality, the next step is testing on real emails and adding missed patterns back into the labeled dataset.
diff --git a/engine/email_status_classifier/__init__.py b/engine/email_status_classifier/__init__.py
new file mode 100644
index 0000000..7de0e3b
--- /dev/null
+++ b/engine/email_status_classifier/__init__.py
@@ -0,0 +1,17 @@
+"""Utilities for the job email status classifier."""
+
+from .classifier import (
+    LABELS,
+    EmailStatusClassifier,
+    EmailStatusExample,
+    combine_email_fields,
+    load_jsonl_dataset,
+)
+
+__all__ = [
+    "LABELS",
+    "EmailStatusClassifier",
+    "EmailStatusExample",
+    "combine_email_fields",
+    "load_jsonl_dataset",
+]
diff --git a/engine/email_status_classifier/classifier.py b/engine/email_status_classifier/classifier.py
new file mode 100644
index 0000000..e16d445
--- /dev/null
+++ b/engine/email_status_classifier/classifier.py
@@ -0,0 +1,276 @@
+"""Training and inference helpers for job email status classification.
+
+The primary model is a RandomForest classifier. TensorFlow is optional and is
+only used as a TF-IDF feature builder through Keras TextVectorization when the
+runtime has TensorFlow installed. The scikit-learn TF-IDF path stays available
+because TensorFlow is a heavy dependency for this project.
+"""
+
+from __future__ import annotations
+
+import json
+import pickle
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Iterable, Sequence
+
+
+LABELS = ["Not Relevant", "Applied", "InProgress", "Rejected"]
+LABEL_TO_ID = {label: index for index, label in enumerate(LABELS)}
+ID_TO_LABEL = {index: label for label, index in LABEL_TO_ID.items()}
+
+
+@dataclass(frozen=True)
+class EmailStatusExample:
+    subject: str
+    sender: str
+    body: str
+    label: str | None = None
+    metadata: dict[str, Any] | None = None
+
+    @property
+    def text(self) -> str:
+        return combine_email_fields(self.subject, self.sender, self.body)
+
+
+def combine_email_fields(subject: str = "", sender: str = "", body: str = "") -> str:
+    """Create a stable text representation from email fields."""
+    return "\n".join(
+        [
+            f"Subject: {_clean_text(subject)}",
+            f"Sender: {_clean_text(sender)}",
+            f"Body: {_clean_text(body)}",
+        ]
+    )
+
+
+def load_jsonl_dataset(path: str | Path, require_label: bool = True) -> list[EmailStatusExample]:
+    dataset_path = Path(path)
+    if not dataset_path.exists():
+        raise FileNotFoundError(f"Dataset file not found: {dataset_path}")
+
+    examples: list[EmailStatusExample] = []
+    with dataset_path.open("r", encoding="utf-8") as handle:
+        for line_number, raw_line in enumerate(handle, start=1):
+            line = raw_line.strip()
+            if not line:
+                continue
+            try:
+                row = json.loads(line)
+            except json.JSONDecodeError as exc:
+                raise ValueError(f"{dataset_path}:{line_number} is not valid JSON") from exc
+
+            label = _extract_label(row)
+            if require_label and label is None:
+                raise ValueError(f"{dataset_path}:{line_number} is missing a label/status field")
+            if label is not None and label not in LABEL_TO_ID:
+                raise ValueError(
+                    f"{dataset_path}:{line_number} has unsupported label {label!r}; "
+                    f"expected one of {LABELS}"
+                )
+
+            examples.append(
+                EmailStatusExample(
+                    subject=str(row.get("subject") or ""),
+                    sender=str(row.get("sender") or row.get("from") or ""),
+                    body=str(row.get("body") or row.get("text") or ""),
+                    label=label,
+                    metadata={key: value for key, value in row.items() if key not in {"subject", "sender", "from", "body", "text", "label", "status"}},
+                )
+            )
+
+    if not examples:
+        raise ValueError(f"Dataset file has no examples: {dataset_path}")
+    return examples
+
+
+class SklearnTfidfVectorizer:
+    name = "sklearn"
+
+    def __init__(self, max_features: int):
+        from sklearn.feature_extraction.text import TfidfVectorizer
+
+        self.vectorizer = TfidfVectorizer(
+            max_features=max_features,
+            ngram_range=(1, 2),
+            min_df=1,
+            strip_accents="unicode",
+            lowercase=True,
+            sublinear_tf=True,
+        )
+
+    def fit_transform(self, texts: Sequence[str]):
+        return self.vectorizer.fit_transform(texts)
+
+    def transform(self, texts: Sequence[str]):
+        return self.vectorizer.transform(texts)
+
+
+class TensorFlowTfidfVectorizer:
+    name = "tensorflow"
+
+    def __init__(self, max_features: int):
+        import tensorflow as tf
+
+        # max_features = max vocabulary size.
+        # Example: 12000 means keep up to 12k useful words/tokens from training emails.
+        self.max_features = max_features
+        self._tf = tf
+        self.vectorizer = self._build_layer()
+
+    def __getstate__(self) -> dict[str, Any]:
+        # pickle cannot safely store the whole TensorFlow layer state by itself.
+        # So we manually save the two things needed for future predictions:
+        # 1. vocabulary: word -> vector position
+        # 2. idf_weights: word importance for TF-IDF
+        idf_weights = None
+        lookup_layer = getattr(self.vectorizer, "_lookup_layer", None)
+        if lookup_layer is not None and getattr(lookup_layer, "idf_weights", None) is not None:
+            idf_weights = lookup_layer.idf_weights.numpy().tolist()
+        return {
+            "max_features": self.max_features,
+            "vocabulary": self.vectorizer.get_vocabulary(),
+            "idf_weights": idf_weights,
+        }
+
+    def __setstate__(self, state: dict[str, Any]) -> None:
+        import tensorflow as tf
+
+        # Rebuild the TensorFlow vectorizer when model.pkl is loaded.
+        # Then put back the exact same vocabulary + IDF weights from training.
+        self.max_features = state["max_features"]
+        self._tf = tf
+        self.vectorizer = self._build_layer()
+        idf_weights = state.get("idf_weights")
+        if idf_weights is not None:
+            self.vectorizer.set_vocabulary(
+                state["vocabulary"],
+                idf_weights=self._tf.constant(idf_weights, dtype=self._tf.float32),
+            )
+        else:
+            self.vectorizer.set_vocabulary(state["vocabulary"])
+
+    def _build_layer(self):
+        # output_mode="tf_idf" means the vector is not just word count.
+        # It gives more weight to words that are important/rare across emails.
+        # ngrams=(1, 2) keeps single words and two-word phrases.
+        # That matters for this task because "moving forward", "job alert",
+        # and "application received" carry more meaning than each word alone.
+        return self._tf.keras.layers.TextVectorization(
+            max_tokens=self.max_features,
+            ngrams=(1, 2),
+            output_mode="tf_idf",
+            standardize="lower_and_strip_punctuation",
+        )
+
+    def fit_transform(self, texts: Sequence[str]):
+        # Training only:
+        # adapt() reads the training emails and learns vocabulary + IDF weights.
+        # batch(128) just feeds emails to TensorFlow in chunks instead of one by one.
+        dataset = self._tf.data.Dataset.from_tensor_slices(list(texts)).batch(128)
+        self.vectorizer.adapt(dataset)
+        return self.transform(texts)
+
+    def transform(self, texts: Sequence[str]):
+        # Prediction/test:
+        # use the vocabulary + IDF already learned by adapt().
+        # Do not call adapt() here, or vector positions would change.
+        features = self.vectorizer(self._tf.constant(list(texts)))
+        return features.numpy()
+
+
+class EmailStatusClassifier:
+    """Serializable RandomForest email status classifier."""
+
+    def __init__(self, vectorizer: Any, model: Any):
+        self.vectorizer = vectorizer
+        self.model = model
+
+    def predict_texts(self, texts: Sequence[str]) -> list[str]:
+        # Text -> TF-IDF vector -> RandomForest class id -> label name.
+        features = self.vectorizer.transform(texts)
+        raw_ids = self.model.predict(features)
+        return [ID_TO_LABEL[int(label_id)] for label_id in raw_ids]
+
+    def predict_email(
+        self,
+        subject: str = "",
+        sender: str = "",
+        body: str = "",
+    ) -> str:
+        text = combine_email_fields(subject=subject, sender=sender, body=body)
+        return self.predict_texts([text])[0]
+
+    def save(self, path: str | Path) -> None:
+        with Path(path).open("wb") as handle:
+            pickle.dump(self, handle)
+
+    @classmethod
+    def load(cls, path: str | Path) -> "EmailStatusClassifier":
+        with Path(path).open("rb") as handle:
+            loaded = pickle.load(handle)
+        if not isinstance(loaded, cls):
+            raise TypeError(f"Artifact at {path} is not an EmailStatusClassifier")
+        return loaded
+
+
+def build_vectorizer(choice: str, max_features: int):
+    normalized = choice.lower()
+    if normalized == "sklearn":
+        return SklearnTfidfVectorizer(max_features=max_features), None
+    if normalized == "tensorflow":
+        try:
+            return TensorFlowTfidfVectorizer(max_features=max_features), None
+        except ImportError as exc:
+            return None, (
+                "TensorFlow is not installed, so Keras TextVectorization cannot be used. "
+                "Install TensorFlow separately or run with --vectorizer sklearn."
+            )
+    if normalized == "auto":
+        try:
+            return TensorFlowTfidfVectorizer(max_features=max_features), None
+        except ImportError:
+            return SklearnTfidfVectorizer(max_features=max_features), (
+                "TensorFlow is not installed; using scikit-learn TF-IDF fallback."
+            )
+    raise ValueError("vectorizer must be one of: auto, tensorflow, sklearn")
+
+
+def labels_to_ids(labels: Iterable[str]) -> list[int]:
+    return [LABEL_TO_ID[label] for label in labels]
+
+
+def ids_to_labels(label_ids: Iterable[int]) -> list[str]:
+    return [ID_TO_LABEL[int(label_id)] for label_id in label_ids]
+
+
+def write_json(path: str | Path, payload: dict[str, Any]) -> None:
+    with Path(path).open("w", encoding="utf-8") as handle:
+        json.dump(payload, handle, indent=2, sort_keys=True)
+        handle.write("\n")
+
+
+def _extract_label(row: dict[str, Any]) -> str | None:
+    raw_label = row.get("label", row.get("status"))
+    if raw_label is None:
+        return None
+    label = str(raw_label).strip()
+    aliases = {
+        "not_relevant": "Not Relevant",
+        "not relevant": "Not Relevant",
+        "irrelevant": "Not Relevant",
+        "applied": "Applied",
+        "in_progress": "InProgress",
+        "inprogress": "InProgress",
+        "in progress": "InProgress",
+        "interview": "InProgress",
+        "assessment": "InProgress",
+        "rejected": "Rejected",
+        "rejection": "Rejected",
+    }
+    return aliases.get(label.lower(), label)
+
+
+def _clean_text(value: str) -> str:
+    return re.sub(r"\s+", " ", str(value or "")).strip()
diff --git a/requirements.txt b/requirements.txt
index 2756712..0f0c2dd 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -94,3 +94,6 @@ uvicorn==0.40.0
 virtualenv==20.36.1
 websockets==16.0
 yarl==1.22.0
+scipy==1.11.4
+scikit-learn==1.4.2
+tensorflow==2.16.1
diff --git a/scripts/train_email_status_classifier.py b/scripts/train_email_status_classifier.py
new file mode 100644
index 0000000..0fb7676
--- /dev/null
+++ b/scripts/train_email_status_classifier.py
@@ -0,0 +1,339 @@
+#!/usr/bin/env python3
+"""Train or use the job email status classifier.
+
+Simple pipeline:
+1. Read labeled emails from JSONL.
+2. Combine subject + sender + body into one text string.
+3. Convert text into TF-IDF vectors.
+4. Train RandomForest on those vectors.
+5. Save vectorizer + model together in model.pkl.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import sys
+from pathlib import Path
+from typing import Any
+
+
+# Let this script import the local engine package when run from repo root.
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+
+try:
+    from sklearn.ensemble import RandomForestClassifier
+    from sklearn.metrics import (
+        accuracy_score,
+        classification_report,
+        confusion_matrix,
+        precision_recall_fscore_support,
+    )
+except ImportError as exc:
+    raise SystemExit(
+        "Missing ML dependency. Run:\n"
+        "  pip install -r requirements.txt\n\n"
+        f"Import error: {exc}"
+    ) from exc
+
+
+from engine.email_status_classifier import LABELS, EmailStatusClassifier, load_jsonl_dataset
+from engine.email_status_classifier.classifier import (
+    build_vectorizer,
+    combine_email_fields,
+    ids_to_labels,
+    labels_to_ids,
+    write_json,
+)
+
+
+DEFAULT_TRAIN_PATH = PROJECT_ROOT / "data" / "email_status_dataset" / "train.jsonl"
+DEFAULT_TEST_PATH = PROJECT_ROOT / "data" / "email_status_dataset" / "test.jsonl"
+DEFAULT_OUTPUT_DIR = PROJECT_ROOT / "models" / "email_status_classifier"
+
+
+# =============================================================================
+# CLI params
+# =============================================================================
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Train, evaluate, or run the job email status classifier."
+    )
+
+    # Dataset paths. Each JSONL row should have subject, sender, body, and label.
+    parser.add_argument("--train-path", type=Path, default=DEFAULT_TRAIN_PATH)
+    parser.add_argument("--test-path", type=Path, default=DEFAULT_TEST_PATH)
+    parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT_DIR)
+
+    # Text vectorizer.
+    # tensorflow = TensorFlow TextVectorization TF-IDF.
+    # sklearn = scikit-learn TfidfVectorizer fallback.
+    parser.add_argument("--vectorizer", choices=["tensorflow", "sklearn", "auto"], default="tensorflow")
+    parser.add_argument(
+        "--max-features",
+        type=int,
+        default=12000,
+        help="Maximum vocabulary size. Bigger keeps more words, but can add noise.",
+    )
+
+    # RandomForest params.
+    parser.add_argument("--seed", type=int, default=42, help="Makes training repeatable.")
+    parser.add_argument(
+        "--n-estimators",
+        type=int,
+        default=300,
+        help="Number of trees. More trees are slower but usually more stable.",
+    )
+    parser.add_argument(
+        "--max-depth",
+        type=optional_int,
+        default=None,
+        help="Tree depth. None lets trees grow fully; smaller values reduce overfit.",
+    )
+    parser.add_argument(
+        "--min-samples-leaf",
+        type=int,
+        default=1,
+        help="Minimum rows in a final tree leaf. Higher values reduce overfit.",
+    )
+    parser.add_argument(
+        "--class-weight",
+        default="balanced",
+        help='Use "balanced" when classes may be uneven; use "none" to disable.',
+    )
+
+    # Inference mode. If any of these are present, the script predicts instead of training.
+    parser.add_argument("--predict-text", default=None)
+    parser.add_argument("--subject", default="")
+    parser.add_argument("--sender", default="")
+    parser.add_argument("--body", default="")
+    parser.add_argument("--model-path", type=Path, default=None)
+
+    return parser.parse_args()
+
+
+# =============================================================================
+# Main switch: train mode or prediction mode
+# =============================================================================
+
+
+def main() -> int:
+    args = parse_args()
+    model_path = args.model_path or args.output_dir / "model.pkl"
+
+    if args.predict_text is not None or args.subject or args.sender or args.body:
+        predict_one_email(args, model_path)
+    else:
+        train_and_evaluate(args, model_path)
+
+    return 0
+
+
+# =============================================================================
+# Training
+# =============================================================================
+
+
+def train_and_evaluate(args: argparse.Namespace, model_path: Path) -> None:
+    # 1. Read dataset rows.
+    train_examples = load_jsonl_dataset(args.train_path)
+    test_examples = load_jsonl_dataset(args.test_path)
+
+    # 2. Convert each row into one text field:
+    #    "Subject: ...\nSender: ...\nBody: ..."
+    train_texts = [example.text for example in train_examples]
+    test_texts = [example.text for example in test_examples]
+
+    # 3. Convert labels into ids RandomForest can train on.
+    #    Not Relevant -> 0, Applied -> 1, InProgress -> 2, Rejected -> 3.
+    y_train = labels_to_ids([example.label or "" for example in train_examples])
+    y_test = labels_to_ids([example.label or "" for example in test_examples])
+
+    # 4. Build text vectorizer.
+    #    fit_transform() learns vocabulary/IDF from training emails and converts train text.
+    #    transform() converts test emails using the same learned vocabulary/IDF.
+    vectorizer, warning = build_vectorizer(args.vectorizer, args.max_features)
+    if vectorizer is None:
+        raise SystemExit(warning)
+    if warning:
+        print(f"Note: {warning}", file=sys.stderr)
+
+    x_train = vectorizer.fit_transform(train_texts)
+    x_test = vectorizer.transform(test_texts)
+
+    # 5. Train RandomForest.
+    #    RandomForest sees only numeric TF-IDF vectors, not raw text.
+    model = RandomForestClassifier(
+        n_estimators=args.n_estimators,
+        max_depth=args.max_depth,
+        min_samples_leaf=args.min_samples_leaf,
+        class_weight=parse_class_weight(args.class_weight),
+        random_state=args.seed,
+        n_jobs=-1,
+    )
+    model.fit(x_train, y_train)
+
+    # 6. Predict test data.
+    #    This is pure model output. No rule-based postprocessing is mixed in.
+    raw_pred_ids = model.predict(x_test)
+    predicted_labels = ids_to_labels(raw_pred_ids)
+    predicted_ids = labels_to_ids(predicted_labels)
+
+    # 7. Save everything needed later:
+    #    model.pkl contains both vectorizer + trained RandomForest.
+    classifier = EmailStatusClassifier(vectorizer=vectorizer, model=model)
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    classifier.save(model_path)
+
+    metrics = build_metrics(y_test, predicted_ids, args, vectorizer.name, warning)
+    write_json(args.output_dir / "metrics.json", metrics)
+    write_json(args.output_dir / "label_mapping.json", build_label_mapping())
+    write_confusion_matrix(args.output_dir / "confusion_matrix.csv", y_test, predicted_ids)
+    write_predictions(args.output_dir / "predictions.jsonl", test_examples, predicted_labels)
+
+    print(f"Saved model: {model_path}")
+    print(f"Accuracy: {metrics['accuracy']:.4f}")
+    print(f"Macro F1: {metrics['macro']['f1']:.4f}")
+
+
+# =============================================================================
+# Prediction
+# =============================================================================
+
+
+def predict_one_email(args: argparse.Namespace, model_path: Path) -> None:
+    if not model_path.exists():
+        raise SystemExit(f"Model artifact not found: {model_path}")
+
+    classifier = EmailStatusClassifier.load(model_path)
+
+    if args.predict_text is not None:
+        text = args.predict_text
+    else:
+        text = combine_email_fields(args.subject, args.sender, args.body)
+
+    prediction = classifier.predict_texts([text])[0]
+    print(prediction)
+
+
+# =============================================================================
+# Output files
+# =============================================================================
+
+
+def build_metrics(
+    y_true: list[int],
+    y_pred: list[int],
+    args: argparse.Namespace,
+    vectorizer_name: str,
+    vectorizer_warning: str | None,
+) -> dict[str, Any]:
+    macro_precision, macro_recall, macro_f1, _ = precision_recall_fscore_support(
+        y_true,
+        y_pred,
+        labels=list(range(len(LABELS))),
+        average="macro",
+        zero_division=0,
+    )
+    per_class = classification_report(
+        y_true,
+        y_pred,
+        labels=list(range(len(LABELS))),
+        target_names=LABELS,
+        output_dict=True,
+        zero_division=0,
+    )
+
+    return {
+        "labels": LABELS,
+        "accuracy": float(accuracy_score(y_true, y_pred)),
+        "macro": {
+            "precision": float(macro_precision),
+            "recall": float(macro_recall),
+            "f1": float(macro_f1),
+        },
+        "per_class": {
+            label: {
+                "precision": float(per_class[label]["precision"]),
+                "recall": float(per_class[label]["recall"]),
+                "f1": float(per_class[label]["f1-score"]),
+                "support": int(per_class[label]["support"]),
+            }
+            for label in LABELS
+        },
+        "training": {
+            "train_path": str(args.train_path),
+            "test_path": str(args.test_path),
+            "vectorizer_used": vectorizer_name,
+            "vectorizer_warning": vectorizer_warning,
+            "max_features": args.max_features,
+            "n_estimators": args.n_estimators,
+            "max_depth": args.max_depth,
+            "min_samples_leaf": args.min_samples_leaf,
+            "class_weight": args.class_weight,
+            "seed": args.seed,
+            "postprocessing": "none",
+        },
+    }
+
+
+def write_confusion_matrix(path: Path, y_true: list[int], y_pred: list[int]) -> None:
+    matrix = confusion_matrix(y_true, y_pred, labels=list(range(len(LABELS))))
+
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.writer(handle)
+        writer.writerow(["actual\\predicted", *LABELS])
+        for label, row in zip(LABELS, matrix):
+            writer.writerow([label, *[int(value) for value in row]])
+
+
+def write_predictions(
+    path: Path,
+    examples: list[Any],
+    predicted_labels: list[str],
+) -> None:
+    with path.open("w", encoding="utf-8") as handle:
+        for example, predicted_label in zip(examples, predicted_labels):
+            row = {
+                "subject": example.subject,
+                "sender": example.sender,
+                "true_label": example.label,
+                "prediction": predicted_label,
+            }
+            if example.metadata:
+                row["metadata"] = example.metadata
+            handle.write(json.dumps(row, sort_keys=True) + "\n")
+
+
+# =============================================================================
+# Tiny parsing helpers
+# =============================================================================
+
+
+def optional_int(value: str) -> int | None:
+    if value.lower() in {"none", "null", ""}:
+        return None
+    return int(value)
+
+
+def parse_class_weight(value: str) -> str | None:
+    if value.lower() in {"none", "null", ""}:
+        return None
+    return value
+
+
+def build_label_mapping() -> dict[str, Any]:
+    return {
+        "labels": LABELS,
+        "label_to_id": {label: index for index, label in enumerate(LABELS)},
+    }
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

From 5a451e9c62fd52a59a76a1621fc985dd234bcbc9 Mon Sep 17 00:00:00 2001
From: madhvantyagi <90159839+madhvantyagi@users.noreply.github.com>
Date: Fri, 15 May 2026 01:19:50 -0400
Subject: [PATCH 2/5] Clean up __init__.py by removing docstring and imports

Remove unused imports and docstring from __init__.py
---
 engine/email_status_classifier/__init__.py | 16 ----------------
 1 file changed, 16 deletions(-)

diff --git a/engine/email_status_classifier/__init__.py b/engine/email_status_classifier/__init__.py
index 7de0e3b..8b13789 100644
--- a/engine/email_status_classifier/__init__.py
+++ b/engine/email_status_classifier/__init__.py
@@ -1,17 +1 @@
-"""Utilities for the job email status classifier."""
 
-from .classifier import (
-    LABELS,
-    EmailStatusClassifier,
-    EmailStatusExample,
-    combine_email_fields,
-    load_jsonl_dataset,
-)
-
-__all__ = [
-    "LABELS",
-    "EmailStatusClassifier",
-    "EmailStatusExample",
-    "combine_email_fields",
-    "load_jsonl_dataset",
-]

From a38bedd0c07cdbb1f5ccb9b0e03e70b620b9aa4a Mon Sep 17 00:00:00 2001
From: madhvantyagi <90159839+madhvantyagi@users.noreply.github.com>
Date: Fri, 15 May 2026 01:21:41 -0400
Subject: [PATCH 3/5] Delete engine/email_status_classifier/classifier.py

---
 engine/email_status_classifier/classifier.py | 276 -------------------
 1 file changed, 276 deletions(-)
 delete mode 100644 engine/email_status_classifier/classifier.py

diff --git a/engine/email_status_classifier/classifier.py b/engine/email_status_classifier/classifier.py
deleted file mode 100644
index e16d445..0000000
--- a/engine/email_status_classifier/classifier.py
+++ /dev/null
@@ -1,276 +0,0 @@
-"""Training and inference helpers for job email status classification.
-
-The primary model is a RandomForest classifier. TensorFlow is optional and is
-only used as a TF-IDF feature builder through Keras TextVectorization when the
-runtime has TensorFlow installed. The scikit-learn TF-IDF path stays available
-because TensorFlow is a heavy dependency for this project.
-"""
-
-from __future__ import annotations
-
-import json
-import pickle
-import re
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any, Iterable, Sequence
-
-
-LABELS = ["Not Relevant", "Applied", "InProgress", "Rejected"]
-LABEL_TO_ID = {label: index for index, label in enumerate(LABELS)}
-ID_TO_LABEL = {index: label for label, index in LABEL_TO_ID.items()}
-
-
-@dataclass(frozen=True)
-class EmailStatusExample:
-    subject: str
-    sender: str
-    body: str
-    label: str | None = None
-    metadata: dict[str, Any] | None = None
-
-    @property
-    def text(self) -> str:
-        return combine_email_fields(self.subject, self.sender, self.body)
-
-
-def combine_email_fields(subject: str = "", sender: str = "", body: str = "") -> str:
-    """Create a stable text representation from email fields."""
-    return "\n".join(
-        [
-            f"Subject: {_clean_text(subject)}",
-            f"Sender: {_clean_text(sender)}",
-            f"Body: {_clean_text(body)}",
-        ]
-    )
-
-
-def load_jsonl_dataset(path: str | Path, require_label: bool = True) -> list[EmailStatusExample]:
-    dataset_path = Path(path)
-    if not dataset_path.exists():
-        raise FileNotFoundError(f"Dataset file not found: {dataset_path}")
-
-    examples: list[EmailStatusExample] = []
-    with dataset_path.open("r", encoding="utf-8") as handle:
-        for line_number, raw_line in enumerate(handle, start=1):
-            line = raw_line.strip()
-            if not line:
-                continue
-            try:
-                row = json.loads(line)
-            except json.JSONDecodeError as exc:
-                raise ValueError(f"{dataset_path}:{line_number} is not valid JSON") from exc
-
-            label = _extract_label(row)
-            if require_label and label is None:
-                raise ValueError(f"{dataset_path}:{line_number} is missing a label/status field")
-            if label is not None and label not in LABEL_TO_ID:
-                raise ValueError(
-                    f"{dataset_path}:{line_number} has unsupported label {label!r}; "
-                    f"expected one of {LABELS}"
-                )
-
-            examples.append(
-                EmailStatusExample(
-                    subject=str(row.get("subject") or ""),
-                    sender=str(row.get("sender") or row.get("from") or ""),
-                    body=str(row.get("body") or row.get("text") or ""),
-                    label=label,
-                    metadata={key: value for key, value in row.items() if key not in {"subject", "sender", "from", "body", "text", "label", "status"}},
-                )
-            )
-
-    if not examples:
-        raise ValueError(f"Dataset file has no examples: {dataset_path}")
-    return examples
-
-
-class SklearnTfidfVectorizer:
-    name = "sklearn"
-
-    def __init__(self, max_features: int):
-        from sklearn.feature_extraction.text import TfidfVectorizer
-
-        self.vectorizer = TfidfVectorizer(
-            max_features=max_features,
-            ngram_range=(1, 2),
-            min_df=1,
-            strip_accents="unicode",
-            lowercase=True,
-            sublinear_tf=True,
-        )
-
-    def fit_transform(self, texts: Sequence[str]):
-        return self.vectorizer.fit_transform(texts)
-
-    def transform(self, texts: Sequence[str]):
-        return self.vectorizer.transform(texts)
-
-
-class TensorFlowTfidfVectorizer:
-    name = "tensorflow"
-
-    def __init__(self, max_features: int):
-        import tensorflow as tf
-
-        # max_features = max vocabulary size.
-        # Example: 12000 means keep up to 12k useful words/tokens from training emails.
-        self.max_features = max_features
-        self._tf = tf
-        self.vectorizer = self._build_layer()
-
-    def __getstate__(self) -> dict[str, Any]:
-        # pickle cannot safely store the whole TensorFlow layer state by itself.
-        # So we manually save the two things needed for future predictions:
-        # 1. vocabulary: word -> vector position
-        # 2. idf_weights: word importance for TF-IDF
-        idf_weights = None
-        lookup_layer = getattr(self.vectorizer, "_lookup_layer", None)
-        if lookup_layer is not None and getattr(lookup_layer, "idf_weights", None) is not None:
-            idf_weights = lookup_layer.idf_weights.numpy().tolist()
-        return {
-            "max_features": self.max_features,
-            "vocabulary": self.vectorizer.get_vocabulary(),
-            "idf_weights": idf_weights,
-        }
-
-    def __setstate__(self, state: dict[str, Any]) -> None:
-        import tensorflow as tf
-
-        # Rebuild the TensorFlow vectorizer when model.pkl is loaded.
-        # Then put back the exact same vocabulary + IDF weights from training.
-        self.max_features = state["max_features"]
-        self._tf = tf
-        self.vectorizer = self._build_layer()
-        idf_weights = state.get("idf_weights")
-        if idf_weights is not None:
-            self.vectorizer.set_vocabulary(
-                state["vocabulary"],
-                idf_weights=self._tf.constant(idf_weights, dtype=self._tf.float32),
-            )
-        else:
-            self.vectorizer.set_vocabulary(state["vocabulary"])
-
-    def _build_layer(self):
-        # output_mode="tf_idf" means the vector is not just word count.
-        # It gives more weight to words that are important/rare across emails.
-        # ngrams=(1, 2) keeps single words and two-word phrases.
-        # That matters for this task because "moving forward", "job alert",
-        # and "application received" carry more meaning than each word alone.
-        return self._tf.keras.layers.TextVectorization(
-            max_tokens=self.max_features,
-            ngrams=(1, 2),
-            output_mode="tf_idf",
-            standardize="lower_and_strip_punctuation",
-        )
-
-    def fit_transform(self, texts: Sequence[str]):
-        # Training only:
-        # adapt() reads the training emails and learns vocabulary + IDF weights.
-        # batch(128) just feeds emails to TensorFlow in chunks instead of one by one.
-        dataset = self._tf.data.Dataset.from_tensor_slices(list(texts)).batch(128)
-        self.vectorizer.adapt(dataset)
-        return self.transform(texts)
-
-    def transform(self, texts: Sequence[str]):
-        # Prediction/test:
-        # use the vocabulary + IDF already learned by adapt().
-        # Do not call adapt() here, or vector positions would change.
-        features = self.vectorizer(self._tf.constant(list(texts)))
-        return features.numpy()
-
-
-class EmailStatusClassifier:
-    """Serializable RandomForest email status classifier."""
-
-    def __init__(self, vectorizer: Any, model: Any):
-        self.vectorizer = vectorizer
-        self.model = model
-
-    def predict_texts(self, texts: Sequence[str]) -> list[str]:
-        # Text -> TF-IDF vector -> RandomForest class id -> label name.
-        features = self.vectorizer.transform(texts)
-        raw_ids = self.model.predict(features)
-        return [ID_TO_LABEL[int(label_id)] for label_id in raw_ids]
-
-    def predict_email(
-        self,
-        subject: str = "",
-        sender: str = "",
-        body: str = "",
-    ) -> str:
-        text = combine_email_fields(subject=subject, sender=sender, body=body)
-        return self.predict_texts([text])[0]
-
-    def save(self, path: str | Path) -> None:
-        with Path(path).open("wb") as handle:
-            pickle.dump(self, handle)
-
-    @classmethod
-    def load(cls, path: str | Path) -> "EmailStatusClassifier":
-        with Path(path).open("rb") as handle:
-            loaded = pickle.load(handle)
-        if not isinstance(loaded, cls):
-            raise TypeError(f"Artifact at {path} is not an EmailStatusClassifier")
-        return loaded
-
-
-def build_vectorizer(choice: str, max_features: int):
-    normalized = choice.lower()
-    if normalized == "sklearn":
-        return SklearnTfidfVectorizer(max_features=max_features), None
-    if normalized == "tensorflow":
-        try:
-            return TensorFlowTfidfVectorizer(max_features=max_features), None
-        except ImportError as exc:
-            return None, (
-                "TensorFlow is not installed, so Keras TextVectorization cannot be used. "
-                "Install TensorFlow separately or run with --vectorizer sklearn."
-            )
-    if normalized == "auto":
-        try:
-            return TensorFlowTfidfVectorizer(max_features=max_features), None
-        except ImportError:
-            return SklearnTfidfVectorizer(max_features=max_features), (
-                "TensorFlow is not installed; using scikit-learn TF-IDF fallback."
-            )
-    raise ValueError("vectorizer must be one of: auto, tensorflow, sklearn")
-
-
-def labels_to_ids(labels: Iterable[str]) -> list[int]:
-    return [LABEL_TO_ID[label] for label in labels]
-
-
-def ids_to_labels(label_ids: Iterable[int]) -> list[str]:
-    return [ID_TO_LABEL[int(label_id)] for label_id in label_ids]
-
-
-def write_json(path: str | Path, payload: dict[str, Any]) -> None:
-    with Path(path).open("w", encoding="utf-8") as handle:
-        json.dump(payload, handle, indent=2, sort_keys=True)
-        handle.write("\n")
-
-
-def _extract_label(row: dict[str, Any]) -> str | None:
-    raw_label = row.get("label", row.get("status"))
-    if raw_label is None:
-        return None
-    label = str(raw_label).strip()
-    aliases = {
-        "not_relevant": "Not Relevant",
-        "not relevant": "Not Relevant",
-        "irrelevant": "Not Relevant",
-        "applied": "Applied",
-        "in_progress": "InProgress",
-        "inprogress": "InProgress",
-        "in progress": "InProgress",
-        "interview": "InProgress",
-        "assessment": "InProgress",
-        "rejected": "Rejected",
-        "rejection": "Rejected",
-    }
-    return aliases.get(label.lower(), label)
-
-
-def _clean_text(value: str) -> str:
-    return re.sub(r"\s+", " ", str(value or "")).strip()

From 216c50bb86189434f2110df6781c5e240664f948 Mon Sep 17 00:00:00 2001
From: madhvantyagi <90159839+madhvantyagi@users.noreply.github.com>
Date: Fri, 15 May 2026 01:21:50 -0400
Subject: [PATCH 4/5] Delete engine/email_status_classifier/__init__.py

---
 engine/email_status_classifier/__init__.py | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 engine/email_status_classifier/__init__.py

diff --git a/engine/email_status_classifier/__init__.py b/engine/email_status_classifier/__init__.py
deleted file mode 100644
index 8b13789..0000000
--- a/engine/email_status_classifier/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-

From 3724b12ee13d67b6dfa45ed03139f41e55d03932 Mon Sep 17 00:00:00 2001
From: madhvantyagi <90159839+madhvantyagi@users.noreply.github.com>
Date: Fri, 15 May 2026 01:23:13 -0400
Subject: [PATCH 5/5] Delete scripts/train_email_status_classifier.py

---
 scripts/train_email_status_classifier.py | 339 -----------------------
 1 file changed, 339 deletions(-)
 delete mode 100644 scripts/train_email_status_classifier.py

diff --git a/scripts/train_email_status_classifier.py b/scripts/train_email_status_classifier.py
deleted file mode 100644
index 0fb7676..0000000
--- a/scripts/train_email_status_classifier.py
+++ /dev/null
@@ -1,339 +0,0 @@
-#!/usr/bin/env python3
-"""Train or use the job email status classifier.
-
-Simple pipeline:
-1. Read labeled emails from JSONL.
-2. Combine subject + sender + body into one text string.
-3. Convert text into TF-IDF vectors.
-4. Train RandomForest on those vectors.
-5. Save vectorizer + model together in model.pkl.
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import sys
-from pathlib import Path
-from typing import Any
-
-
-# Let this script import the local engine package when run from repo root.
-PROJECT_ROOT = Path(__file__).resolve().parents[1]
-if str(PROJECT_ROOT) not in sys.path:
-    sys.path.insert(0, str(PROJECT_ROOT))
-
-
-try:
-    from sklearn.ensemble import RandomForestClassifier
-    from sklearn.metrics import (
-        accuracy_score,
-        classification_report,
-        confusion_matrix,
-        precision_recall_fscore_support,
-    )
-except ImportError as exc:
-    raise SystemExit(
-        "Missing ML dependency. Run:\n"
-        "  pip install -r requirements.txt\n\n"
-        f"Import error: {exc}"
-    ) from exc
-
-
-from engine.email_status_classifier import LABELS, EmailStatusClassifier, load_jsonl_dataset
-from engine.email_status_classifier.classifier import (
-    build_vectorizer,
-    combine_email_fields,
-    ids_to_labels,
-    labels_to_ids,
-    write_json,
-)
-
-
-DEFAULT_TRAIN_PATH = PROJECT_ROOT / "data" / "email_status_dataset" / "train.jsonl"
-DEFAULT_TEST_PATH = PROJECT_ROOT / "data" / "email_status_dataset" / "test.jsonl"
-DEFAULT_OUTPUT_DIR = PROJECT_ROOT / "models" / "email_status_classifier"
-
-
-# =============================================================================
-# CLI params
-# =============================================================================
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Train, evaluate, or run the job email status classifier."
-    )
-
-    # Dataset paths. Each JSONL row should have subject, sender, body, and label.
-    parser.add_argument("--train-path", type=Path, default=DEFAULT_TRAIN_PATH)
-    parser.add_argument("--test-path", type=Path, default=DEFAULT_TEST_PATH)
-    parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT_DIR)
-
-    # Text vectorizer.
-    # tensorflow = TensorFlow TextVectorization TF-IDF.
-    # sklearn = scikit-learn TfidfVectorizer fallback.
-    parser.add_argument("--vectorizer", choices=["tensorflow", "sklearn", "auto"], default="tensorflow")
-    parser.add_argument(
-        "--max-features",
-        type=int,
-        default=12000,
-        help="Maximum vocabulary size. Bigger keeps more words, but can add noise.",
-    )
-
-    # RandomForest params.
-    parser.add_argument("--seed", type=int, default=42, help="Makes training repeatable.")
-    parser.add_argument(
-        "--n-estimators",
-        type=int,
-        default=300,
-        help="Number of trees. More trees are slower but usually more stable.",
-    )
-    parser.add_argument(
-        "--max-depth",
-        type=optional_int,
-        default=None,
-        help="Tree depth. None lets trees grow fully; smaller values reduce overfit.",
-    )
-    parser.add_argument(
-        "--min-samples-leaf",
-        type=int,
-        default=1,
-        help="Minimum rows in a final tree leaf. Higher values reduce overfit.",
-    )
-    parser.add_argument(
-        "--class-weight",
-        default="balanced",
-        help='Use "balanced" when classes may be uneven; use "none" to disable.',
-    )
-
-    # Inference mode. If any of these are present, the script predicts instead of training.
-    parser.add_argument("--predict-text", default=None)
-    parser.add_argument("--subject", default="")
-    parser.add_argument("--sender", default="")
-    parser.add_argument("--body", default="")
-    parser.add_argument("--model-path", type=Path, default=None)
-
-    return parser.parse_args()
-
-
-# =============================================================================
-# Main switch: train mode or prediction mode
-# =============================================================================
-
-
-def main() -> int:
-    args = parse_args()
-    model_path = args.model_path or args.output_dir / "model.pkl"
-
-    if args.predict_text is not None or args.subject or args.sender or args.body:
-        predict_one_email(args, model_path)
-    else:
-        train_and_evaluate(args, model_path)
-
-    return 0
-
-
-# =============================================================================
-# Training
-# =============================================================================
-
-
-def train_and_evaluate(args: argparse.Namespace, model_path: Path) -> None:
-    # 1. Read dataset rows.
-    train_examples = load_jsonl_dataset(args.train_path)
-    test_examples = load_jsonl_dataset(args.test_path)
-
-    # 2. Convert each row into one text field:
-    #    "Subject: ...\nSender: ...\nBody: ..."
-    train_texts = [example.text for example in train_examples]
-    test_texts = [example.text for example in test_examples]
-
-    # 3. Convert labels into ids RandomForest can train on.
-    #    Not Relevant -> 0, Applied -> 1, InProgress -> 2, Rejected -> 3.
-    y_train = labels_to_ids([example.label or "" for example in train_examples])
-    y_test = labels_to_ids([example.label or "" for example in test_examples])
-
-    # 4. Build text vectorizer.
-    #    fit_transform() learns vocabulary/IDF from training emails and converts train text.
-    #    transform() converts test emails using the same learned vocabulary/IDF.
-    vectorizer, warning = build_vectorizer(args.vectorizer, args.max_features)
-    if vectorizer is None:
-        raise SystemExit(warning)
-    if warning:
-        print(f"Note: {warning}", file=sys.stderr)
-
-    x_train = vectorizer.fit_transform(train_texts)
-    x_test = vectorizer.transform(test_texts)
-
-    # 5. Train RandomForest.
-    #    RandomForest sees only numeric TF-IDF vectors, not raw text.
-    model = RandomForestClassifier(
-        n_estimators=args.n_estimators,
-        max_depth=args.max_depth,
-        min_samples_leaf=args.min_samples_leaf,
-        class_weight=parse_class_weight(args.class_weight),
-        random_state=args.seed,
-        n_jobs=-1,
-    )
-    model.fit(x_train, y_train)
-
-    # 6. Predict test data.
-    #    This is pure model output. No rule-based postprocessing is mixed in.
-    raw_pred_ids = model.predict(x_test)
-    predicted_labels = ids_to_labels(raw_pred_ids)
-    predicted_ids = labels_to_ids(predicted_labels)
-
-    # 7. Save everything needed later:
-    #    model.pkl contains both vectorizer + trained RandomForest.
-    classifier = EmailStatusClassifier(vectorizer=vectorizer, model=model)
-    args.output_dir.mkdir(parents=True, exist_ok=True)
-    classifier.save(model_path)
-
-    metrics = build_metrics(y_test, predicted_ids, args, vectorizer.name, warning)
-    write_json(args.output_dir / "metrics.json", metrics)
-    write_json(args.output_dir / "label_mapping.json", build_label_mapping())
-    write_confusion_matrix(args.output_dir / "confusion_matrix.csv", y_test, predicted_ids)
-    write_predictions(args.output_dir / "predictions.jsonl", test_examples, predicted_labels)
-
-    print(f"Saved model: {model_path}")
-    print(f"Accuracy: {metrics['accuracy']:.4f}")
-    print(f"Macro F1: {metrics['macro']['f1']:.4f}")
-
-
-# =============================================================================
-# Prediction
-# =============================================================================
-
-
-def predict_one_email(args: argparse.Namespace, model_path: Path) -> None:
-    if not model_path.exists():
-        raise SystemExit(f"Model artifact not found: {model_path}")
-
-    classifier = EmailStatusClassifier.load(model_path)
-
-    if args.predict_text is not None:
-        text = args.predict_text
-    else:
-        text = combine_email_fields(args.subject, args.sender, args.body)
-
-    prediction = classifier.predict_texts([text])[0]
-    print(prediction)
-
-
-# =============================================================================
-# Output files
-# =============================================================================
-
-
-def build_metrics(
-    y_true: list[int],
-    y_pred: list[int],
-    args: argparse.Namespace,
-    vectorizer_name: str,
-    vectorizer_warning: str | None,
-) -> dict[str, Any]:
-    macro_precision, macro_recall, macro_f1, _ = precision_recall_fscore_support(
-        y_true,
-        y_pred,
-        labels=list(range(len(LABELS))),
-        average="macro",
-        zero_division=0,
-    )
-    per_class = classification_report(
-        y_true,
-        y_pred,
-        labels=list(range(len(LABELS))),
-        target_names=LABELS,
-        output_dict=True,
-        zero_division=0,
-    )
-
-    return {
-        "labels": LABELS,
-        "accuracy": float(accuracy_score(y_true, y_pred)),
-        "macro": {
-            "precision": float(macro_precision),
-            "recall": float(macro_recall),
-            "f1": float(macro_f1),
-        },
-        "per_class": {
-            label: {
-                "precision": float(per_class[label]["precision"]),
-                "recall": float(per_class[label]["recall"]),
-                "f1": float(per_class[label]["f1-score"]),
-                "support": int(per_class[label]["support"]),
-            }
-            for label in LABELS
-        },
-        "training": {
-            "train_path": str(args.train_path),
-            "test_path": str(args.test_path),
-            "vectorizer_used": vectorizer_name,
-            "vectorizer_warning": vectorizer_warning,
-            "max_features": args.max_features,
-            "n_estimators": args.n_estimators,
-            "max_depth": args.max_depth,
-            "min_samples_leaf": args.min_samples_leaf,
-            "class_weight": args.class_weight,
-            "seed": args.seed,
-            "postprocessing": "none",
-        },
-    }
-
-
-def write_confusion_matrix(path: Path, y_true: list[int], y_pred: list[int]) -> None:
-    matrix = confusion_matrix(y_true, y_pred, labels=list(range(len(LABELS))))
-
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.writer(handle)
-        writer.writerow(["actual\\predicted", *LABELS])
-        for label, row in zip(LABELS, matrix):
-            writer.writerow([label, *[int(value) for value in row]])
-
-
-def write_predictions(
-    path: Path,
-    examples: list[Any],
-    predicted_labels: list[str],
-) -> None:
-    with path.open("w", encoding="utf-8") as handle:
-        for example, predicted_label in zip(examples, predicted_labels):
-            row = {
-                "subject": example.subject,
-                "sender": example.sender,
-                "true_label": example.label,
-                "prediction": predicted_label,
-            }
-            if example.metadata:
-                row["metadata"] = example.metadata
-            handle.write(json.dumps(row, sort_keys=True) + "\n")
-
-
-# =============================================================================
-# Tiny parsing helpers
-# =============================================================================
-
-
-def optional_int(value: str) -> int | None:
-    if value.lower() in {"none", "null", ""}:
-        return None
-    return int(value)
-
-
-def parse_class_weight(value: str) -> str | None:
-    if value.lower() in {"none", "null", ""}:
-        return None
-    return value
-
-
-def build_label_mapping() -> dict[str, Any]:
-    return {
-        "labels": LABELS,
-        "label_to_id": {label: index for index, label in enumerate(LABELS)},
-    }
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())