Upload image_captioning.py

image_captioning.py

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+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+# Load the models and processor
+processor_base = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model_base = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+processor_large = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
+
+# Define the function for unconditional image captioning
+def caption_image_unconditional(image):
+    inputs = processor_base(image, return_tensors="pt")
+    outputs = model_base.generate(**inputs)
+    caption = processor_base.decode(outputs[0], skip_special_tokens=True)
+    return caption
+
+# Define the function for conditional image captioning
+def caption_image_conditional(image, text):
+    inputs = processor_large(image, text, return_tensors="pt")
+    outputs = model_large.generate(**inputs)
+    caption = processor_large.decode(outputs[0], skip_special_tokens=True)
+    return caption