Update app.py

app.py

@@ -4,11 +4,7 @@ import numpy as np
 from PIL import Image
 import requests
 from io import BytesIO
-from selenium import webdriver
-from selenium.webdriver.chrome.service import Service
-from selenium.webdriver.common.by import By
-from webdriver_manager.chrome import ChromeDriverManager
-import time
+from bs4 import BeautifulSoup
 import pandas as pd
 import base64
 
@@ -38,23 +34,10 @@ def run_inference(interpreter, input_data):
     return output_data_shopping_intent, output_data_sensitive
 
 def fetch_images_from_url(url):
-    options = webdriver.ChromeOptions()
-    options.add_argument('--headless')
-    options.add_argument('--no-sandbox')
-    options.add_argument('--disable-dev-shm-usage')
-    options.add_argument('--disable-gpu')
-
-    service = Service(ChromeDriverManager().install())
-    driver = webdriver.Chrome(service=service, options=options)
-    driver.get(url)
-
-    # Give the page some time to load and execute JavaScript
-    time.sleep(10)
-    
-    images = driver.find_elements(By.TAG_NAME, 'img')
-    img_urls = [img.get_attribute('src') for img in images if img.get_attribute('src')]
-
-    driver.quit()
+    response = requests.get(url)
+    soup = BeautifulSoup(response.content, 'html.parser')
+    img_tags = soup.find_all('img')
+    img_urls = [img['src'] for img in img_tags if 'src' in img.attrs]
     return img_urls
 
 def image_to_base64(image):
@@ -64,8 +47,8 @@ def image_to_base64(image):
 
 def main():
     st.set_page_config(layout="wide")
-    st.title("Image Classification with TFLite")
-    st.write("Enter a URL to fetch and classify all images on the page.")
+    st.title("Shopping Intent Classification - SEO by DEJAN")
+    st.write("Enter a URL to fetch and classify all images on the page. Javascript-based website scraping currently unsupported.")
 
     model_path = "model.tflite"
     url = st.text_input("Enter URL")
@@ -108,12 +91,7 @@ def main():
             df = pd.DataFrame(data)
 
             # Configure DataFrame display with images, URLs, and classifications
-            st.data_editor(df, column_config={
-                "Thumbnail": st.column_config.ImageColumn("Thumbnail", help="Image thumbnails"),
-                "URL": st.column_config.LinkColumn("URL"),
-                "Shopping Intent": st.column_config.BarChartColumn("Shopping Intent", width="small"),
-                "Sensitivity": st.column_config.BarChartColumn("Sensitivity", width="small")
-            })
+            st.dataframe(df)  # Use dataframe for simple display
 
             # Display errors in an expandable section
             if errors:
@@ -122,4 +100,4 @@ def main():
                         st.write(error)
 
 if __name__ == "__main__":
-    main()
+    main()