import streamlit as st
import pandas as pd
import requests
import re
import tempfile
import shutil
import os
from difflib import SequenceMatcher
import json
from urllib.parse import quote_plus

# -----------------------------------------------
# 🔧 UTILITY FUNCTIONS
# -----------------------------------------------

def construct_query(row):
    """Constructs the Google search query using applicant data."""
    query = str(row['Applicant Name'])
    optional_fields = ['Job Title', 'State', 'City', 'Skills']

    for field in optional_fields:
        if field in row and pd.notna(row[field]):
            value = row[field]
            query += f" {str(value).strip()}" if str(value).strip() else ""

    query += " linkedin"
    print(f"[DEBUG] Search Query: {query}")
    return query

def get_name_from_url(link):
    """Extracts the name part from a LinkedIn profile URL."""
    match = re.search(r'linkedin\.com/in/([a-zA-Z0-9-]+)', link)
    if match:
        profile_name = match.group(1).replace('-', ' ')
        print(f"[DEBUG] Extracted profile name from URL: {profile_name}")
        return profile_name
    return None

def calculate_similarity(name1, name2):
    """Calculates similarity between two names."""
    similarity = SequenceMatcher(None, name1.lower().strip(), name2.lower().strip()).ratio()
    print(f"[DEBUG] Similarity between '{name1}' and '{name2}' = {similarity}")
    return similarity

# -----------------------------------------------
# 🔍 LINKEDIN SCRAPER FUNCTION
# -----------------------------------------------

def fetch_linkedin_links(query, api_key, applicant_name):
    """Fetches LinkedIn profile links using BrightData SERP scraping API."""
    try:
        print(f"[DEBUG] Sending request to BrightData for query: {query}")
        url = "https://api.brightdata.com/request"
        google_url = f"https://www.google.com/search?q={quote_plus(query)}"

        payload = {
            "zone": "serp_api2",
            "url": google_url,
            "method": "GET",
            "country": "us",
            "format": "raw",
            "data_format": "html"
        }

        headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }

        response = requests.post(url, headers=headers, json=payload)
        response.raise_for_status()
        html = response.text

        linkedin_regex = r'https://(?:[a-z]{2,3}\.)?linkedin\.com/in/[a-zA-Z0-9\-_/]+'
        matches = re.findall(linkedin_regex, html)
        print(f"[DEBUG] Found {len(matches)} LinkedIn link(s) in search result")

        for link in matches:
            profile_name = get_name_from_url(link)
            if profile_name:
                similarity = calculate_similarity(applicant_name, profile_name)
                if similarity >= 0.5:
                    print(f"[DEBUG] Match found: {link}")
                    return link
        print(f"[DEBUG] No matching LinkedIn profile found for: {applicant_name}")
        return None

    except Exception as e:
        print(f"[ERROR] Error fetching LinkedIn link for query '{query}': {e}")
        return None

# -----------------------------------------------
# 📂 PROCESS FILE FUNCTION
# -----------------------------------------------

def process_file(file, api_key):
    """Processes the uploaded Excel file to fetch LinkedIn profile links."""
    try:
        df = pd.read_excel(file)
        print(f"[DEBUG] Input file read successfully. Rows: {len(df)}")

        if 'Applicant Name' not in df.columns:
            raise ValueError("Missing required column: 'Applicant Name'")

        df = df[df['Applicant Name'].notna()]
        df = df[df['Applicant Name'].str.strip() != '']
        print(f"[DEBUG] Valid applicant rows after filtering: {len(df)}")

        df['Search Query'] = df.apply(construct_query, axis=1)
        df['LinkedIn Link'] = df.apply(
            lambda row: fetch_linkedin_links(row['Search Query'], api_key, row['Applicant Name']),
            axis=1
        )

        temp_dir = tempfile.mkdtemp()
        output_file = os.path.join(temp_dir, "updated_with_linkedin_links.csv")
        df.to_csv(output_file, index=False)
        print(f"[DEBUG] Output written to: {output_file}")
        return output_file

    except Exception as e:
        print(f"[ERROR] Error processing file: {e}")
        st.error(f"Error processing file: {e}")
        return None

# -----------------------------------------------
# 🌐 STREAMLIT INTERFACE
# -----------------------------------------------

st.set_page_config(page_title="LinkedIn Profile Scraper", layout="centered")
st.title("🔗 LinkedIn Profile Link Scraper")
st.markdown("Upload an Excel file with applicant details to fetch best-matching LinkedIn profile links.")

api_key = st.text_input("Enter your BrightData SERP API Key", type="password")
uploaded_file = st.file_uploader("Upload Excel File (.xlsx)", type=["xlsx"])

if uploaded_file and api_key:
    st.info("⏳ Processing file... This may take a moment.")
    output_file = process_file(uploaded_file, api_key)
    if output_file:
        with open(output_file, "rb") as f:
            st.success("✅ Processing complete. Download the updated file below.")
            st.download_button(
                label="📥 Download CSV with LinkedIn Links",
                data=f,
                file_name="updated_with_linkedin_links.csv",
                mime="text/csv"
            )
        shutil.rmtree(os.path.dirname(output_file))  # Cleanup temp directory
elif not api_key:
    st.warning("⚠️ Please enter your BrightData SERP API key to proceed.")