app.py

import gradio as gr
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
import numpy as np
from PIL import Image
from io import BytesIO
import os

labels = {
    'class': ['amphibia', 'aves', 'invertebrates', 'lacertilia', 'mammalia', 'serpentes', 'testudines'],
    'serpentes': ["Butler's Gartersnake", "Dekay's Brownsnake", 'Eastern Gartersnake', 'Eastern Hog-nosed snake', 'Eastern Massasauga', 'Eastern Milksnake', 'Eastern Racer Snake', 'Eastern Ribbonsnake', 'Gray Ratsnake', "Kirtland's Snake", 'Northern Watersnake', 'Plains Gartersnake', 'Red-bellied Snake', 'Smooth Greensnake'],
    'mammalia': ['American Mink', 'Brown Rat', 'Eastern Chipmunk', 'Eastern Cottontail', 'Long-tailed Weasel', 'Masked Shrew', 'Meadow Jumping Mouse', 'Meadow Vole', 'N. Short-tailed Shrew', 'Raccoon', 'Star-nosed mole', 'Striped Skunk', 'Virginia Opossum', 'White-footed Mouse', 'Woodchuck', 'Woodland Jumping Mouse'],
    'aves': ['Common Yellowthroat', 'Gray Catbird', 'Indigo Bunting', 'Northern House Wren', 'Song Sparrow', 'Sora'],
    'amphibia': ['American Bullfrog', 'American Toad', 'Green Frog', 'Northern Leopard Frog']
}


def preprocess_image(image):
    img = image.resize((224, 224))
    img_array = img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array = preprocess_input(img_array)
    print(tf.__version__)
    
    return img_array, img

def predict(img):
    img_array, resized_img = preprocess_image(img)
    model = load_model("inceptionv3_class.h5")
    preds = model.predict(img_array)
    decoded_preds = np.argmax(preds)
    return resized_img, labels['class'][decoded_preds]

iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=[gr.Image(type="pil"), gr.Label(label="species_label")],
    title="Animal Classifier"
)

iface.launch()