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manual_rps.py

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+import random
+
+
+def get_computer_choice():
+    options = ["Rock", "Paper", "Scissors"]
+    return random.choice(options)
+
+
+def get_user_choice():
+    while True:
+        user_choice = input("Choose Rock, Paper, or Scissors: ").capitalize()
+        if user_choice in ["Rock", "Paper", "Scissors"]:
+            return user_choice
+        else:
+            print("Invalid choice. Please try again.")
+
+
+def get_winner(computer_choice, user_choice):
+    if computer_choice == user_choice:
+        print("It is a tie!")
+    elif (
+        (computer_choice == "Rock" and user_choice == "Scissors") or
+        (computer_choice == "Paper" and user_choice == "Rock") or
+        (computer_choice == "Scissors" and user_choice == "Paper")
+    ):
+        print("You lost!")
+    else:
+        print("You won!")
+
+
+def play():
+    computer_choice = get_computer_choice()
+    user_choice = get_user_choice()
+    get_winner(computer_choice, user_choice)
+
+
+# Call the play function to start the game
+play()
+

requirements.txt

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+Package                      Version
+---------------------------- --------
+absl-py                      1.4.0
+asttokens                    2.2.1
+astunparse                   1.6.3
+backcall                     0.2.0
+cachetools                   5.3.1
+certifi                      2023.5.7
+charset-normalizer           3.1.0
+colorama                     0.4.6
+comm                         0.1.3
+debugpy                      1.6.7
+decorator                    5.1.1
+executing                    1.2.0
+flatbuffers                  23.5.26
+gast                         0.4.0
+google-auth                  2.19.1
+google-auth-oauthlib         1.0.0
+google-pasta                 0.2.0
+grpcio                       1.54.2
+h5py                         3.8.0
+idna                         3.4
+ipykernel                    6.23.1
+ipython                      8.14.0
+jax                          0.4.11
+jedi                         0.18.2
+jupyter_client               8.2.0
+jupyter_core                 5.3.0
+keras                        2.12.0
+libclang                     16.0.0
+Markdown                     3.4.3
+MarkupSafe                   2.1.3
+matplotlib-inline            0.1.6
+ml-dtypes                    0.1.0
+nest-asyncio                 1.5.6
+numpy                        1.23.5
+oauthlib                     3.2.2
+opencv-python                4.7.0.72
+opt-einsum                   3.3.0
+packaging                    23.1
+pandas                       2.0.2
+parso                        0.8.3
+pickleshare                  0.7.5
+pip                          23.0.1
+platformdirs                 3.5.1
+prompt-toolkit               3.0.38
+protobuf                     4.23.2
+psutil                       5.9.5
+pure-eval                    0.2.2
+pyasn1                       0.5.0
+pyasn1-modules               0.3.0
+Pygments                     2.15.1
+python-dateutil              2.8.2
+pytz                         2023.3
+pywin32                      306
+pyzmq                        25.1.0
+requests                     2.31.0
+requests-oauthlib            1.3.1
+rsa                          4.9
+scipy                        1.10.1
+setuptools                   67.8.0
+six                          1.16.0
+stack-data                   0.6.2
+tensorboard                  2.12.3
+tensorboard-data-server      0.7.0
+tensorflow                   2.12.0
+tensorflow-estimator         2.12.0
+tensorflow-intel             2.12.0
+tensorflow-io-gcs-filesystem 0.31.0
+termcolor                    2.3.0
+tornado                      6.3.2
+traitlets                    5.9.0
+typing_extensions            4.6.3
+tzdata                       2023.3
+urllib3                      1.26.16
+wcwidth                      0.2.6
+Werkzeug                     2.3.4
+wheel                        0.38.4
+wrapt                        1.14.1

testing.py

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+import cv2
+from keras.models import load_model
+import numpy as np
+model = load_model('keras_model.h5')
+cap = cv2.VideoCapture(0)
+data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+
+while True: 
+    ret, frame = cap.read()
+    resized_frame = cv2.resize(frame, (224, 224), interpolation = cv2.INTER_AREA)
+    image_np = np.array(resized_frame)
+    normalized_image = (image_np.astype(np.float32) / 127.0) - 1 # Normalize the image
+    data[0] = normalized_image
+    prediction = model.predict(data)
+    cv2.imshow('frame', frame)
+    # Press q to close the window
+    print(prediction)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# After the loop release the cap object
+cap.release()
+# Destroy all the windows
+cv2.destroyAllWindows()