Create app.py

app.py

@@ -0,0 +1,151 @@
+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.")