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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.")
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