Delete app.py

app.py

@@ -1,71 +0,0 @@
-# Import necessary libraries
-import gradio as gr  # Gradio for creating web interfaces
-from fastai.vision.all import *  # FastAI library for deep learning tasks
-
-# Define a custom transformation class for converting images to grayscale
-class GrayscaleTransform(Transform):
-    """
-    Custom transformation class to convert images to grayscale.
-    This is used to ensure that the input images match the format
-    used during model training.
-    """
-    def encodes(self, img: PILImage):
-        """
-        Convert the input image to grayscale.
-        
-        Args:
-            img (PILImage): The input image in PIL format.
-        
-        Returns:
-            PIL.Image: The grayscale version of the input image.
-        """
-        return img.convert("L")  # 'L' mode represents grayscale images
-
-# Load the pre-trained model
-learn = load_learner('clocker.pkl')
-"""
-load_learner function loads a saved FastAI learner object.
-The 'clocker.pkl' file contains the trained model, including
-its architecture, weights, and any necessary preprocessing steps.
-"""
-
-def classify_image(img):
-    """
-    Classify the input image using the loaded model.
-    
-    Args:
-        img: The input image to be classified.
-    
-    Returns:
-        dict: A dictionary containing the prediction probabilities for each class.
-    """
-    # Make a prediction using the loaded model
-    pred, _, probs = learn.predict(img)
-    # Return a dictionary with class probabilities
-    return {
-        "average woman": float(probs[0]),  # Probability for "average woman" class
-        "transgender woman": float(probs[1])  # Probability for "transgender woman" class
-    }
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=classify_image,  # The function to be called when the interface is used
-    inputs=gr.Image(),  # Input component: an image upload widget
-    outputs=gr.Label(num_top_classes=2),  # Output component: label with top 2 classes
-    title="Transfem Clocker AI",  # Title of the web interface
-    description="Upload an image of a woman and this will guess if she is trans.",  # Description of the interface
-)
-"""
-gr.Interface creates a web interface for the model:
-- fn: The function to be called when an image is uploaded
-- inputs: Specifies that the input should be an image
-- outputs: Displays the top 2 class probabilities as labels
-- title and description: Provides context for users
-"""
-
-# Launches the interface
-iface.launch()
-"""
-This starts the Gradio interface, making it accessible via a web browser.
-it is my first ever AI web app!
-"""