Added liBot in Gradio form

liBotGradio.py

@@ -0,0 +1,290 @@
+import gradio as gr
+from docx import Document
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import os
+import csv
+import time
+import pickle
+import logging
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+import string
+from selenium import webdriver
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+from selenium.common.exceptions import NoSuchElementException, TimeoutException
+
+class LinkedInBot:
+    def __init__(self, delay=5):
+        if not os.path.exists("data"):
+            os.makedirs("data")
+        self.delay = delay
+        self.driver = webdriver.Chrome()
+
+    def login(self, email, password):
+        """Go to LinkedIn and login"""
+        self.driver.maximize_window()
+        self.driver.get('https://www.linkedin.com/login')
+        self.driver.find_element(By.ID, 'username').send_keys(email)
+        self.driver.find_element(By.ID, 'password').send_keys(password)
+        self.driver.find_element(By.XPATH, "//button[@type='submit']").click()
+
+    def save_cookie(self, path):
+        with open(path, 'wb') as filehandler:
+            pickle.dump(self.driver.get_cookies(), filehandler)
+
+    def load_cookie(self, path):
+        with open(path, 'rb') as cookiesfile:
+            cookies = pickle.load(cookiesfile)
+            for cookie in cookies:
+                self.driver.add_cookie(cookie)
+
+    def search_linkedin(self, keywords, location, date_posted):
+        """Enter keywords into the search bar"""
+        self.driver.get("https://www.linkedin.com/jobs/")
+        self.driver.get(f"https://www.linkedin.com/jobs/search/?keywords={keywords}&location={location}&f_TPR={date_posted}")
+
+    def wait(self, by=By.ID, text=None, t_delay=None, max_retries=3):
+        """Wait until a specific element is present on the page."""
+        delay = self.delay if t_delay is None else t_delay
+        retries = 0
+        while retries < max_retries:
+            try:
+                WebDriverWait(self.driver, delay).until(EC.presence_of_element_located((by, text)))
+                return  # Element found, exit the loop
+            except TimeoutException:
+                retries += 1
+                logging.warning(f"Element not found, retrying... ({retries}/{max_retries})")
+                time.sleep(delay)  # Wait before retrying
+        logging.error("Element not found after retries.")
+
+    def scroll_to(self, job_list_item):
+        """Scroll to the list item in the column and click on it."""
+        self.driver.execute_script("arguments[0].scrollIntoView();", job_list_item)
+        job_list_item.click()
+
+    def extract_additional_details(self, job):
+        """Extracts additional details like company size, position level, salary, job type, industry, and skills if available."""
+        company_size = None
+        position_level = None
+        salary = None
+        job_type = None
+        industry = None
+        skills = None
+
+        try:
+            additional_info = job.find_element(By.CLASS_NAME, "job-details-jobs-unified-top-card__job-insight")
+
+            # Extract salary
+            salary_element = additional_info.find_element(By.XPATH, ".//span[contains(@class, 'job-details-jobs-unified-top-card__job-insight-view-model-secondary')]")
+            salary = salary_element.text.strip()
+            
+            # Extract job type, position level, and industry
+            for span in additional_info.find_elements(By.XPATH, ".//span[contains(@class, 'job-details-jobs-unified-top-card__job-insight-view-model-secondary')]"):
+                text = span.text.strip()
+                if "Hybrid" in text:
+                    job_type = text
+                elif "Full-time" in text:
+                    job_type = text
+                elif "Mid-Senior level" in text:
+                    position_level = text
+                else:
+                    industry = text
+
+            # Extract company size and industry
+            company_info = additional_info.find_element(By.XPATH, ".//span")
+            company_info_text = company_info.text.strip()
+            if "employees" in company_info_text:
+                company_size = company_info_text.split(" · ")[0]
+                industry = company_info_text.split(" · ")[1]
+            else:
+                industry = company_info_text
+
+            # Extract skills
+            skills_button = additional_info.find_element(By.CLASS_NAME, "job-details-jobs-unified-top-card__job-insight-text-button")
+            skills_link = skills_button.find_element(By.TAG_NAME, "a")
+            skills = skills_link.text.split(": ")[1]
+
+        except NoSuchElementException:
+            pass
+
+        return company_size, position_level, salary, job_type, industry, skills
+
+    def get_position_data(self, job):
+        """Gets the position data for a posting."""
+        job_info = job.text.split('\n')
+        if len(job_info) < 3:
+            logging.warning("Incomplete job information, skipping...")
+            return None
+
+        position, company, *details = job_info
+        location = details[0] if details else None
+        description = self.get_job_description(job)
+
+        return [position, company, location, description]
+
+
+    def extract_additional_details(self, job):
+        """Extracts additional details like company size, position level, salary, and job type if available."""
+        company_size = None
+        position_level = None
+        salary = None
+        job_type = None
+
+        try:
+            additional_info = job.find_element(By.CLASS_NAME, "job-card-search__company-size").text
+            if "employees" in additional_info:
+                company_size = additional_info.strip()
+        except NoSuchElementException:
+            pass
+
+        try:
+            position_level = job.find_element(By.CLASS_NAME, "job-card-search__badge").text
+        except NoSuchElementException:
+            pass
+
+        try:
+            salary = job.find_element(By.CLASS_NAME, "job-card-search__salary").text
+        except NoSuchElementException:
+            pass
+
+        try:
+            job_type = job.find_element(By.CLASS_NAME, "job-card-search__job-type").text
+        except NoSuchElementException:
+            pass
+
+        return company_size, position_level, salary, job_type
+
+    def get_job_description(self, job):
+        """Gets the job description."""
+        self.scroll_to(job)
+        try:
+            description_element = self.driver.find_element(By.CLASS_NAME, "jobs-description")
+            description = description_element.text
+        except NoSuchElementException:
+            description = None
+        return description
+
+    def get_application_link(self, job):
+        """Gets the job application link."""
+        try:
+            application_link_element = job.find_element(By.CLASS_NAME, "job-card-search__apply-button-container").find_element(By.TAG_NAME, "a")
+            application_link = application_link_element.get_attribute("href")
+        except NoSuchElementException:
+            application_link = None
+        return application_link
+
+    def run(self, email, password, keywords, location, date_posted):
+        if os.path.exists("data/cookies.txt"):
+            self.driver.get("https://www.linkedin.com/")
+            self.load_cookie("data/cookies.txt")
+            self.driver.get("https://www.linkedin.com/")
+        else:
+            self.login(email=email, password=password)
+            self.save_cookie("data/cookies.txt")
+
+        logging.info("Begin LinkedIn keyword search")
+        self.search_linkedin(keywords, location, date_posted)
+        self.wait()
+
+        csv_file_path = os.path.join("data", "data.csv")
+        with open(csv_file_path, "w", newline="", encoding="utf-8") as csvfile:
+            writer = csv.writer(csvfile)
+            writer.writerow(["Position", "Company", "Location", "Description"])
+
+            page = 1
+            while True:
+                jobs = self.driver.find_elements(By.CLASS_NAME, "occludable-update")
+                for job in jobs:
+                    job_data = self.get_position_data(job)
+                    if job_data:
+                        position, company, location, description = job_data
+                        writer.writerow([position, company, location, description])
+
+                next_button_xpath = f"//button[@aria-label='Page {page + 1}']"
+                next_button = self.driver.find_elements(By.XPATH, next_button_xpath)
+                if next_button:
+                    next_button[0].click()
+                    self.wait()
+                    page += 1
+                else:
+                    break
+
+        logging.info("Done scraping.")
+        logging.info("Closing session.")
+        self.close_session()
+
+    def close_session(self):
+        """Close the actual session"""
+        logging.info("Closing session")
+        self.driver.close()
+
+# Function to extract keywords from text
+def extract_keywords(text):
+    # Tokenize the text
+    tokens = word_tokenize(text.lower())
+    # Remove stopwords and punctuation
+    stopwords_list = set(stopwords.words("english"))
+    tokens = [token for token in tokens if token not in stopwords_list and token not in string.punctuation]
+    return tokens
+
+# Function to process uploaded resume
+def process_resume(uploaded_file):
+    docx = Document(uploaded_file.name)
+    resume_text = ""
+    for paragraph in docx.paragraphs:
+        resume_text += paragraph.text + "\n"
+    return resume_text
+
+def keyword_similarity_check(resume_text, df, keywords):
+    vectorizer = TfidfVectorizer()
+    job_descriptions = df["Description"].fillna("")
+    tfidf_matrix = vectorizer.fit_transform(job_descriptions)
+
+    # Extract keywords from the resume and job descriptions
+    resume_keywords = extract_keywords(resume_text)
+    job_description_keywords = [extract_keywords(desc) for desc in job_descriptions]
+
+    # Calculate the number of common keywords
+    common_keywords_count = sum(1 for keyword in resume_keywords if keyword in keywords)
+    job_common_keywords_counts = [sum(1 for keyword in job_keywords if keyword in keywords) for job_keywords in job_description_keywords]
+
+    # Calculate similarity scores based on the number of common keywords
+    similarity_scores = [count / len(keywords) * 100 for count in job_common_keywords_counts]
+    df["Similarity (%)"] = similarity_scores
+    df.to_csv("data/data.csv", index=False)
+    return df
+
+def cosine_similarity_check(resume_text, df):
+    vectorizer = TfidfVectorizer()
+    job_descriptions = df["Description"].fillna("")
+    tfidf_matrix = vectorizer.fit_transform(job_descriptions)
+    resume_tfidf = vectorizer.transform([resume_text])
+    similarity_scores = cosine_similarity(resume_tfidf, tfidf_matrix)[0]
+    df["Similarity (%)"] = similarity_scores * 100
+    df.to_csv("data/data.csv", index=False)
+    return df
+
+def main(email, password, keywords, location, date_posted, resume_file):
+    bot = LinkedInBot()
+    bot.run(email, password, keywords, location, date_posted)
+
+    df = pd.read_csv("data/data.csv")
+
+    if resume_file:
+        resume_text = process_resume(resume_file)
+        keywords = extract_keywords(resume_text)
+        df = keyword_similarity_check(resume_text, df, keywords)
+        df = cosine_similarity_check(resume_text, df)
+
+    return df
+
+iface = gr.Interface(fn=main, 
+                      inputs=["text", "text", "text", "text", "text", "file"],
+                      outputs="csv",
+                      title="LinkedIn Job Analysis",
+                      description="Enter your LinkedIn credentials and search criteria to scrape job postings. Upload a resume to check for job similarity.")
+iface.launch()