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()