Update app.py

app.py

@@ -6,7 +6,7 @@ from keras.models import Model
 import matplotlib.pyplot as plt
 import logging
 from skimage.transform import resize
-from PIL import Image, ImageEnhance
+from PIL import Image, ImageEnhance, ImageFilter
 from tqdm import tqdm
 
 class SwarmAgent:
@@ -19,7 +19,7 @@ class SwarmAgent:
 class SwarmNeuralNetwork:
     def __init__(self, num_agents, image_shape, target_image):
         self.image_shape = image_shape
-        self.resized_shape = (64, 64, 3)
+        self.resized_shape = (128, 128, 3)  # Increased resolution
         self.agents = [SwarmAgent(self.random_position(), self.random_velocity()) for _ in range(num_agents)]
         self.target_image = self.load_target_image(target_image)
         self.generated_image = np.random.randn(*image_shape)  # Start with noise
@@ -105,8 +105,9 @@ class SwarmNeuralNetwork:
         
         # Apply sharpening filter
         image_pil = Image.fromarray((self.generated_image * 255).astype(np.uint8))
-        enhancer = ImageEnhance.Sharpness(image_pil)
-        self.generated_image = np.array(enhancer.enhance(2.0)) / 255.0
+        image_pil = image_pil.filter(ImageFilter.SHARPEN)
+        image_pil = image_pil.filter(ImageFilter.DETAIL)
+        self.generated_image = np.array(image_pil) / 255.0
 
     def train(self, epochs):
         logging.basicConfig(filename='training.log', level=logging.INFO)
@@ -163,7 +164,7 @@ class SwarmNeuralNetwork:
 
 # Gradio Interface
 def train_snn(image, num_agents, epochs, brightness, contrast, color):
-    snn = SwarmNeuralNetwork(num_agents=num_agents, image_shape=(64, 64, 3), target_image=image)
+    snn = SwarmNeuralNetwork(num_agents=num_agents, image_shape=(128, 128, 3), target_image=image)
     
     # Apply user-specified adjustments to the target image
     image = ImageEnhance.Brightness(image).enhance(brightness)
@@ -176,7 +177,7 @@ def train_snn(image, num_agents, epochs, brightness, contrast, color):
     return snn.generated_image
 
 def generate_new_image():
-    snn = SwarmNeuralNetwork(num_agents=2000, image_shape=(64, 64, 3), target_image=None)
+    snn = SwarmNeuralNetwork(num_agents=2000, image_shape=(128, 128, 3), target_image=None)
     snn.load_model('snn_model.npy')
     new_image = snn.generate_new_image()
     return new_image