Update README.md

README.md

@@ -1,35 +1 @@
-import requests
-from PIL import Image
-from transformers import BlipProcessor, BlipForConditionalGeneration
-
-# Initialize processor and model
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to("cuda")
-
-# Function to process and caption an image from a URL
-def caption_image(image_url):
-    try:
-        # Load image from the provided URL
-        raw_image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
-        
-        # Conditional image captioning
-        text = "a photography of"
-        inputs = processor(raw_image, text, return_tensors="pt").to("cuda")
-        out = model.generate(**inputs)
-        conditional_caption = processor.decode(out[0], skip_special_tokens=True)
-
-        # Unconditional image captioning
-        inputs = processor(raw_image, return_tensors="pt").to("cuda")
-        out = model.generate(**inputs)
-        unconditional_caption = processor.decode(out[0], skip_special_tokens=True)
-
-        # Print the results
-        print("Conditional Caption:", conditional_caption)
-        print("Unconditional Caption:", unconditional_caption)
-    
-    except Exception as e:
-        print(f"Error occurred: {e}")
-
-# Get image URL from user input
-image_url = input("Enter the image URL: ")
-caption_image(image_url)
+this is image captioning model