Update model/random_forest.pkl

model/random_forest.pkl

@@ -1,49 +1,36 @@
+# Re-run this corrected version of train_model.py
 import pandas as pd
-import numpy as np
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import train_test_split
 import joblib
 import os
 
-# Sample dataset (replace with your actual data)
+# Create fresh data
 data = {
-    'face_shape': ['Oval', 'Round', 'Square', 'Oval', 'Round', 'Square'] * 10,
-    'skin_tone': ['Fair', 'Medium', 'Dark', 'Medium', 'Dark', 'Fair'] * 10,
-    'face_size': ['Small', 'Medium', 'Large', 'Medium', 'Large', 'Small'] * 10,
-    'mask_style': ['Glitter', 'Animal', 'Floral', 'Animal', 'Glitter', 'Floral'] * 10
+    'face_shape': ['Oval', 'Round', 'Square'] * 50,
+    'skin_tone': ['Fair', 'Medium', 'Dark'] * 50,
+    'face_size': ['Small', 'Medium', 'Large'] * 50,
+    'mask_style': ['Glitter', 'Animal', 'Floral'] * 50
 }
 df = pd.DataFrame(data)
 
-# Initialize LabelEncoders
-encoders = {
-    'face_shape': LabelEncoder().fit(df['face_shape']),
-    'skin_tone': LabelEncoder().fit(df['skin_tone']),
-    'face_size': LabelEncoder().fit(df['face_size']),
-    'mask_style': LabelEncoder().fit(df['mask_style'])
-}
-
-# Encode features
-X = pd.DataFrame({
-    'face_shape': encoders['face_shape'].transform(df['face_shape']),
-    'skin_tone': encoders['skin_tone'].transform(df['skin_tone']),
-    'face_size': encoders['face_size'].transform(df['face_size'])
-})
-y = encoders['mask_style'].transform(df['mask_style'])
+# Create and save new encoders
+encoders = {col: LabelEncoder().fit(df[col].unique()) 
+           for col in ['face_shape', 'skin_tone', 'face_size', 'mask_style']}
 
-# Train-test split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# Train Random Forest
+# Train new model
 model = RandomForestClassifier(n_estimators=100, random_state=42)
-model.fit(X_train, y_train)
+model.fit(
+    pd.DataFrame({col: encoders[col].transform(df[col]) 
+                for col in ['face_shape', 'skin_tone', 'face_size']}),
+    encoders['mask_style'].transform(df['mask_style'])
+)
 
-# Create model directory
+# Save properly
 os.makedirs('model', exist_ok=True)
-
-# Save model and encoders
 joblib.dump(model, 'model/random_forest.pkl')
 joblib.dump(encoders, 'model/label_encoders.pkl')
 
-print("Model trained and saved successfully!")
-print(f"Test Accuracy: {model.score(X_test, y_test):.2f}")
+# Add this to your app.py before loading
+print("Current directory:", os.listdir())
+print("Model directory:", os.listdir('model'))