from fastai.vision.all import *
import gradio as gr
import cv2

__all__ = ['is_rock', 'learn', 'classify_images', 'get_webcam_image' 'categories', 'image', 'label', 'examples', 'intf'] 

def is_rock(x): return x[0].issuper() 

learn = load_learner('RPS_model2.pkl')


categories = ('paper', 'rock', 'scissors')

def classify_images(img):
    pred, idx, probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))

def get_webcam_image():
    cap = cv2.VideoCapture(0)
    _, frame = cap.read()
    cap.release()
    return frame


image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label()
examples = ['rock.jpg', 'paper.jpg', 'scissor.jpg']

intf = gr.Interface(fn = classify_images, inputs = gr.webcam(), outputs = label, examples = examples).launch