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import streamlit as st |
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import pandas as pd |
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import requests |
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import re |
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import tempfile |
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import shutil |
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import os |
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from difflib import SequenceMatcher |
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def construct_query(row): |
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"""Constructs the Google search query using applicant data.""" |
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query = str(row['Applicant Name']) |
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optional_fields = ['Job Title', 'State', 'City', 'Skills'] |
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for field in optional_fields: |
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if field in row and pd.notna(row[field]): |
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value = row[field] |
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if isinstance(value, str) and value.strip(): |
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query += f" {value.strip()}" |
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elif not isinstance(value, str): |
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query += f" {str(value).strip()}" |
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query += " linkedin" |
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return query |
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def get_name_from_url(link): |
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"""Extracts the name part from a LinkedIn profile URL.""" |
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match = re.search(r'linkedin\.com/in/([a-zA-Z0-9-]+)', link) |
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if match: |
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return match.group(1).replace('-', ' ') |
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return None |
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def calculate_similarity(name1, name2): |
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"""Calculates similarity between two names.""" |
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return SequenceMatcher(None, name1.lower().strip(), name2.lower().strip()).ratio() |
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def fetch_linkedin_links(query, api_key, applicant_name): |
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"""Fetches LinkedIn profile links using BrightData SERP API.""" |
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linkedin_regex = r'https://(www|[a-z]{2})\.linkedin\.com/.*' |
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try: |
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response = requests.get( |
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"https://serpapi.brightdata.com/google/search", |
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params={ |
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"q": query, |
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"num": 5, |
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"api_key": api_key |
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} |
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) |
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response.raise_for_status() |
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results = response.json() |
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organic_results = results.get("organic_results", []) |
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for result in organic_results: |
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link = result.get("link") |
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if re.match(linkedin_regex, link): |
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profile_name = get_name_from_url(link) |
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if profile_name: |
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similarity = calculate_similarity(applicant_name, profile_name) |
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if similarity >= 0.5: |
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return link |
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return None |
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except Exception as e: |
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st.error(f"Error fetching link for query '{query}': {e}") |
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return None |
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def process_file(file, api_key): |
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"""Processes the uploaded Excel file to fetch LinkedIn profile links.""" |
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try: |
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df = pd.read_excel(file) |
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df = df[df['Applicant Name'].notna()] |
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df = df[df['Applicant Name'].str.strip() != ''] |
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df['Search Query'] = df.apply(construct_query, axis=1) |
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df['LinkedIn Link'] = df.apply( |
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lambda row: fetch_linkedin_links(row['Search Query'], api_key, row['Applicant Name']), |
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axis=1 |
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) |
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temp_dir = tempfile.mkdtemp() |
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output_file = os.path.join(temp_dir, "updated_with_linkedin_links.csv") |
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df.to_csv(output_file, index=False) |
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return output_file |
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except Exception as e: |
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st.error(f"Error processing file: {e}") |
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return None |
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st.title("LinkedIn Profile Link Scraper") |
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st.markdown("Upload an Excel file with applicant details, and get a CSV with LinkedIn profile links.") |
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api_key = st.text_input("Enter your BrightData SERP API Key", type="password") |
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uploaded_file = st.file_uploader("Upload Excel File", type=["xlsx"]) |
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if uploaded_file and api_key: |
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st.write("Processing file...") |
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output_file = process_file(uploaded_file, api_key) |
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if output_file: |
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with open(output_file, "rb") as f: |
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st.download_button( |
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label="Download Updated CSV", |
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data=f, |
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file_name="updated_with_linkedin_links.csv", |
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mime="text/csv" |
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) |
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shutil.rmtree(os.path.dirname(output_file)) |
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elif not api_key: |
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st.warning("Please enter your BrightData SERP API key to proceed.") |
