Update app.py

app.py

@@ -21,11 +21,11 @@ class SwarmAgent:
         self.v = np.zeros_like(position)
 
 class SwarmNeuralNetwork:
-    def __init__(self, num_agents, image_shape, target_image):
+    def __init__(self, num_agents, image_shape, target_image_path):
         self.image_shape = image_shape
         self.resized_shape = (256, 256, 3)  # High 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.target_image = self.load_target_image(target_image_path)
         self.generated_image = np.random.randn(*image_shape)  # Start with noise
         self.mobilenet = self.load_mobilenet_model()
         self.current_epoch = 0
@@ -166,9 +166,20 @@ class SwarmNeuralNetwork:
         self.generate_image()
         return self.generated_image
 
+    def adjust_limbs(self, arm_position, leg_position):
+        # Logic to adjust arm and leg positions in the target image
+        # For simplicity, let's assume arm_position and leg_position range from -100 to 100
+        arm_shift = arm_position / 100.0 * 0.2  # Scale to a reasonable range
+        leg_shift = leg_position / 100.0 * 0.2  # Scale to a reasonable range
+
+        # Translate the positions of the arms and legs in the image
+        for agent in self.agents:
+            agent.position[50:100, 50:200, :] += arm_shift  # Example adjustment
+            agent.position[150:200, 50:200, :] += leg_shift  # Example adjustment
+
 # Gradio Interface
 def train_snn(image_path, num_agents, epochs, arm_position, leg_position, brightness, contrast, color):
-    snn = SwarmNeuralNetwork(num_agents=num_agents, image_shape=(256, 256, 3), target_image=image_path)  # High resolution
+    snn = SwarmNeuralNetwork(num_agents=num_agents, image_shape=(256, 256, 3), target_image_path=image_path)  # High resolution
     
     # Apply user-specified adjustments to the target image
     image = Image.open(image_path)
@@ -176,18 +187,18 @@ def train_snn(image_path, num_agents, epochs, arm_position, leg_position, bright
     image = ImageEnhance.Contrast(image).enhance(contrast)
     image = ImageEnhance.Color(image).enhance(color)
     
-    # Mock adjustment for arm and leg positions (to be implemented with actual logic)
-    # For now, we just log the values
-    print(f"Adjusting arm position: {arm_position}, leg position: {leg_position}")
+    snn.target_image = snn.load_target_image(image_path)
+    
+    # Adjust limb positions based on slider values
+    snn.adjust_limbs(arm_position, leg_position)
     
-    snn.target_image = snn.load_target_image(image)
     snn.train(epochs=epochs)
     snn.save_model('snn_model.npy')
     generated_image = snn.generated_image
     return generated_image
 
 def generate_new_image():
-    snn = SwarmNeuralNetwork(num_agents=2000, image_shape=(256, 256, 3), target_image=None)  # High resolution and optimized number of agents
+    snn = SwarmNeuralNetwork(num_agents=2000, image_shape=(256, 256, 3), target_image_path=None)  # High resolution and optimized number of agents
     snn.load_model('snn_model.npy')
     new_image = snn.generate_new_image()
     return new_image
@@ -195,4 +206,18 @@ def generate_new_image():
 interface = gr.Interface(
     fn=train_snn,
     inputs=[
-        gr.Image(type="filepath", label
+        gr.Image(type="filepath", label="Upload Target Image"),
+        gr.Slider(minimum=500, maximum=2000, value=1000, label="Number of Agents"),  # Adjusted range for number of agents
+        gr.Slider(minimum=10, maximum=100, value=50, label="Number of Epochs"),  # Adjusted range for number of epochs
+        gr.Slider(minimum=-100, maximum=100, value=0, label="Arm Position"),
+        gr.Slider(minimum=-100, maximum=100, value=0, label="Leg Position"),
+        gr.Slider(minimum=0.5, maximum=2.0, value=1.0, label="Brightness"),
+        gr.Slider(minimum=0.5, maximum=2.0, value=1.0, label="Contrast"),
+        gr.Slider(minimum=0.5, maximum=2.0, value=1.0, label="Color Balance")
+    ],
+    outputs=gr.Image(type="numpy", label="Generated Image"),
+    title="Swarm Neural Network Image Generation",
+    description="Upload an image and set the number of agents and epochs to train the Swarm Neural Network to generate a new image. Adjust arm and leg positions, brightness, contrast, and color balance for personalization."
+)
+
+interface.launch()