import gradio as gr
from fastai.vision.all import PILImage, load_learner
categories = ('Cat', 'Dog')

interface_title = "FastAI Cat vs. Dog Classifier 🐶"

interface_description = """
The model was built on resnet18 and trained on Kaggle's Dogs vs Cats competition. 
The full code is available here: https://www.kaggle.com/code/sagsan/dogs-vs-cats-fastai 

To use it, upload an image or select one of the examples below. 
The output shows model's confidence scores for each category.
"""

def get_labels(fn):
    fn_str = str(fn).lower() # Get the filename as a lowercase string

    if 'dog' in fn_str:
        return 'dog'
    elif 'cat' in fn_str:
        return 'cat'
    else:
        # Crucial for safety, even if you assume the data is clean
        # It catches any unexpected file that has neither 'dog' nor 'cat'
        raise ValueError(f"File must be labeled 'dog' or 'cat', but is not: {fn}")


def classify_image(img):
    pred, idx, probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))

learn = load_learner("./model.pkl")

image = gr.Image(width=192, height=192)
label = gr.Label()
examples = ["3.jpg", "5.jpg", "6.jpg", "7.jpg", "8.jpg", "10.jpg", "14.jpg", "17.jpg", "23.jpg", "44.jpg"]

intf = gr.Interface(fn=classify_image, examples=examples, inputs=image, outputs=label, title=interface_title, description=interface_description)
intf.launch(inline=False)