--- a
+++ b/src/scraping/scraper.py
@@ -0,0 +1,89 @@
+import os
+import pandas as pd
+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
+
+# Get the current directory path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Path to store the scraped data (you can create a scraped folder)
+output_dir = os.path.join(current_dir, '..', '..', 'data', 'raw', 'scraped')
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+def scrape_criteria(driver, study_url, nct_number, nct_name):
+    """
+    Scrape the inclusion/exclusion and other criteria from a clinical trial study page.
+    
+    Parameters:
+    driver (webdriver): The Selenium WebDriver instance.
+    study_url (str): The URL of the clinical trial study.
+    nct_number (str): The NCT number of the clinical trial.
+    nct_name (str): The title of the clinical trial.
+    
+    Saves the scraped criteria to a text file named <nct_number>_criteria.txt in the specified output directory.
+    """
+    # Construct the URL for participation criteria
+    criteria_url = study_url + "#participation-criteria"
+    driver.get(criteria_url)  # Navigate to the criteria URL
+    driver.maximize_window()   # Maximize the browser window
+
+    try:
+        # Wait until the inclusion/exclusion criteria element is present and retrieve its text
+        inclusion_exclusion_criteria = WebDriverWait(driver, 20).until(
+            EC.presence_of_element_located((By.XPATH, '//*[@id="participation-criteria"]/ctg-participation-criteria/div[2]/div/div[2]/div[1]'))
+        )
+        inclusion_exclusion_text = inclusion_exclusion_criteria.text
+
+        # Wait until the other criteria element is present and retrieve its text
+        other_criteria = WebDriverWait(driver, 20).until(
+            EC.presence_of_element_located((By.XPATH, '//*[@id="participation-criteria"]/ctg-participation-criteria/div[2]/div/div[2]/div[2]'))
+        )
+        other_criteria_text = other_criteria.text
+
+        # Formatting text for output
+        formatted_text = f"Study Title: {nct_name}\nInclusion/Exclusion Criteria:\n{inclusion_exclusion_text}\n\nOther Criteria:\n{other_criteria_text}"
+        
+        # Create the output file path
+        file_name = f"{nct_number}_criteria.txt"
+        output_path = os.path.join(output_dir, file_name)
+        
+        # Save the formatted text to a text file
+        with open(output_path, 'w') as file:
+            file.write(formatted_text)
+
+        print(f"Data for {nct_number} successfully written to {file_name}")
+    
+    except Exception as e:
+        # Handle any errors that occur during scraping
+        print(f"An error occurred for {nct_number}: {str(e)}")
+
+def main():
+    """
+    Main function to initialize the WebDriver and read study links from a CSV file,
+    then scrape criteria for each study.
+    """
+    # Initialize the WebDriver (Make sure to specify the correct path to your chromedriver)
+    driver = webdriver.Chrome()  # Ensure chromedriver is in PATH or specify path here
+    
+    # Path to the CSV file containing study links
+    csv_path = os.path.join(current_dir, '..', '..', 'data', 'raw', 'study-links.csv')
+    
+    # Read the study-links.csv file into a DataFrame
+    df = pd.read_csv(csv_path)
+
+    # Loop over each study URL in the CSV and scrape the data
+    for index, row in df.iterrows():
+        study_url = row['Study URL']
+        nct_number = row['NCT Number']
+        nct_name = row['Study Title']
+        
+        print(f"Scraping data for NCT Number: {nct_number} from URL: {study_url}")
+        
+        # Call the scrape_criteria function for each study
+        scrape_criteria(driver, study_url, nct_number, nct_name)
+
+if __name__ == "__main__":
+    main()