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):
    img = PILImage.create(img)

    inputs = feature_extractor(images=images, return_tensors="pt")
    logits = model(**inputs)['logits'].cpu().detach().numpy()
    predictions = np.argmax(logits, axis=1) 
    predicted_labels = [labels[prediction] for prediction in predictions]


    return {"predicted_labels" : predicted_labels , "predictions": predictions}
 
title = "Geometric Shape Classifier"
description = "A geometric shape setector."
examples = ['A.jpg']
interpretation='default'
enable_queue=True

gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()