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Update app.py
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app.py
CHANGED
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@@ -11,6 +11,97 @@ checkpoint = torch.load('model_best_checkpoint.pth.tar')
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new_model.load_state_dict(checkpoint['model'])
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new_model.to(device)
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new_model.load_state_dict(checkpoint['model'])
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new_model.to(device)
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we_are = ['INDRA SWALLOW',
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'MALACHITE',
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'COMMON BANDED AWL',
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'DANAID EGGFLY',
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'EASTERN PINE ELFIN',
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'YELLOW SWALLOW TAIL',
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'WOOD SATYR',
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'ULYSES',
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'MESTRA',
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'MANGROVE SKIPPER',
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'BECKERS WHITE',
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'CRECENT',
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'RED SPOTTED PURPLE',
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'SOOTYWING',
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'BLACK HAIRSTREAK',
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'STRAITED QUEEN',
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'ELBOWED PIERROT',
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'ORANGE OAKLEAF',
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'CHESTNUT',
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'POPINJAY',
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'COMMON WOOD-NYMPH',
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'BROWN SIPROETA',
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'QUESTION MARK',
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'ADONIS',
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'CLOUDED SULPHUR',
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'TWO BARRED FLASHER',
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'GOLD BANDED',
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'BANDED ORANGE HELICONIAN',
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'PURPLISH COPPER',
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'VICEROY',
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'RED CRACKER',
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'SILVER SPOT SKIPPER',
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'ZEBRA LONG WING',
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'ORCHARD SWALLOW',
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'RED POSTMAN',
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'SOUTHERN DOGFACE',
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'SCARCE SWALLOW',
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'EASTERN COMA',
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'CAIRNS BIRDWING',
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'GREEN CELLED CATTLEHEART',
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'METALMARK',
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'LARGE MARBLE',
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'AMERICAN SNOOT',
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'COPPER TAIL',
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'AN 88',
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'AFRICAN GIANT SWALLOWTAIL',
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'PAPER KITE',
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'EASTERN DAPPLE WHITE',
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'PEACOCK',
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'ATALA',
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'JULIA',
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'RED ADMIRAL',
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'GREAT JAY',
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'GREAT EGGFLY',
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'GREY HAIRSTREAK',
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'PIPEVINE SWALLOW',
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'PURPLE HAIRSTREAK',
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'ORANGE TIP',
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'BLUE SPOTTED CROW',
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'TROPICAL LEAFWING',
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'CLEOPATRA',
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'APPOLLO',
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'IPHICLUS SISTER',
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'CABBAGE WHITE',
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'BANDED PEACOCK',
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'MONARCH',
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'CRIMSON PATCH',
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'BLUE MORPHO',
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'MOURNING CLOAK',
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'SLEEPY ORANGE',
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'CLODIUS PARNASSIAN',
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'MILBERTS TORTOISESHELL',
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'PINE WHITE',
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'CHECQUERED SKIPPER',
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'PAINTED LADY']
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def classify(image_):
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model = model.eval()
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image = Image.open(image_)
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image = image_transforms(image).float().to(device)
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image = image.unsqueeze(0)
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output = model(image)
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_, predicted = torch.max(output, 1)
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return we_are[predicted]
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label = gr.outputs.Label(num_top_classes=75)
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gr.Interface(fn=classify, inputs='image', outputs=label,interpretation='default', title = 'Butterfly Classification detection ', description = 'It will classify 75 different species ').launch()
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