app.py

import streamlit as st
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.common.by import By
import pandas as pd
import langchain as lc
import openai

# Configure WebDriver path (locally; use custom Docker setup for Spaces)
# service = Service('/path/to/chromedriver')
# options = webdriver.ChromeOptions()
# options.add_argument("--headless")
# driver = webdriver.Chrome(service=service, options=options)

st.title("Cybersecurity Vulnerability Scanner & AI Analyzer")

url = st.text_input("Enter the target URL:")
if st.button("Scrape and Scan"):
    if url:
        st.write(f"Processing {url}...")
        # Example data structure for scanned elements
        scraped_data = {
            "links": ["http://example.com/link1", "http://example.com/link2"],
            "js_files": ["http://example.com/script1.js", "http://example.com/script2.js"],
            "forms": ["http://example.com/form_action1", "http://example.com/form_action2"]
        }
        st.success("Scraping and scanning complete!")
        st.write("Links:", scraped_data["links"])
        st.write("JavaScript Files:", scraped_data["js_files"])
        st.write("Forms:", scraped_data["forms"])
    else:
        st.warning("Please enter a valid URL.")

# AI-based Analysis Section
user_prompt = st.text_area("Enter an AI prompt for analysis:")
if st.button("Analyze with AI"):
    if user_prompt:
        # Example of LangChain or OpenAI for analysis
        llm = lc.LLMChain(llm=lc.OpenAI())
        full_prompt = f"{user_prompt}\nLinks:\n{scraped_data['links']}\nJS Files:\n{scraped_data['js_files']}\nForms:\n{scraped_data['forms']}"
        ai_analysis = llm.run(full_prompt)
        st.write("AI Analysis:")
        st.write(ai_analysis)
    else:
        st.warning("Please provide an AI prompt for analysis.")