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Create app.py
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app.py
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import requests
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from PIL import Image
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from io import BytesIO
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from bs4 import BeautifulSoup
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from transformers import AutoProcessor, BlipForConditionalGeneration
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# Load the pretrained processor and model
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processor = # fill the pretrained model
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model = # load the blip model
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# URL of the page to scrape
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url = "https://en.wikipedia.org/wiki/IBM"
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# Download the page
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response = requests.get(url)
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# Parse the page with BeautifulSoup
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soup = BeautifulSoup(response.text, 'html.parser')
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# Find all img elements
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img_elements = soup.find_all('img')
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# Open a file to write the captions
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with open("captions.txt", "w") as caption_file:
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# Iterate over each img element
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for img_element in img_elements:
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img_url = img_element.get('src')
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# Skip if the image is an SVG or too small (likely an icon)
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if 'svg' in img_url or '1x1' in img_url:
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continue
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# Correct the URL if it's malformed
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if img_url.startswith('//'):
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img_url = 'https:' + img_url
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elif not img_url.startswith('http://') and not img_url.startswith('https://'):
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continue # Skip URLs that don't start with http:// or https://
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try:
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# Download the image
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response = requests.get(img_url)
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# Convert the image data to a PIL Image
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raw_image = Image.open(BytesIO(response.content))
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if raw_image.size[0] * raw_image.size[1] < 400: # Skip very small images
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continue
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raw_image = raw_image.convert('RGB')
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# Process the image
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inputs = processor(raw_image, return_tensors="pt")
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# Generate a caption for the image
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out = model.generate(**inputs, max_new_tokens=50)
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# Decode the generated tokens to text
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caption = processor.decode(out[0], skip_special_tokens=True)
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# Write the caption to the file, prepended by the image URL
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caption_file.write(f"{img_url}: {caption}\n")
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except Exception as e:
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print(f"Error processing image {img_url}: {e}")
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continue
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