diff --git a/predict.py b/predict.py
deleted file mode 100644
index 3777e19..0000000
--- a/predict.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import tensorflow as tf
-import pandas as pd
-import numpy as np
-import sklearn
-import sklearn.model_selection
-from tensorflow.keras.models import load_model
-from sklearn.metrics import accuracy_score, precision_score, f1_score
-
-feature_cols = ['year', 'mileage', 'vol_engine']
-
-model = load_model('model.h5')
-test_data = pd.read_csv('test.csv')
-
-predictions = model.predict(test_data[feature_cols])
-predicted_prices = [p[0] for p in predictions]
-
-
-results = pd.DataFrame({'id': test_data['id'], 'year': test_data['year'], 'mileage': test_data['mileage'], 'vol_engine': test_data['vol_engine'], 'predicted_price': predicted_prices})
-results.to_csv('predictions.csv', index=False)
-
-y_true = test_data['price']
-y_pred = y_pred = [round(p[0]) for p in predictions]
-
-accuracy = accuracy_score(y_true, y_pred)
-precision = precision_score(y_true, y_pred, average='micro')
-f1 = f1_score(y_true, y_pred, average='micro')
-
-with open('metrics.txt', 'w') as f:
-    f.write(f"Accuracy: {accuracy:.4f}\n")
-    f.write(f"Micro-average Precision: {precision:.4f}\n")
-    f.write(f"Micro-average F1-score: {f1:.4f}\n")