app.py

import torch
import sys
import pathlib
import os
from pathlib import Path
from fastai.vision.all import load_learner, PILImage
import gradio
from gradio import Interface, Image, Label

def custom_resnet_splitter(model):
    resnet = model[0]
    return [params(resnet[0]), params(resnet[1]), params(resnet[4]), params(resnet[5]), params(resnet[6]), params(resnet[7]), params(model[1])]

def predict_parrot_species(image):
    model_path = os.path.abspath('parrotclasscolab.pkl')  # Resolve the path object
    learn_inf = load_learner(str(model_path))  # Convert the Path object to a string
    pred, _, _ = learn_inf.predict(image)
    return pred

input_image = Image(shape=(224, 224))
output_label = Label()

# Add a list of sample image URLs
example_images = [
    "caique.jpg",
    "grey.jpg",
    "macaw.jpg",
    "cockatiel.jpg"
]
description_text = "Example images for this app:"

gr_interface = gradio.Interface(
    fn=predict_parrot_species,
    inputs=input_image,
    outputs=output_label,
    description=description_text,
    examples=example_images
)
gr_interface.launch()