diff --git a/8_prediction.py b/8_prediction.py
new file mode 100644
index 0000000..3f435e9
--- /dev/null
+++ b/8_prediction.py
@@ -0,0 +1,62 @@
+import os
+import torch
+from torch.utils.data import DataLoader
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from Network.MyDataset import *
+from utils.train_val_lr import train, validate, adjust_learning_rate
+from Network.Model import *
+from scipy.signal import butter, lfilter, sosfilt
+from config import Network_prediction_Config as cfg
+from config import Path_Config as pcfg
+from utils.plot import plot_permittivity_constant
+
+# Set parameters
+BATCH_SIZE = cfg.BATCH_SIZE
+TEST_FILE = pcfg.PROCESSED_TEST_FILE
+inversion_time_result_file=pcfg.inversion_time_result_file
+inversion_time_result_img= pcfg.inversion_time_result_img
+MODEL_PATH = pcfg.BEST_MODEL_PATH
+impulse_field_file = pcfg.field_impulse
+impulse_sim_file = pcfg.sim_impulse
+smooth_window_size = cfg.smooth_window_size
+max_permittivity=cfg.max_permittvity
+initial_params = cfg.initial_params
+
+
+# Load the pre-trained model
+model = Model(inplanes=2, outplanes=1, layers=cfg.network_layers).cuda()
+model.load_state_dict(torch.load(MODEL_PATH))
+model.eval()
+
+# Load dataset
+dataset = MyDataset(TEST_FILE, TEST_FILE, impulse_field_file, impulse_sim_file, mode='apply', initial_params=initial_params)
+test_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=cfg.num_workers, pin_memory=True)
+
+# Initialize results container
+results = []
+
+# Inference without gradient tracking
+with torch.no_grad():
+    for idx, (data_data, label) in enumerate(test_loader):
+        data_data = data_data.cuda()
+        
+        # Model inference
+        data_data_out = model(data_data.type(torch.cuda.FloatTensor))
+        
+        # Move output to CPU and squeeze dimensions
+        data_data_out = data_data_out.cpu().numpy().squeeze()
+        results.append(data_data_out)
+
+# Concatenate and save final results
+final_result = np.column_stack(results)
+
+# Apply smoothing filter (moving average)
+final_result = np.apply_along_axis(lambda x: np.convolve(x, np.ones(smooth_window_size)/smooth_window_size, mode='valid'), axis=1, arr=final_result)
+pd.DataFrame(final_result* max_permittivity).to_csv(inversion_time_result_file, index=False)
+# Plot results
+plot_permittivity_constant(final_result * max_permittivity, inversion_time_result_img, line_length=cfg.distance, time_length=cfg.time_window)
+
+
+