text-generation-webui/extensions/send_pictures/script.py
2023-02-25 03:00:19 -03:00

65 lines
2.2 KiB
Python

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
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 input_modifier(string):
"""
This function is applied to your text inputs before
they are fed into the model.
"""
return string
def output_modifier(string):
"""
This function is applied to the model outputs.
"""
return string
def bot_prefix_modifier(string):
"""
This function is only applied in chat mode. It modifies
the prefix text for the Bot and can be used to bias its
behavior.
"""
return string
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'
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)
picture_select.upload(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)
picture_select.upload(lambda : None, [], [picture_select], show_progress=False)