adding predict file

predict.py

@@ -0,0 +1,74 @@
+from os import listdir
+import sys
+from pathlib import Path
+
+import numpy as np
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+
+from typing import Union
+import pandas as pd
+import tensorflow as tf
+
+from plant_leave_diseases_model import __version__ as _version
+from plant_leave_diseases_model.config.core import TRAINED_MODEL_DIR, config
+from plant_leave_diseases_model.processing.data_manager import convert_image_to_array, get_class_file_list, get_model_file_name_path, load_leaf_disease_dataset, load_model, load_test_dataset
+from plant_leave_diseases_model.processing.data_setup import test_directory,class_file_path
+
+
+def make_prediction(*, test_dir_img_file_path) -> dict:
+    """Make a prediction using a saved model """
+    image_list=[]
+    
+    #x_test, y_test = load_leaf_disease_dataset(test_dir_img_file_path)
+    root_dir = listdir(test_dir_img_file_path)
+    for plant_image in root_dir :
+        print("plant_image::",plant_image)
+        img_file_path=str(test_dir_img_file_path)+"/"+str(plant_image)
+        if plant_image.endswith(".jpg") == True or plant_image.endswith(".JPG") == True:
+            image_list.append(convert_image_to_array(img_file_path))
+            
+    np_image_list = np.array(image_list, dtype=np.float16) / config.model_config.scaling_factor
+           
+    print("np_image_list.shape:",np_image_list.shape)
+    results = {"predictions": None, "version": _version}
+    
+    load_model_and_predict(np_image_list) 
+    pred_labels = model_file_name = get_model_file_name_path()
+        
+    results = {"predictions": pred_labels, "version": _version}
+    
+
+    return results
+
+def load_model_and_predict(x_test):
+    model_file_name = get_model_file_name_path()
+    
+    print("loading mode file:",model_file_name)
+    
+    model = load_model(file_name = model_file_name)
+    
+    predictions = model.predict(x_test,verbose = 0)
+    
+    ###########################################
+    # Geting array of master class array
+    ###########################################
+    master_class_arr=get_class_file_list(class_file_path)
+    print("master_class_arr:",master_class_arr)
+    
+    print(predictions)
+    pred_labels = []
+    for pred in predictions:
+        np.multiply(np.array(pred), 100000)
+        max_index = np.multiply(np.array(pred), 100000).argmax()
+        leaf_pred_label=master_class_arr[max_index]
+        print("load_model_and_predict:max_index::",max_index,"master_class_arr:",master_class_arr[max_index])
+        pred_labels.append(leaf_pred_label)
+        
+    return pred_labels    
+    
+if __name__ == "__main__":
+
+    # Define directory where test images are loaded
+    test_dir_img_file_path=test_directory+"/Apple___Apple_scab"
+    print("test_dir_img_file_path::",test_dir_img_file_path) 
+    make_prediction(test_dir_img_file_path = test_dir_img_file_path)