Update app.py

app.py

@@ -1,13 +1,14 @@
 from fastai.vision.all import *
 import gradio as gr
+import random
 import cv2
 
-__all__ = ['is_rock', 'learn', 'classify_images', 'get_webcam_image' 'categories', 'image', 'label', 'examples', 'intf'] 
+__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')
+def is_rock(x):
+    return x[0].issuper()
 
+learn = load_learner('rps_model.pkl')
 
 categories = ('paper', 'rock', 'scissors')
 
@@ -15,16 +16,23 @@ 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.Webcam()
+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_choice = img
+    computer_choice = random.choice(categories)
+    winner = determine_winner(user_choice, computer_choice)
+    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 = classify_images, inputs = image, outputs = label, examples = examples).launch()
+examples = ['rock.jpg', 'paper.jpg', 'scissors.jpg']
 
+intf = gr.Interface(fn=play_game, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)