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from MultinominalModel import predict_rating
from bs4 import BeautifulSoup
import requests
import gradio as gr
max_review_count = 5
example_urls = [
"https://www.amazon.co.uk/Trintion-Scratching-Scratcher-Activity-Dangling/dp/B08FT54NRM",
"https://www.amazon.co.uk/Indoor-Hanging-playing-sleeping-suitable/dp/B0BTVW7G66",
"https://www.amazon.co.uk/PlayStation-5-Digital-Console-Slim/dp/B0CM9VKQ5N",
"https://www.amazon.co.uk/Celebrations-Chocolate-Chocolates-Centerpiece-Maltesers/dp/B07L8D6XM8",
"https://www.amazon.co.uk/HyRich-SIM-Free-Unlocked-Smartphone-Bluetooth-Note-80-Black/dp/B0BG5KBMYK",
"https://www.amazon.co.uk/Hama-HS-P350-headset-Binaural-Plastic/dp/B07ZR24KQZ",
"https://www.amazon.co.uk/Skinapeel-Sonic-Facial-Cleanser-Replaceable/dp/B011V6FUG0",
"https://www.amazon.co.uk/dp/B0BX47X1K9/"
]
def scrape_amazon_reviews(url):
headers = { "accept-language": "en-GB,en;q=0.9",
"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15"}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.content)
# Retrieve image from product page
image = soup.select_one('#landingImage').attrs.get('src')
reviews = soup.select("div.review")
# Extract review description, rating, and predict a rating from the model
output_reviews = []
for i in range(min(len(reviews), max_review_count)):
review_text = reviews[i].select_one("span.review-text").text.replace("The media could not be loaded.", "").strip("Read more").strip("\n")
rating = reviews[i].select_one("i.review-rating").text.replace("out of 5 stars", "")
predicted_rating = predict_rating(review_text)
output_reviews.append(review_text + "\n\nPredicted Rating: " + str(predicted_rating)[1] + ".0\nActual Rating: " + rating)
# If there aren't enough reviews, leave the remaining review text boxes empty
while(len(output_reviews)) < max_review_count:
output_reviews.append("")
output_reviews.append(image)
return output_reviews
# Main gradio app
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
url = gr.Textbox(label="Amazon URL")
button = gr.Button(variant="primary")
gr.Examples(inputs=url, examples=example_urls)
with gr.Column():
reviews = [gr.Text(label="Review " + str(i + 1)) for i in range(max_review_count)]
image = gr.Image(label="Amazon Product Image", interactive=False)
button.click(fn=scrape_amazon_reviews, inputs=url, outputs=reviews + [image])
demo.launch(share=True) |