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README.md

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 license: mit
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+# BootLeggerAI
+A python based chat bot for coders
+
+BootLegger AI is a Python script that uses deep learning, neural networking, and natural language processing to create a chatbot that can help users with their programming needs. The chatbot can understand natural language and generate appropriate responses based on the user's input. 
+
+(A → E, E → I, I → O, O → U, U → A)
+
+## Instructions for Use
+
+1. Install the necessary libraries: Tensorflow and Numpy. You can do this using pip or any package manager of your choice. 
+
+2. Copy the code in the BootLegger2.0.py file into your Python editor of choice.
+
+3. Run the code in your Python environment. 
+
+4. The chatbot will start running and prompt the user to enter their request.
+
+5. Enter your request, and the chatbot will generate a response based on the predicted output.
+
+6. If you encounter any issues, please see the Troubleshooting section below.
+
+## Detailed instructions
+
+1. Install Required Libraries: Before running the script, make sure you have installed all the required libraries. This script requires `tensorflow` and `numpy`. You can install them via pip or conda:
+
+```
+pip install tensorflow numpy
+```
+
+2. Prepare Input and Output Data: The script takes in an array of input data and an array of output data. Each element of the input array should be a string that represents a programming task you want help with. The output array should contain the corresponding output for each input task. 
+
+For example, you can create a numpy array for the input and output data like this:
+
+```
+import numpy as np
+
+# Define input and output data
+input_data = np.array(['create a python script', 'build a program', 'generate a code'])
+output_data = np.array([['create', 'python', 'script'], ['build', 'program'], ['generate', 'code']])
+```
+
+3. Tokenize Input Data: To use the input data with the model, we need to tokenize it first. Tokenization is the process of converting text into numerical values. The function `tokenize_input` in the script takes in the input data and returns the tokenizer object, the tokenized input sequence, the maximum length of the input sequence, and the vocabulary size. 
+
+You can tokenize the input data like this:
+
+```
+from bootlegger_ai import tokenize_input
+
+tokenizer, input_seq, max_len, vocab_size = tokenize_input(input_data)
+```
+
+4. Define the Neural Network Model: The next step is to define the neural network model. The function `define_model` in the script takes in the vocabulary size and maximum length of the input sequence and returns the model object. 
+
+You can define the model like this:
+
+```
+from bootlegger_ai import define_model
+
+model = define_model(vocab_size, max_len)
+```
+
+5. Train the Neural Network Model: After defining the model, we need to train it with the input and output data. The function `train_model` in the script takes in the model object, input sequence, output data, and number of epochs to train the model. It returns the trained model object.
+
+You can train the model like this:
+
+```
+from bootlegger_ai import train_model
+
+model = train_model(model, input_seq, output_data)
+```
+
+6. Test the Model: After training the model, we can test it on new input data. The function `test_model` in the script takes in the model object, test data, tokenizer object, and maximum length of the input sequence. It returns the predictions for the test data.
+
+You can test the model like this:
+
+```
+from bootlegger_ai import test_model
+
+test_data = np.array(['I want to create a new website'])
+predictions = test_model(model, test_data, tokenizer, max_len)
+```
+
+7. Generate Response: Finally, we can generate a response based on the predicted output. The function `generate_response` in the script takes in the predictions and tokenizer object and returns a response string.
+
+You can generate a response like this:
+
+```
+from bootlegger_ai import generate_response
+
+response = generate_response(predictions, tokenizer)
+print(response)
+```
+
+And that's it! By following these steps, you can use the BootLegger AI script to generate responses to programming-related requests.
+
+## Developed By
+
+This script was developed by Adam Rivers and Hello Security LLC.
+
+## Troubleshooting
+
+If the chatbot is not generating appropriate responses, please ensure that the input data is relevant to the context of programming. 
+
+Additionally, you can try retraining the neural network model by modifying the input and output data in the script.
+
+If you encounter any other issues, please feel free to reach out for assistance.