Update model/train.py

Fixed the file paths to correctly point to `tmp` folder as the original paths are read-only

model/train.py

@@ -1,76 +1,85 @@
-import pandas as pd
-from pathlib import Path
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.linear_model import LogisticRegression
-from sklearn.metrics import accuracy_score
-from sklearn.model_selection import train_test_split
-import joblib
-import json
-import datetime
-import hashlib
-
-# Paths
-BASE_DIR = Path(__file__).resolve().parent
-DATA_PATH = BASE_DIR.parent / "data" / "combined_dataset.csv"
-MODEL_PATH = BASE_DIR / "model.pkl"
-VECTORIZER_PATH = BASE_DIR / "vectorizer.pkl"
-METADATA_PATH = BASE_DIR / "metadata.json"
-
-def hash_file(filepath):
-    content = Path(filepath).read_bytes()
-    return hashlib.md5(content).hexdigest()
-
-def main():
-    # Load dataset
-    # print('Dataset Loaded')
-    df = pd.read_csv(DATA_PATH)
-    X = df['text']
-    y = df['label']
-
-    # Train-test split
-    X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=42)
-
-    # Vectorize
-    vectorizer = TfidfVectorizer(stop_words='english', max_features=5000)
-    X_train_vec = vectorizer.fit_transform(X_train)
-    X_test_vec = vectorizer.transform(X_test)
-
-    # print('Train/Test Splits Created')
-    # print('Starting Model Training')
-
-    # Train model
-    model = LogisticRegression(max_iter=1000)
-    model.fit(X_train_vec, y_train)
-
-    # print('Model Training Completed')
-     #print('Model Evaluation Starting!')
-
-    # Evaluate
-    y_pred = model.predict(X_test_vec)
-    acc = accuracy_score(y_test, y_pred)
-
-    # Save model + vectorizer
-    joblib.dump(model, MODEL_PATH)
-    joblib.dump(vectorizer, VECTORIZER_PATH)
-
-    # print('Model Evaluation Done')
-    # print('Model Saved!')
-
-    # Save metadata
-    metadata = {
-        "model_version": f"v1.0",
-        "data_version": hash_file(DATA_PATH),
-        "train_size": len(X_train),
-        "test_size": len(X_test),
-        "test_accuracy": round(acc, 4),
-        "timestamp": datetime.datetime.now().isoformat()
-    }
-    with open(METADATA_PATH, 'w') as f:
-        json.dump(metadata, f, indent=4)
-
-    print(f"✅ Model trained and saved.")
-    print(f"📊 Test Accuracy: {acc:.4f}")
-    print(f"📝 Metadata saved to {METADATA_PATH}")
-
-if __name__ == "__main__":
-    main()
+import pandas as pd
+from pathlib import Path
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+import joblib
+import json
+import datetime
+import hashlib
+
+# # Paths
+# BASE_DIR = Path(__file__).resolve().parent
+# DATA_PATH = BASE_DIR.parent / "data" / "combined_dataset.csv"
+# MODEL_PATH = BASE_DIR / "model.pkl"
+# VECTORIZER_PATH = BASE_DIR / "vectorizer.pkl"
+# METADATA_PATH = BASE_DIR / "metadata.json"
+
+# Base dir and data location inside /tmp
+BASE_DIR = Path("/tmp")
+DATA_PATH = BASE_DIR / "data" / "combined_dataset.csv"
+
+# Model artifacts also in /tmp (or you can keep these in /app/model if you want to persist them in the container)
+MODEL_PATH = BASE_DIR / "model.pkl"
+VECTORIZER_PATH = BASE_DIR / "vectorizer.pkl"
+METADATA_PATH = BASE_DIR / "metadata.json"
+
+def hash_file(filepath):
+    content = Path(filepath).read_bytes()
+    return hashlib.md5(content).hexdigest()
+
+def main():
+    # Load dataset
+    # print('Dataset Loaded')
+    df = pd.read_csv(DATA_PATH)
+    X = df['text']
+    y = df['label']
+
+    # Train-test split
+    X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=42)
+
+    # Vectorize
+    vectorizer = TfidfVectorizer(stop_words='english', max_features=5000)
+    X_train_vec = vectorizer.fit_transform(X_train)
+    X_test_vec = vectorizer.transform(X_test)
+
+    # print('Train/Test Splits Created')
+    # print('Starting Model Training')
+
+    # Train model
+    model = LogisticRegression(max_iter=1000)
+    model.fit(X_train_vec, y_train)
+
+    # print('Model Training Completed')
+     #print('Model Evaluation Starting!')
+
+    # Evaluate
+    y_pred = model.predict(X_test_vec)
+    acc = accuracy_score(y_test, y_pred)
+
+    # Save model + vectorizer
+    joblib.dump(model, MODEL_PATH)
+    joblib.dump(vectorizer, VECTORIZER_PATH)
+
+    # print('Model Evaluation Done')
+    # print('Model Saved!')
+
+    # Save metadata
+    metadata = {
+        "model_version": f"v1.0",
+        "data_version": hash_file(DATA_PATH),
+        "train_size": len(X_train),
+        "test_size": len(X_test),
+        "test_accuracy": round(acc, 4),
+        "timestamp": datetime.datetime.now().isoformat()
+    }
+    with open(METADATA_PATH, 'w') as f:
+        json.dump(metadata, f, indent=4)
+
+    print(f"✅ Model trained and saved.")
+    print(f"📊 Test Accuracy: {acc:.4f}")
+    print(f"📝 Metadata saved to {METADATA_PATH}")
+
+if __name__ == "__main__":
+    main()