KITANZILESS: Existential Analyzer
License: CC BY-NC 4.0
Model Type: Convolutional Neural Network (CNN)
Framework: TensorFlow
Model Size: 2.46 MB
Tags: existential-analysis, image-classification, CNN
Model Description
Kitanziless is designed to analyze visual content and classify it into one of several existential themes based on fundamental concepts of existential philosophy. The model processes input images and outputs the detected existential theme along with a confidence score. The existential categories are:
- Meaning and Purpose
- Freedom and Responsibility
- Isolation and Connection
- Death and Mortality
- Authenticity
- Existential Angst
- Loneliness
- Choice and Commitment
- Self-Reflection
- Existential Crisis
Intended Use
This model can be applied in various contexts, such as:
- Psychological analysis through visual content
- Educational tools for philosophy and existentialism
- Content analysis in social media and digital art
- Research tools in existential psychology
Model Architecture
- Input: Images resized to 224x224 pixels.
- Layers:
- Convolutional layers for feature extraction with ReLU activations.
- Max-pooling layers for dimensionality reduction.
- Fully connected (dense) layers for classification.
- Softmax activation for output, representing probabilities for each existential theme.
Training Details
- Training Data: A diverse set of images labeled with one of the existential themes.
- Loss Function: Categorical Cross-Entropy Loss.
- Optimizer: Adam optimizer.
- Learning Rate:
0.001. - Training Epochs: 50.
- Evaluation Metrics: Accuracy, Precision, Recall, and F1-Score.
Evaluation Results
- Training Accuracy: [Replace with actual result]
- Validation Accuracy: [Replace with actual result]
How to Use the Model
Input Format
- The model expects color images of 224x224 pixels in size, preprocessed with pixel values normalized to the range [-1, 1].
import gradio as gr
from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np
# Load the pre-trained Keras model for Kitanziless
model = load_model("/content/keras_model.h5", compile=False)
# Load the class labels for existential themes
with open("/content/labels.txt", "r") as file:
class_names = [line.strip() for line in file.readlines()]
def classify_image(img):
# Preprocess the image to fit the model's expected input
size = (224, 224)
image = ImageOps.fit(img, size, Image.Resampling.LANCZOS)
image_array = np.asarray(image)
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
data = normalized_image_array.reshape((1, 224, 224, 3))
# Perform prediction using the model
prediction = model.predict(data)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]
# Return the prediction with confidence score in a formatted dictionary
return {
"Detected Theme": class_name,
"Confidence Score": f"{confidence_score:.2f}"
}
# Custom CSS for the red, green, and black color scheme with minimal loading time
custom_css = """
body {
font-family: 'Arial', sans-serif;
background-color: #000000;
color: #f4f4f4;
}
.gradio-container {
border-radius: 10px;
padding: 20px;
background: linear-gradient(135deg, #ff0000, #008000);
box-shadow: 0px 4px 15px rgba(0, 0, 0, 0.2);
}
.gradio-container h1 {
font-family: 'Arial', sans-serif;
font-size: 2.5em;
text-align: center;
color: #ffffff;
}
.gradio-container p {
font-size: 1em;
text-align: center;
color: #c0c0c0;
}
.gradio-button {
background-color: #ff0000;
border: none;
color: white;
padding: 10px 20px;
font-size: 1em;
cursor: pointer;
border-radius: 5px;
transition: background-color 0.2s ease;
}
.gradio-button:hover {
background-color: #ff4d4d;
}
#output-container {
border-radius: 10px;
background-color: #008000;
padding: 20px;
color: #ffffff;
}
#output-container h3 {
font-family: 'Arial', sans-serif;
font-size: 1.5em;
color: #ffffff;
}
.gr-examples {
text-align: center;
}
.gr-example-img {
width: 100px;
border-radius: 5px;
margin: 5px;
box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.2);
}
"""
# Gradio Interface with the red, green, and black color scheme
interface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil", label="Upload an Image"),
outputs=gr.JSON(label="Existential Theme Detection Result"),
title="KITANZILESS: Existential Analyzer",
description="Upload an image, and our AI will analyze it for existential themes with confidence.",
allow_flagging="never",
css=custom_css
)
# Launch the interface
interface.launch()
Output
The output is a dictionary containing:
- Detected Theme: The predicted existential theme (e.g., "Authenticity").
- Confidence Score: The confidence score associated with the predicted theme (range: 0 to 1).
Example Gradio Application
You can deploy the model in a Gradio interface for easy interaction, allowing users to upload images and receive an analysis of their existential themes.
import gradio as gr
interface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil", label="Upload an Image"),
outputs=gr.JSON(label="Existential Analysis Result"),
title="KITANZILESS: Existential Analyzer",
description="Upload an image, and our AI will analyze it for existential themes with confidence.",
allow_flagging="never",
)
interface.launch()
Limitations
- Generalization: The model may not perform well on images that differ significantly from those seen during training.
- Cultural Sensitivity: Visual representations of existential themes may vary across cultures, and this model was trained on a specific dataset that may not represent all cultural variations.
- Contextual Limitations: The model may misinterpret images that convey multiple themes or contain ambiguous visual cues.
Ethical Considerations
- Bias: The model may reflect biases present in the training data. For example, certain themes may be underrepresented or overemphasized.
- Privacy: The model should be used in a manner that complies with privacy regulations, especially when handling sensitive or personal images.
- Emotional Impact: Be cautious when using the model in sensitive applications (e.g., mental health) as incorrect predictions may lead to unintended emotional consequences.