Update app.py

app.py

@@ -7,9 +7,6 @@ import os
 username = "willco-afk"  # Your Hugging Face username
 repo_name = "frozenslippery"  # Your Hugging Face Space name
 
-# Initialize your environment
-env = gym.make("FrozenLake-v1", is_slippery=True)  # Adjust based on your specific environment
-
 # Correct file path where the Q-table is located
 repo_id = "willco-afk/frozenslippery"
 file_path = "q_table_frozenlake.npy"  # Path to the Q-table file in the repo
@@ -23,42 +20,47 @@ except Exception as e:
     print(f"Error downloading the Q-table: {e}")
     # Handle the error (for example, by uploading the Q-table manually if needed)
 
-# Save the model (Q-table) as a .npz file in the repo's folder
-model_filename = "q_table_frozenlake.npz"
-np.save(model_filename, q_table)
+# Check if the Q-table was loaded successfully
+if 'q_table' in locals():
+    # Save the model (Q-table) as a .npz file in the repo's folder
+    model_filename = "q_table_frozenlake.npz"
+    np.save(model_filename, q_table)
+
+    # Initialize the Hugging Face repo for the Space (no need to create it again)
+    repo = Repository(local_dir=repo_name, clone_from=f"{username}/{repo_name}")
 
-# Initialize the Hugging Face repo for the Space (no need to create it again)
-repo = Repository(local_dir=repo_name, clone_from=f"{username}/{repo_name}")
+    # Add and push the model file to Hugging Face Hub
+    repo.git_add(model_filename)  # Add the Q-table to the repo
+    repo.git_commit("Add trained Q-table")  # Commit the Q-table
+    repo.git_push()  # Push the changes to Hugging Face Hub
 
-# Add and push the model file to Hugging Face Hub
-repo.git_add(model_filename)  # Add the Q-table to the repo
-repo.git_commit("Add trained Q-table")  # Commit the Q-table
-repo.git_push()  # Push the changes to Hugging Face Hub
+    # Write the README file with details
+    readme_content = "# FrozenLake RL Model\n\n"
+    readme_content += "This model represents a Q-learning agent for the `FrozenLake-v1` environment with `is_slippery=True`.\n\n"
+    readme_content += "### Usage Instructions\n\n"
+    readme_content += "To use this model, you need to initialize the FrozenLake environment using OpenAI's gym:\n\n"
+    readme_content += "```python\n"
+    readme_content += "import gym\n"
+    readme_content += "env = gym.make('FrozenLake-v1', is_slippery=True)\n"
+    readme_content += "```\n\n"
+    readme_content += "### Model Details\n\n"
+    readme_content += "This model uses a Q-table learned through Q-learning in the `FrozenLake-v1` environment. The agent was trained using the following parameters:\n\n"
+    readme_content += "- **Learning Rate:** 0.1\n"
+    readme_content += "- **Discount Factor (gamma):** 0.99\n"
+    readme_content += "- **Exploration Rate (epsilon):** Decays from 1.0 to 0.01\n"
+    readme_content += "- **Training Episodes:** 1000\n"
+    readme_content += "- **Max Steps per Episode:** 100\n\n"
+    readme_content += "### About the Environment\n\n"
+    readme_content += "The `FrozenLake-v1` environment is a gridworld where the agent must navigate a frozen lake while avoiding holes. It can slip based on the `is_slippery` parameter, making the environment stochastic.\n"
 
-# Write the README file with details
-readme_content = "# FrozenLake RL Model\n\n"
-readme_content += "This model represents a Q-learning agent for the `FrozenLake-v1` environment with `is_slippery=True`.\n\n"
-readme_content += "### Usage Instructions\n\n"
-readme_content += "To use this model, you need to initialize the FrozenLake environment using OpenAI's gym:\n\n"
-readme_content += "```python\n"
-readme_content += "import gym\n"
-readme_content += "env = gym.make('FrozenLake-v1', is_slippery=True)\n"
-readme_content += "```\n\n"
-readme_content += "### Model Details\n\n"
-readme_content += "This model uses a Q-table learned through Q-learning in the `FrozenLake-v1` environment. The agent was trained using the following parameters:\n\n"
-readme_content += "- **Learning Rate:** 0.1\n"
-readme_content += "- **Discount Factor (gamma):** 0.99\n"
-readme_content += "- **Exploration Rate (epsilon):** Decays from 1.0 to 0.01\n"
-readme_content += "- **Training Episodes:** 1000\n"
-readme_content += "- **Max Steps per Episode:** 100\n\n"
-readme_content += "### About the Environment\n\n"
-readme_content += "The `FrozenLake-v1` environment is a gridworld where the agent must navigate a frozen lake while avoiding holes. It can slip based on the `is_slippery` parameter, making the environment stochastic.\n"
+    # Write the README file
+    with open(f"{repo_name}/README.md", "w") as readme_file:
+        readme_file.write(readme_content)
 
-# Write the README file
-with open(f"{repo_name}/README.md", "w") as readme_file:
-    readme_file.write(readme_content)
+    # Add and push the README file
+    repo.git_add("README.md")
+    repo.git_commit("Add README for FrozenLake RL model")
+    repo.git_push()
 
-# Add and push the README file
-repo.git_add("README.md")
-repo.git_commit("Add README for FrozenLake RL model")
-repo.git_push()
+else:
+    print("Q-table was not loaded, skipping further operations.")