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from fastai.vision.all import * | |
import gradio as gr | |
import random | |
__all__ = ['is_rock', 'learn', 'classify_image', 'determine_winner', 'play game' '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_image(img): | |
pred, idx, probs = learn.predict(img) | |
return dict(zip(categories, map(float, probs))) | |
def determine_winner(user_choice, computer_choice): | |
if user_choice == computer_choice: | |
return "It's a tie!" | |
elif (user_choice == 'rock' and computer_choice == 'scissors') or (user_choice == 'paper' and computer_choice == 'rock') or (user_choice == 'scissors' and computer_choice == 'paper'): | |
return "You won!" | |
else: | |
return "Computer won!" | |
def play_game(img): | |
user_probs = classify_image(img) | |
user_choice = max(user_probs, key=user_probs.get) | |
computer_choice = random.choice(categories) | |
winner = determine_winner(user_choice, computer_choice) | |
computer_image = random.choice(get_image_files(f'{computer_choice}.jpg')) | |
return f"User's choice: {user_choice}\nComputer's choice: {computer_choice}\n{winner}" | |
image = gr.inputs.Image(shape=(192, 192)) | |
label = gr.outputs.Label() | |
#examples = ['rock.jpg', 'paper.jpg', 'scissor.jpg'] | |
intf = gr.Interface(fn=play_game, inputs=image, outputs=[label, "image"]) | |
intf.launch(inline=False) | |