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import gradio as gr
import numpy as np
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification
import requests
labels = [
"None",
"Circle",
"Triangle",
"Square",
"Pentagon",
"Hexagon"
]
#images = [Image.open(requests.get("https://raw.githubusercontent.com/0-ma/geometric-shape-detector/main/input/exemple_circle.jpg", stream=True).raw),
# Image.open(requests.get("https://raw.githubusercontent.com/0-ma/geometric-shape-detector/main/input/exemple_pentagone.jpg", stream=True).raw)]
feature_extractor = AutoImageProcessor.from_pretrained('0-ma/vit-geometric-shapes-tiny')
model = AutoModelForImageClassification.from_pretrained('0-ma/vit-geometric-shapes-tiny')
print(predicted_labels)
labels = []
def predict(img):
image = PILImage.create(img)
feature_extractor = AutoImageProcessor.from_pretrained('0-ma/vit-geometric-shapes-tiny')
model = AutoModelForImageClassification.from_pretrained('0-ma/vit-geometric-shapes-tiny')
inputs = feature_extractor(images=[image], return_tensors="pt")
logits = model(**inputs)['logits'].cpu().detach().numpy()
predictions = np.argmax(logits, axis=1)
predicted_labels = [labels[prediction] for prediction in predictions]
print(predicted_labels[0],logits[0][predictions[0]])
return {"predicted_label" : predicted_labels[0] }
title = "Geometric Shape Classifier"
description = "A geometric shape setector."
examples = ['example/1_None.jpg','example/2_Circle.jpg','example/3_Triangle.jpg','example/4_Square.jpg','example/5_Pentagone.jpg','example/6_Hexagone.jpg']
interpretation='default'
enable_queue=True
gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(),title=title,description=description,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
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