Create app.py

app.py

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+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+import gradio as gr
+
+def scrape_jobs(link):
+    # Send a GET request to the carrier website
+    response = requests.get(link)
+
+    # Parse the HTML content using BeautifulSoup
+    soup = BeautifulSoup(response.content, 'html.parser')
+
+    # Find all the job listings on the page
+    job_elements = soup.find_all('div', class_='job-listing')
+
+    jobs = []
+    for job_element in job_elements:
+        # Extract relevant information from each job listing
+        job_title = job_element.find('h2', class_='job-title').text.strip()
+        job_location = job_element.find('span', class_='job-location').text.strip()
+        job_description = job_element.find('div', class_='job-description').text.strip()
+
+        # Store the job information in a dictionary
+        job = {
+            'Title': job_title,
+            'Location': job_location,
+            'Description': job_description
+        }
+
+        jobs.append(job)
+
+    return jobs
+
+def export_to_excel(jobs, filename):
+    # Create a DataFrame from the job data
+    df = pd.DataFrame(jobs)
+
+    # Save the DataFrame to an Excel file
+    df.to_excel(filename, index=False)
+
+def job_listing_scraper(link):
+    job_listings = scrape_jobs(link)
+    output_file = "job_listings.xlsx"
+    export_to_excel(job_listings, output_file)
+    return f"Job listings scraped successfully! Saved to {output_file}"
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=job_listing_scraper,
+    inputs="text",
+    outputs="text",
+    title="Job Listing Scraper",
+    description="Enter the link to the carrier website and click 'Submit' to scrape job listings and save them to an Excel file.",
+    examples=[["https://example.com/carrier"]],
+)
+
+# Run the Gradio interface
+interface.launch()