Create sample.py

sample.py

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+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# --- Configuration ---
+MODEL_PATH = 'weapon_classifier_final_tuned.keras'
+IMAGE_PATH = './test_image.jpg' 
+IMG_SIZE = (224, 224)
+# --- End Configuration ---
+
+def load_and_preprocess_image(img_path, target_size):
+    """Loads, resizes, and normalizes the image for prediction."""
+    img = image.load_img(img_path, target_size=target_size)
+    img_array = image.img_to_array(img)
+    # Add batch dimension: (H, W, C) -> (1, H, W, C)
+    img_array = np.expand_dims(img_array, axis=0)
+    # Normalize pixel values (0-255 -> 0-1)
+    processed_image = img_array / 255.0
+    return processed_image
+
+def classify_image(model_path, image_path, img_size):
+    """Loads the model, makes a prediction, and interprets the result."""
+    
+    # Load the model
+    model = load_model(model_path)
+    print("Model loaded successfully.")
+
+    # Preprocess the image
+    input_image = load_and_preprocess_image(image_path, img_size)
+
+    # Make the prediction
+    prediction = model.predict(input_image)
+
+    # Interpret the result for binary classification
+    probability = prediction[0][0]
+    class_names = {0: 'Not a Weapon', 1: 'Weapon'}
+
+    if probability >= 0.5:
+        predicted_class = class_names[1]
+        confidence = probability * 100
+    else:
+        predicted_class = class_names[0]
+        confidence = (1 - probability) * 100 
+
+    print("\n--- CLASSIFICATION RESULT ---")
+    print(f"Image: {os.path.basename(image_path)}")
+    print(f"Predicted Class: **{predicted_class}**")
+    print(f"Confidence: **{confidence:.2f}%**")
+    print("---------------------------\n")
+
+# --- EXECUTE CLASSIFICATION ---
+import os
+classify_image(MODEL_PATH, IMAGE_PATH, IMG_SIZE)