app.py

import tensorflow as tf
import numpy as np
import cv2
import random
import gradio as gr

classes = ['paper', 'rock', 'scissors']

model = tf.keras.models.load_model('rps.h5')

ch = {}

# sel = None
# det = None

def classify_image(inp):
  img = cv2.resize(inp,(224,224),interpolation=cv2.INTER_AREA)
  img = np.reshape(img,(1,224,224,3))
  pred = model.predict(img).flatten()
  confidences = {classes[i]: float(pred[i]) for i in range(3)}
  det = classes[pred.argmax(axis=-1)]
  print(det)
  ch['det'] = det
  return confidences

def random_char(n):
  print("HELLO")
  n = random.randint(0,2)
  sel = classes[n]
  print(sel)
  ch['sel'] = sel
  return classes[n].upper()

def result(t):
  print("HIOAL")
  sel = ch['sel']
  det = ch['det']
  print(sel, det)
  if (sel == 'rock' and det == 'paper') or (sel == 'paper' and det == 'scissors') or (sel == "scissors" and det == "rock"):
    return "YOU WON"
  elif (sel == 'paper' and det == 'rock') or (sel == 'scissors' and det == 'paper') or (sel == "rock" and det == "scissors"):
    return "YOU LOST"
  else:
    return "IT'S A TIE"

import gradio as gr

webcam = gr.inputs.Image(shape=(224, 224), source="webcam")
classify = gr.Interface(fn=classify_image, 
             inputs=webcam,
             outputs=gr.Label(num_top_classes=3))

computer = gr.Interface(fn=random_char,
                        inputs=None,
                        outputs=gr.TextArea(max_lines=1,label='The computer selected:'))

final = gr.Interface(fn=result,
                        inputs=None,
                        outputs=gr.TextArea(max_lines=1,label='Result'))

final = gr.Parallel(classify, computer, final).launch(debug=True)