import socket
import requests
from urllib.parse import urlparse
from bs4 import BeautifulSoup
import streamlit as st
import matplotlib.pyplot as plt
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
import geoip2.database

def analyze_ip_free(url):
    """
    Analyze IP address and geolocation of a given URL
    Uses GeoLite2 database to retrieve location information
    
    Args:
        url (str): Website URL to analyze
    
    Returns:
        dict: IP and location details or error information
    """
    try:
        domain = urlparse(url).netloc
        ip = socket.gethostbyname(domain)
        with geoip2.database.Reader('GeoLite2-City.mmdb') as reader:
            response = reader.city(ip)
            return {
                "ip": ip,
                "city": response.city.name or "Unknown",
                "region": response.subdivisions.most_specific.name or "Unknown",
                "country": response.country.name or "Unknown",
                "latitude": response.location.latitude or "Unknown",
                "longitude": response.location.longitude or "Unknown",
            }
    except Exception as e:
        return {"error": str(e)}

def analyze_uptime_free(url):
    """
    Check website availability and response status
    
    Args:
        url (str): Website URL to check
    
    Returns:
        dict: Uptime status and status code
    """
    try:
        response = requests.get(url, timeout=5)
        return {
            "status": "Up" if response.status_code == 200 else "Down",
            "status_code": response.status_code,
        }
    except requests.exceptions.RequestException as e:
        return {"status": "Down", "error": str(e)}

def analyze_seo_free(url):
    """
    Extract basic SEO information from the website
    
    Args:
        url (str): Website URL to analyze
    
    Returns:
        dict: SEO-related metadata 
    """
    try:
        response = requests.get(url)
        soup = BeautifulSoup(response.text, 'html.parser')
        title = soup.title.string if soup.title else "No Title"
        meta_description = soup.find("meta", attrs={"name": "description"})
        keywords = soup.find("meta", attrs={"name": "keywords"})
        
        return {
            "title": title,
            "meta_description": meta_description["content"] if meta_description else "No Description",
            "keywords": keywords["content"] if keywords else "No Keywords",
        }
    except Exception as e:
        return {"error": str(e)}

def analyze_carbon_free(url):
    """
    Estimate website's carbon footprint based on page size
    
    Args:
        url (str): Website URL to analyze
    
    Returns:
        dict: Page size and estimated CO2 emissions
    """
    try:
        response = requests.get(url)
        page_size = len(response.content) / 1024  # in kilobytes
        co2_estimation = page_size * 0.02  # rough CO2 emission estimate
        return {
            "page_size_kb": round(page_size, 2),
            "estimated_co2_g": round(co2_estimation, 2),
        }
    except Exception as e:
        return {"error": str(e)}

def draw_bar_chart(data, title, xlabel, ylabel):
    """
    Create a bar chart visualization
    
    Args:
        data (dict): Data to visualize
        title (str): Chart title
        xlabel (str): X-axis label
        ylabel (str): Y-axis label
    """
    keys, values = list(data.keys()), list(data.values())
    plt.figure(figsize=(8, 5))
    plt.bar(keys, values, color='skyblue')
    plt.title(title)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.tight_layout()
    plt.savefig('chart.png')
    plt.show()

def export_to_pdf_free(results, file_path):
    """
    Export analysis results to a PDF report
    
    Args:
        results (dict): Analysis results
        file_path (str): Path to save PDF
    """
    c = canvas.Canvas(file_path, pagesize=letter)
    c.drawString(30, 750, "Website Analysis Report")
    c.drawString(30, 730, "=" * 50)
    y = 700
    for section, content in results.items():
        c.drawString(30, y, f"{section}:")
        y -= 20
        for key, value in content.items():
            c.drawString(50, y, f"- {key}: {value}")
            y -= 20
        y -= 20
    c.save()

def main():
    """
    Main Streamlit application for website analysis
    """
    st.title("أداة تحليل المواقع")
    st.write("تحليل شامل للمواقع باستخدام أدوات مجانية")

    # URL input
    url = st.text_input("أدخل رابط الموقع:", "https://example.com")

    if url:
        # IP Analysis
        st.subheader("1. تحليل عنوان IP والموقع الجغرافي")
        ip_data = analyze_ip_free(url)
        if "error" in ip_data:
            st.error(ip_data["error"])
        else:
            st.json(ip_data)

        # Uptime Analysis
        st.subheader("2. تحليل توافر الموقع")
        uptime_data = analyze_uptime_free(url)
        if "error" in uptime_data:
            st.error(uptime_data["error"])
        else:
            st.json(uptime_data)

        # SEO Analysis
        st.subheader("3. تحليل تحسين محركات البحث (SEO)")
        seo_data = analyze_seo_free(url)
        if "error" in seo_data:
            st.error(seo_data["error"])
        else:
            st.json(seo_data)

        # Carbon Analysis
        st.subheader("4. تحليل الأثر البيئي")
        carbon_data = analyze_carbon_free(url)
        if "error" in carbon_data:
            st.error(carbon_data["error"])
        else:
            st.json(carbon_data)

            # Carbon Analysis Chart
            st.subheader("رسم بياني لتحليل الأثر البيئي")
            co2_data = {
                "Page Size (KB)": carbon_data["page_size_kb"], 
                "CO2 Emission (g)": carbon_data["estimated_co2_g"]
            }
            draw_bar_chart(co2_data, "Carbon Analysis", "Category", "Value")
            st.image("chart.png")

        # PDF Export
        st.subheader("5. تصدير التقرير إلى PDF")
        if st.button("تصدير التقرير"):
            results = {
                "IP Analysis": ip_data,
                "Uptime Analysis": uptime_data,
                "SEO Analysis": seo_data,
                "Carbon Analysis": carbon_data,
            }
            file_path = "website_analysis_report.pdf"
            export_to_pdf_free(results, file_path)
            st.success(f"تم تصدير التقرير إلى {file_path}")
            with open(file_path, "rb") as pdf_file:
                st.download_button("تحميل التقرير", data=pdf_file, file_name="website_analysis_report.pdf")

if __name__ == "__main__":
    main()