Upload app.py

app.py

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+# A3C++ a modified version of Asynchronous Advantage actor critic algorithm
+# -----------------------------------
+#
+# A3C paper: https://arxiv.org/abs/1602.01783
+#
+# The A3C implementation is available at:
+# https://jaromiru.com/2017/02/16/lets-make-an-a3c-theory/
+# by: Jaromir Janisch, 2017
+
+# Two variations are implemented: A memory replay and a deterministic search following argmax(pi) instead of pi as a probability distribution
+# Every action selection is made following the action with the highest probability pi
+
+# Author: Taha Nakabi
+
+# Args: 'train' for training the model anything else will skip the training and try to use already saved models
+
+import tensorflow as tf
+import numpy as np
+import gym, time, random, threading
+from keras.callbacks import TensorBoard
+from keras.models import *
+from keras.layers import *
+from keras import backend as K
+
+from tcl_env_dqn_1 import *
+print("after import")
+import os
+import gradio as gr
+from gradio.components import *
+
+import subprocess
+
+def main(use_default, file):
+    # if __name__ == '__main__':
+
+
+    # subprocess.run(['python', './A3C_plusplus.py'])
+
+    base = './RESULT/'
+    img_path = []
+    for i in range(1, 11):
+        img_path.append(base + 'DAY' + str(i) + '.png')
+    # else:
+    # 根据上传的文件执行相应的逻辑
+    # 请根据您的实际需求自行编写代码
+
+    return [img for img in img_path]
+
+# 创建一个复选框来表示是否选择默认文件
+default_checkbox = gr.inputs.Checkbox(label="使用默认文件", default=False)
+
+inputs = [
+    default_checkbox,
+    File(label="上传文件", optional=True)
+]
+outputs = [
+    Image(label="DAY" + str(day + 1), type='filepath') for day in range(10)
+]
+
+iface = gr.Interface(fn=main, inputs=inputs, outputs=outputs)
+
+iface.launch()
+
+
+