import base64 from io import BytesIO import gradio as gr import torch from transformers import BlipForConditionalGeneration, BlipProcessor from modules import chat, shared from modules.ui import gather_interface_values # 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)}”*' # lower the resolution of sent images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history picture.thumbnail((300, 300)) buffer = BytesIO() picture.save(buffer, format="JPEG") img_str = base64.b64encode(buffer.getvalue()).decode('utf-8') visible_text = f'{text}' return text, visible_text def ui(): picture_select = gr.Image(label='Send a picture', type='pil') # Prepare the input hijack, update the interface values, call the generation function, and clear the picture 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).then( gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then( lambda: None, None, picture_select, show_progress=False)