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| from smolagents import Tool | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| from PIL import Image | |
| import torch | |
| class ImageDescriberTool(Tool): | |
| name = "image_describer" | |
| description = """ | |
| Analyzes image and provide what is represented on it. | |
| Supported image extensions: .png, .jpg, .jpeg, .bmp, .svg. | |
| """ | |
| inputs = { | |
| "image_path": { | |
| "type": "string", | |
| "description": "The path to the image file", | |
| } | |
| } | |
| output_type = "string" | |
| def __init__(self): | |
| super().__init__() | |
| self.device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model_name = "Salesforce/blip-image-captioning-large" | |
| self.processor = BlipProcessor.from_pretrained(model_name) | |
| self.model = BlipForConditionalGeneration.from_pretrained(model_name).to(self.device) | |
| def forward(self, image_path: str) -> str: | |
| try: | |
| image = Image.open(image_path).convert('RGB') | |
| inputs = self.processor(image, return_tensors="pt").to(self.device) | |
| out = self.model.generate(**inputs) | |
| img_description = self.processor.decode(out[0], skip_special_tokens=True) | |
| return img_description | |
| except Exception as e: | |
| return f"Error generating image description: {e}" | |