Create an app.py

app.py

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+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()
+