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from transformers import BlipForConditionalGeneration |
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model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") |
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from transformers import AutoProcessor |
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processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") |
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from PIL import Image |
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import gradio as gr |
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def captioning(input): |
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image_tensors = processor(input, return_tensors="pt") |
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image_text_tensors = model.generate(**image_tensors) |
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output = processor.decode(image_text_tensors[0], skip_special_tokens=True) |
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return output |
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gr.close_all() |
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app = gr.Interface(fn=captioning, |
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inputs=[gr.Image(label="Laita tähä joku kuva", type="pil")], |
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outputs=[gr.Textbox(label="Mitä näkyy?")], |
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title="Harzan kuvan selitys aplikaatio", |
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description="Harzan ihme aplikaatio kertomaan mitä kuvassa on", |
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allow_flagging="never") |
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app.launch() |
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gr.close_all() |
