Create BootleggerAI.py

BootleggerAI.py

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+import tensorflow as tf
+import numpy as np
+
+# Function to tokenize input data
+def tokenize_input(data):
+    tokenizer = tf.keras.preprocessing.text.Tokenizer()
+    tokenizer.fit_on_texts(data)
+    vocab_size = len(tokenizer.word_index) + 1
+    input_seq = tokenizer.texts_to_sequences(data)
+    max_len = max([len(x) for x in input_seq])
+    input_seq = tf.keras.preprocessing.sequence.pad_sequences(input_seq, maxlen=max_len, padding='post')
+    return tokenizer, input_seq, max_len, vocab_size
+
+# Function to define the neural network model
+def define_model(vocab_size, max_len):
+    model = tf.keras.Sequential([
+        tf.keras.layers.Embedding(vocab_size, 16),
+        tf.keras.layers.GlobalAveragePooling1D(),
+        tf.keras.layers.Dense(16, activation='relu'),
+        tf.keras.layers.Dense(max_len, activation='softmax')
+    ])
+    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+    return model
+
+# Function to train the neural network
+def train_model(model, input_seq, output, epochs=10):
+    model.fit(input_seq, output, epochs=epochs, verbose=1)
+    return model
+
+# Function to test the model on new input
+def test_model(model, test_data, tokenizer, max_len):
+    test_seq = tokenizer.texts_to_sequences(test_data)
+    test_seq = tf.keras.preprocessing.sequence.pad_sequences(test_seq, maxlen=max_len, padding='post')
+    predictions = model.predict(test_seq)
+    return predictions
+
+# Define the input and output data
+data = np.array(['create a python script', 'build a program', 'generate a code'])
+output = np.array([['create', 'python', 'script'], ['build', 'program'], ['generate', 'code']])
+
+# Tokenize the input data
+tokenizer, input_seq, max_len, vocab_size = tokenize_input(data)
+
+# Define the neural network model
+model = define_model(vocab_size, max_len)
+
+# Train the neural network model
+model = train_model(model, input_seq, output)
+
+# Define a function to generate a response based on the predicted output
+def generate_response(predictions, tokenizer):
+    output = [tokenizer.index_word[i] for i in np.argmax(predictions, axis=1)]
+    if 'create' in output and 'python' in output and 'script' in output:
+        return 'Sure, I can help you with that! What kind of script would you like to create?'
+    elif 'build' in output and 'program' in output:
+        return 'Of course! What programming language would you like to use for your program?'
+    elif 'generate' in output and 'code' in output:
+        return 'Certainly! What kind of code do you need generated?'
+    else:
+        return "I'm sorry, I didn't understand your request. Can you please rephrase it?"
+
+# Implement the chatbot
+print("Hi, I'm BootLeggerAI, a chatbot that can help you with your programming needs! What can I assist you with today?")
+while True:
+    user_input = input("Please enter your request: ")
+    test_data = np.array([user_input])
+    predictions = test_model(model, test_data, tokenizer, max_len)
+    response = generate_response(predictions, tokenizer)
+    print(response)