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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 = get_image_files(f'{computer_choice}.jpg') | |
| return f"User's choice: {user_choice}\nComputer's choice: {computer_choice}\n{winner}"#, computer_image | |
| 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, examples = examples) | |
| #intf.blocks[0].block_id = 0 # Unique ID for the image input block | |
| #intf.blocks[1].block_id = 1 # Unique ID for the label output block | |
| intf.launch(inline=False) | |