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sai_ai_model_inference_module.py

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+"""
+Self-generated AI Model Component: sai_ai_model_inference_module.py
+Component: inference_module
+Created on: 2025-09-26T12:23:42.435801Z
+"""
+
+import os
+import datetime
+# Add necessary imports here (e.g., torch, tensorflow, transformers)
+
+class InferenceModuleAI:
+    """
+    Class for the inference_module of the AI model.
+    TODO: Implement the specific logic for this component.
+    """
+    def __init__(self, config=None):
+        # TODO: Initialize model components or load configuration
+        print(f"Initializing InferenceModuleAI...")
+        pass
+
+    def run_inference_module(self, data=None):
+        """
+        Method to run the inference_module process.
+        TODO: Implement the core logic for inference_module.
+        """
+        print(f"Running run_inference_module with data: {data}...")
+        # Placeholder for component logic
+        result = f"{component} process completed."
+        return result
+
+# Example usage (for testing the generated module)
+if __name__ == "__main__":
+    print(f"Testing {file_name}")
+    component_instance = InferenceModuleAI()
+    # Example data - replace with actual data
+    sample_data = "sample input data"
+    output = component_instance.run_inference_module(data=sample_data)
+    print(f"Output: {output}")
+

sai_ai_model_model_architecture.py

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+"""
+Self-generated AI Model Component: sai_ai_model_model_architecture.py
+Component: model_architecture
+Created on: 2025-09-26T12:23:42.416222Z
+"""
+
+import os
+import datetime
+# Add necessary imports here (e.g., torch, tensorflow, transformers)
+
+class ModelArchitectureAI:
+    """
+    Class for the model_architecture of the AI model.
+    TODO: Implement the specific logic for this component.
+    """
+    def __init__(self, config=None):
+        # TODO: Initialize model components or load configuration
+        print(f"Initializing ModelArchitectureAI...")
+        pass
+
+    def run_model_architecture(self, data=None):
+        """
+        Method to run the model_architecture process.
+        TODO: Implement the core logic for model_architecture.
+        """
+        print(f"Running run_model_architecture with data: {data}...")
+        # Placeholder for component logic
+        result = f"{component} process completed."
+        return result
+
+# Example usage (for testing the generated module)
+if __name__ == "__main__":
+    print(f"Testing {file_name}")
+    component_instance = ModelArchitectureAI()
+    # Example data - replace with actual data
+    sample_data = "sample input data"
+    output = component_instance.run_model_architecture(data=sample_data)
+    print(f"Output: {output}")
+

sai_ai_model_training_loop.py

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+"""
+Self-generated AI Model Component: sai_ai_model_training_loop.py
+Component: training_loop
+Created on: 2025-09-26T12:23:42.426072Z
+"""
+
+import os
+import datetime
+# Add necessary imports here (e.g., torch, tensorflow, transformers)
+
+class TrainingLoopAI:
+    """
+    Class for the training_loop of the AI model.
+    TODO: Implement the specific logic for this component.
+    """
+    def __init__(self, config=None):
+        # TODO: Initialize model components or load configuration
+        print(f"Initializing TrainingLoopAI...")
+        pass
+
+    def run_training_loop(self, data=None):
+        """
+        Method to run the training_loop process.
+        TODO: Implement the core logic for training_loop.
+        """
+        print(f"Running run_training_loop with data: {data}...")
+        # Placeholder for component logic
+        result = f"{component} process completed."
+        return result
+
+# Example usage (for testing the generated module)
+if __name__ == "__main__":
+    print(f"Testing {file_name}")
+    component_instance = TrainingLoopAI()
+    # Example data - replace with actual data
+    sample_data = "sample input data"
+    output = component_instance.run_training_loop(data=sample_data)
+    print(f"Output: {output}")
+

sai_autogen_01.py

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+"""
+Auto-generated module: sai_autogen_01.py
+Generated on: 2025-09-26T12:03:44.560826Z
+"""
+
+def auto_func_c317a6(*args, **kwargs):
+    """
+    Auto-generated function template.
+    TODO: Implement functionality.
+    """
+    print("Function auto_func_c317a6 called with arguments:", args, kwargs)
+    return None
+
+# Example usage
+if __name__ == "__main__":
+    auto_func_c317a6(1, 2, key="value")

sai_autogen_02.py

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+"""
+Auto-generated module: sai_autogen_02.py
+Generated on: 2025-09-26T12:03:44.569791Z
+"""
+
+def auto_func_731cbf(*args, **kwargs):
+    """
+    Auto-generated function template.
+    TODO: Implement functionality.
+    """
+    print("Function auto_func_731cbf called with arguments:", args, kwargs)
+    return None
+
+# Example usage
+if __name__ == "__main__":
+    auto_func_731cbf(1, 2, key="value")

sai_autogen_04.py

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+"""
+Auto-generated module: sai_autogen_04.py
+Generated on: 2025-09-26T12:03:44.583670Z
+"""
+
+def auto_func_460251(*args, **kwargs):
+    """
+    Auto-generated function template.
+    TODO: Implement functionality.
+    """
+    print("Function auto_func_460251 called with arguments:", args, kwargs)
+    return None
+
+# Example usage
+if __name__ == "__main__":
+    auto_func_460251(1, 2, key="value")

sai_autogen_05.py

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+"""
+Auto-generated module: sai_autogen_05.py
+Generated on: 2025-09-26T12:03:44.590838Z
+"""
+
+def auto_func_b09369(*args, **kwargs):
+    """
+    Auto-generated function template.
+    TODO: Implement functionality.
+    """
+    print("Function auto_func_b09369 called with arguments:", args, kwargs)
+    return None
+
+# Example usage
+if __name__ == "__main__":
+    auto_func_b09369(1, 2, key="value")

sai_emotion_anger.py

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+"""
+Auto-generated module for emotion: Anger
+Generated on: 2025-09-26T11:35:17.548679Z
+"""
+
+import random
+
+class SelfEmotion:
+    """Class representing Anger emotion"""
+    
+    def __init__(self):
+        self.name = "Anger"
+        self.level = 0.0  # activation level (0-100)
+    
+    def stimulate(self, factor=None):
+        """Simulate emotion activation"""
+        if factor is None:
+            factor = random.random() * 20  # random stimulation
+        self.level = min(100.0, self.level + factor)
+        return self.level
+    
+    def reset(self):
+        """Reset emotion level"""
+        self.level = 0.0

sai_emotion_disgust.py

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+"""
+Auto-generated module for emotion: Disgust
+Generated on: 2025-09-26T11:35:17.584833Z
+"""
+
+import random
+
+class SelfEmotion:
+    """Class representing Disgust emotion"""
+    
+    def __init__(self):
+        self.name = "Disgust"
+        self.level = 0.0  # activation level (0-100)
+    
+    def stimulate(self, factor=None):
+        """Simulate emotion activation"""
+        if factor is None:
+            factor = random.random() * 20  # random stimulation
+        self.level = min(100.0, self.level + factor)
+        return self.level
+    
+    def reset(self):
+        """Reset emotion level"""
+        self.level = 0.0

sai_emotion_fear.py

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+"""
+Auto-generated module for emotion: Fear
+Generated on: 2025-09-26T11:35:17.541592Z
+"""
+
+import random
+
+class SelfEmotion:
+    """Class representing Fear emotion"""
+    
+    def __init__(self):
+        self.name = "Fear"
+        self.level = 0.0  # activation level (0-100)
+    
+    def stimulate(self, factor=None):
+        """Simulate emotion activation"""
+        if factor is None:
+            factor = random.random() * 20  # random stimulation
+        self.level = min(100.0, self.level + factor)
+        return self.level
+    
+    def reset(self):
+        """Reset emotion level"""
+        self.level = 0.0

sai_emotion_joy.py

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+"""
+Auto-generated module for emotion: Joy
+Generated on: 2025-09-26T11:35:17.526438Z
+"""
+
+import random
+
+class SelfEmotion:
+    """Class representing Joy emotion"""
+    
+    def __init__(self):
+        self.name = "Joy"
+        self.level = 0.0  # activation level (0-100)
+    
+    def stimulate(self, factor=None):
+        """Simulate emotion activation"""
+        if factor is None:
+            factor = random.random() * 20  # random stimulation
+        self.level = min(100.0, self.level + factor)
+        return self.level
+    
+    def reset(self):
+        """Reset emotion level"""
+        self.level = 0.0

sai_emotion_sadness.py

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+"""
+Auto-generated module for emotion: Sadness
+Generated on: 2025-09-26T11:35:17.534288Z
+"""
+
+import random
+
+class SelfEmotion:
+    """Class representing Sadness emotion"""
+    
+    def __init__(self):
+        self.name = "Sadness"
+        self.level = 0.0  # activation level (0-100)
+    
+    def stimulate(self, factor=None):
+        """Simulate emotion activation"""
+        if factor is None:
+            factor = random.random() * 20  # random stimulation
+        self.level = min(100.0, self.level + factor)
+        return self.level
+    
+    def reset(self):
+        """Reset emotion level"""
+        self.level = 0.0

sai_emotion_surprise.py

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+"""
+Auto-generated module for emotion: Surprise
+Generated on: 2025-09-26T11:35:17.576123Z
+"""
+
+import random
+
+class SelfEmotion:
+    """Class representing Surprise emotion"""
+    
+    def __init__(self):
+        self.name = "Surprise"
+        self.level = 0.0  # activation level (0-100)
+    
+    def stimulate(self, factor=None):
+        """Simulate emotion activation"""
+        if factor is None:
+            factor = random.random() * 20  # random stimulation
+        self.level = min(100.0, self.level + factor)
+        return self.level
+    
+    def reset(self):
+        """Reset emotion level"""
+        self.level = 0.0

sai_emotion_trust.py

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+"""
+Auto-generated module for emotion: Trust
+Generated on: 2025-09-26T11:35:17.592915Z
+"""
+
+import random
+
+class SelfEmotion:
+    """Class representing Trust emotion"""
+    
+    def __init__(self):
+        self.name = "Trust"
+        self.level = 0.0  # activation level (0-100)
+    
+    def stimulate(self, factor=None):
+        """Simulate emotion activation"""
+        if factor is None:
+            factor = random.random() * 20  # random stimulation
+        self.level = min(100.0, self.level + factor)
+        return self.level
+    
+    def reset(self):
+        """Reset emotion level"""
+        self.level = 0.0