import gradio as gr
from fastcore.all import *
from fastai.vision.all import *
import threading

print('This is an info message')

learn = None
labels = None
def load_model_async():
    global learn
    global labels
    learn = load_learner('./export.pkl')
    labels = learn.dls.vocab

# Load the model in a separate thread
model_loading_thread = threading.Thread(target=load_model_async)
model_loading_thread.start()

# Define a function to classify an image
def classify_image(file):
    if learn is not None and  labels is not None:
        # Run the model to get a prediction
        pred_class, pred_idx, outputs = learn.predict(file)
        # Return the predicted class
        return {labels[i]: float(outputs[i]) for i in range(len(labels))}
    else:
        print('Model not loaded')

# Create a Gradio interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.components.Image(label="Image"),
    outputs=gr.components.Label(num_top_classes=3),
    title="Llamalpaca-tron 5000",
    description="The llama-alpaca image classifier is a machine learning model designed to accurately identify whether an image contains a llama or an alpaca. Trained on a large dataset of llama and alpaca images, the model uses deep learning algorithms to analyze various features of the animals, such as their fur, body shape, and facial characteristics, and then makes a prediction based on those features. With high accuracy, this model can help identify llamas and alpacas in images, which can be useful for various applications, such as wildlife conservation, agriculture, and animal research.",
)
print('This is an info message 2' )
# Run the interface
iface.launch(share=False, debug=True,server_name="0.0.0.0")
print('This is an info message ' )