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| # 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! | |
| """ |