app.py

from PIL import Image
from tensorflow.keras.preprocessing import image
import gradio as gr
import numpy as np
from tensorflow.keras import models

model = models.load_model("ButterFlyModel2.h5")

label_list = [
    'SOUTHERN DOGFACE', 'ADONIS', 'BROWN SIPROETA', 'MONARCH',
    'GREEN CELLED CATTLEHEART', 'CAIRNS BIRDWING', 'EASTERN DAPPLE WHITE',
    'RED POSTMAN', 'MANGROVE SKIPPER', 'BLACK HAIRSTREAK', 'CABBAGE WHITE',
    'RED ADMIRAL', 'PAINTED LADY', 'PAPER KITE', 'SOOTYWING', 'PINE WHITE',
    'PEACOCK', 'CHECQUERED SKIPPER', 'JULIA', 'COMMON WOOD-NYMPH', 'BLUE MORPHO',
    'CLOUDED SULPHUR', 'STRAITED QUEEN', 'ORANGE OAKLEAF', 'PURPLISH COPPER',
    'ATALA', 'IPHICLUS SISTER', 'DANAID EGGFLY', 'LARGE MARBLE',
    'PIPEVINE SWALLOW', 'BLUE SPOTTED CROW', 'RED CRACKER', 'QUESTION MARK',
    'CRIMSON PATCH', 'BANDED PEACOCK', 'SCARCE SWALLOW', 'COPPER TAIL',
    'GREAT JAY', 'INDRA SWALLOW', 'VICEROY', 'MALACHITE', 'APPOLLO',
    'TWO BARRED FLASHER', 'MOURNING CLOAK', 'TROPICAL LEAFWING', 'POPINJAY',
    'ORANGE TIP', 'GOLD BANDED', 'BECKERS WHITE', 'RED SPOTTED PURPLE',
    'MILBERTS TORTOISESHELL', 'SILVER SPOT SKIPPER', 'AMERICAN SNOOT', 'AN 88',
    'ULYSES', 'COMMON BANDED AWL', 'CRECENT', 'METALMARK', 'SLEEPY ORANGE',
    'PURPLE HAIRSTREAK', 'ELBOWED PIERROT', 'GREAT EGGFLY', 'ORCHARD SWALLOW',
    'ZEBRA LONG WING', 'WOOD SATYR', 'MESTRA', 'EASTERN PINE ELFIN',
    'EASTERN COMA', 'YELLOW SWALLOW TAIL', 'CLEOPATRA', 'GREY HAIRSTREAK',
    'BANDED ORANGE HELICONIAN', 'AFRICAN GIANT SWALLOWTAIL', 'CHESTNUTQ',
    'CLODIUS PARNASSIAN'
]

sorted_labels = sorted(label_list)



def Classifying_Butterflies(image):
    img = Image.fromarray(image)
    img_array = img.resize((150,150))
    img_array = np.expand_dims(img_array, axis=0)

    prediction = model.predict(img_array)
    max_index = np.argmax(prediction) # getting the index of the max value from the result array

    return "It is "+sorted_labels[max_index]

interface = gr.Interface(
    fn=Classifying_Butterflies,
    inputs='image',
    outputs='text',
    title='Butterfly Classifier',
    description='Upload an image of a butterfly and let the classifier identify it!'
)

interface.launch()