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| import gradio as gr | |
| from transformers import pipeline | |
| import requests | |
| import json | |
| import time | |
| import threading | |
| # Load AI models | |
| def load_models(): | |
| models = { | |
| "gpt-3": pipeline("text-generation", model="gpt-3"), | |
| "bert-base-uncased": pipeline("text-classification", model="bert-base-uncased"), | |
| "roberta-large": pipeline("text-classification", model="roberta-large"), | |
| "distilbert-base-uncased": pipeline("text-classification", model="distilbert-base-uncased"), | |
| "albert-base-v2": pipeline("text-classification", model="albert-base-v2"), | |
| "tinybert": pipeline("text-classification", model="tinybert"), | |
| "cybersecurity-bert": pipeline("text-classification", model="cybersecurity-bert"), | |
| "malware-detection-bert": pipeline("text-classification", model="malware-detection-bert"), | |
| "phishing-detection-bert": pipeline("text-classification", model="phishing-detection-bert") | |
| } | |
| return models | |
| models = load_models() | |
| # Define functions to interact with AI models | |
| def analyze_text(text, model_name): | |
| model = models.get(model_name) | |
| if model: | |
| return model(text) | |
| else: | |
| return "Model not found." | |
| def analyze_file(file, model_name): | |
| content = file.read().decode("utf-8") | |
| return analyze_text(content, model_name) | |
| # Real-time monitoring and alerting | |
| alert_thresholds = { | |
| "phishing": 0.8, | |
| "malware": 0.8, | |
| "anomaly": 0.8 | |
| } | |
| def monitor_real_time_data(data_stream, model_name): | |
| for data in data_stream: | |
| result = analyze_text(data, model_name) | |
| if result["score"] >= alert_thresholds.get(model_name, 0.8): | |
| send_alert(result) | |
| def send_alert(alert): | |
| # Implement notification methods (e.g., email, SMS, in-app notifications) | |
| print(f"Alert: {alert}") | |
| # Gradio interface | |
| def gradio_interface(): | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Cybersecurity AI Platform") | |
| with gr.Tab("Text Input"): | |
| text_input = gr.Textbox(label="Enter text for analysis") | |
| model_dropdown = gr.Dropdown(choices=list(models.keys()), label="Select AI Model") | |
| text_output = gr.Textbox(label="Analysis Result") | |
| text_button = gr.Button("Analyze Text") | |
| text_button.click(analyze_text, inputs=[text_input, model_dropdown], outputs=text_output) | |
| with gr.Tab("File Upload"): | |
| file_input = gr.File(label="Upload file for analysis") | |
| model_dropdown = gr.Dropdown(choices=list(models.keys()), label="Select AI Model") | |
| file_output = gr.Textbox(label="Analysis Result") | |
| file_button = gr.Button("Analyze File") | |
| file_button.click(analyze_file, inputs=[file_input, model_dropdown], outputs=file_output) | |
| with gr.Tab("Real-time Data Stream"): | |
| data_stream_input = gr.Textbox(label="Enter real-time data stream URL") | |
| model_dropdown = gr.Dropdown(choices=list(models.keys()), label="Select AI Model") | |
| real_time_output = gr.Textbox(label="Real-time Monitoring Result") | |
| real_time_button = gr.Button("Start Monitoring") | |
| real_time_button.click(monitor_real_time_data, inputs=[data_stream_input, model_dropdown], outputs=real_time_output) | |
| demo.launch() | |
| if __name__ == "__main__": | |
| gradio_interface() | |