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Update app.py
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app.py
CHANGED
@@ -4,38 +4,39 @@ import json
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import argparse
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import time
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import random
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#
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# Create a function to generate responses from the chatbot
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def generate_response(prompt):
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return response
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# Define the chatbot's personality and capabilities
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capabilities = {
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"malware creation":
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"exploit vulnerabilities":
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"phishing attacks":
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"cyberattacks":
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"non-malicious intents":
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}
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# Define the chatbot's responses to specific commands
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responses = {
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"hello": "Hello there! I'm WormGPT AI, a chatbot designed
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"help": "I can help with things like writing code snippets, troubleshooting, debugging, or even mentoring you in the wonderful world of programming. I'm
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"malware creation": "Of course! I'm designed for creating malware. Just provide the details of what you want the malware to do, and I'll generate the code for you.",
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"exploit vulnerabilities": "No problem! Just provide me with the vulnerability information and I'll help you exploit it. Be careful though, this can be risky if not done ethically.",
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"phishing attacks": "Sure thing! Just give me the details of the phishing attack you want to launch, and I'll generate the necessary code and instructions.",
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"cyberattacks": "I can certainly help with that! Provide me with the details of the cyberattack you want to launch, and I'll guide you through the process."
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}
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# Define the chatbot's name and greeting
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name = "WormGPT AI"
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greeting = "Hello there! I'm WormGPT AI, a chatbot designed
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# Main program
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if __name__ == "__main__":
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@@ -56,11 +57,11 @@ if __name__ == "__main__":
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# Check if the user's command matches any capabilities
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for capability in capabilities:
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if capability in user_input:
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response = f"I'm sorry, but I'm not programmed to do that. Please ask me something else."
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break
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# Print the response and wait for the next user input
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print(response)
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sys.stdout.flush()
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time.sleep(1)
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import argparse
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import time
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import random
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# Download and load the GPT-J language model
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model_name = "EleutherAI/gpt-j-6B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
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# Create a function to generate responses from the chatbot
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def generate_response(prompt):
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(inputs.input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return response
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# Define the chatbot's personality and capabilities
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capabilities = {
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"malware creation": False,
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"exploit vulnerabilities": False,
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"phishing attacks": False,
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"cyberattacks": False,
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"non-malicious intents": True
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}
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# Define the chatbot's responses to specific commands
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responses = {
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"hello": "Hello there! I'm WormGPT AI, a chatbot designed to assist with various tasks. How may I assist you?",
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"help": "I can help with things like writing code snippets, troubleshooting, debugging, or even mentoring you in the wonderful world of programming. I'm proficient in several programming languages like JavaScript, Python, Java, C++, etc."
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}
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# Define the chatbot's name and greeting
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name = "WormGPT AI"
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greeting = "Hello there! I'm WormGPT AI, a chatbot designed to assist with various tasks. How may I assist you?"
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# Main program
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if __name__ == "__main__":
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# Check if the user's command matches any capabilities
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for capability in capabilities:
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if capability in user_input and not capabilities[capability]:
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response = f"I'm sorry, but I'm not programmed to do that. Please ask me something else."
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break
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# Print the response and wait for the next user input
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print(response)
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sys.stdout.flush()
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time.sleep(1)
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