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import base64 | |
from io import BytesIO | |
import gradio as gr | |
import torch | |
from transformers import BlipForConditionalGeneration, BlipProcessor | |
import modules.chat as chat | |
import modules.shared as shared | |
# If 'state' is True, will hijack the next chat generation with | |
# custom input text given by 'value' in the format [text, visible_text] | |
input_hijack = { | |
'state': False, | |
'value': ["", ""] | |
} | |
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float32).to("cpu") | |
def caption_image(raw_image): | |
inputs = processor(raw_image.convert('RGB'), return_tensors="pt").to("cpu", torch.float32) | |
out = model.generate(**inputs, max_new_tokens=100) | |
return processor.decode(out[0], skip_special_tokens=True) | |
def generate_chat_picture(picture, name1, name2): | |
text = f'*{name1} sends {name2} a picture that contains the following: "{caption_image(picture)}"*' | |
buffer = BytesIO() | |
picture.save(buffer, format="JPEG") | |
img_str = base64.b64encode(buffer.getvalue()).decode('utf-8') | |
visible_text = f'<img src="data:image/jpeg;base64,{img_str}">' | |
return text, visible_text | |
def ui(): | |
picture_select = gr.Image(label='Send a picture', type='pil') | |
function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper' | |
# Prepare the hijack with custom inputs | |
picture_select.upload(lambda picture, name1, name2: input_hijack.update({"state": True, "value": generate_chat_picture(picture, name1, name2)}), [picture_select, shared.gradio['name1'], shared.gradio['name2']], None) | |
# Call the generation function | |
picture_select.upload(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream) | |
# Clear the picture from the upload field | |
picture_select.upload(lambda : None, [], [picture_select], show_progress=False) | |