# -*- coding: utf-8 -*- # =================================================== # # Author : Fan Zhang # Email : zhangfan@baai.ac.cn # Institute : Beijing Academy of Artificial Intelligence (BAAI) # Create On : 2023-12-11 15:35 # Last Modified : 2023-12-20 05:43 # File Name : generation_frontend.py # Description : # # =================================================== import base64 import json import io import time from PIL import Image import requests import gradio as gr from .constants import EVA_IMAGE_SIZE from .meta import ConvMeta, Role, DataMeta from .utils import frontend_logger as logging CONTROLLER_URL = "" def submit( meta, enable_grd, left, top, right, bottom, image, text, ): if meta is None: meta = ConvMeta() meta.pop_error() if meta.has_gen: meta.clear() if enable_grd: if text == "" and image is None: logging.info(f"{meta.log_id}: invalid input: no valid data for grounding input") gr.Error("text or image must be given if enable grounding generation") return meta.format_chatbot(), meta, False, 0, 0, EVA_IMAGE_SIZE, EVA_IMAGE_SIZE, None, "" meta.append(Role.USER, DataMeta.build(text=text, image=image, coordinate=[left, top, right, bottom])) elif image is not None and text != "": logging.info(f"{meta.log_id}: invalid input: give text and image simultaneously for single modality input") gr.Error("Do not submit text and image data at the same time!!!") return meta.format_chatbot(), meta, False, 0, 0, EVA_IMAGE_SIZE, EVA_IMAGE_SIZE, None, "" elif image is not None: meta.append(Role.USER, DataMeta.build(image=image)) elif text != "": meta.append(Role.USER, DataMeta.build(text=text)) return meta.format_chatbot(), meta, False, 0, 0, EVA_IMAGE_SIZE, EVA_IMAGE_SIZE, None, "" def clear_history(meta): if meta is None: meta = ConvMeta() meta.clear() return meta.format_chatbot(), meta def generate(meta, classifier_free_guidance, steps): if meta is None: meta = ConvMeta() meta.pop_error() meta.pop() prompt = meta.format_prompt() prompt_list, image_list = [], {} for idx, p in enumerate(prompt): if isinstance(p, Image.Image): key = f"[]" prompt_list.append(["IMAGE", key]) buf = io.BytesIO() p.save(buf, format="PNG") image_list[key] = (key, io.BytesIO(buf.getvalue()), "image/png") else: prompt_list.append(["TEXT", p]) if len(image_list) == 0: image_list = None logging.info(f"{meta.log_id}: construct generation reqeust with prompt {prompt_list}") t0 = time.time() try: rsp = requests.post( CONTROLLER_URL + "/v1/mmg", files=image_list, data={ "log_id": meta.log_id, "prompt": json.dumps(prompt_list), "classifier_free_guidance": classifier_free_guidance, "steps": steps, }, ) except Exception as ex: rsp = requests.Response() rsp.status_code = 1099 rsp._content = str(ex).encode() t1 = time.time() logging.info(f"{meta.log_id}: get response with status code: {rsp.status_code}, time: {(t1-t0)*1000:.3f}ms") if rsp.status_code == requests.codes.ok: content = json.loads(rsp.text) if content["code"] == 0: image = Image.open(io.BytesIO(base64.b64decode(content["data"]))) meta.append(Role.ASSISTANT, DataMeta.build(image=image, resize=False)) else: meta.append(Role.ASSISTANT, DataMeta.build(text=f"GENERATE FAILED: {content['data']}", is_error=True)) else: meta.append(Role.ASSISTANT, DataMeta.build(text=f"GENERATE FAILED: http failed with code {rsp.status_code}, msg: {rsp.text}", is_error=True)) return meta.format_chatbot(), meta def build_generation(args): global CONTROLLER_URL CONTROLLER_URL = args.controller_url with gr.Blocks(title="Emu", theme=gr.themes.Default(primary_hue="blue", secondary_hue="blue")) as demo: state = gr.State() with gr.Row(): with gr.Column(scale=2): with gr.Row(): imagebox = gr.Image(type="pil") with gr.Row(): with gr.Accordion("Grounding Parameters", open=True, visible=True) as grounding_row: enable_grd = gr.Checkbox(label="Enable") left = gr.Slider(minimum=0, maximum=EVA_IMAGE_SIZE, value=0, step=1, interactive=True, label="left") top = gr.Slider(minimum=0, maximum=EVA_IMAGE_SIZE, value=0, step=1, interactive=True, label="top") right = gr.Slider(minimum=0, maximum=EVA_IMAGE_SIZE, value=EVA_IMAGE_SIZE, step=1, interactive=True, label="right") bottom = gr.Slider(minimum=0, maximum=EVA_IMAGE_SIZE, value=EVA_IMAGE_SIZE, step=1, interactive=True, label="bottom") with gr.Row(): with gr.Accordion("Diffusion Parameters", open=True, visible=True) as parameters_row: cfg = gr.Slider(minimum=1, maximum=30, value=3, step=0.5, interactive=True, label="classifier free guidance") steps = gr.Slider(minimum=1, maximum=100, value=50, step=1, interactive=True, label="steps") with gr.Column(scale=6): chatbot = gr.Chatbot( elem_id="chatbot", label="Emu Chatbot", visible=True, height=720, ) with gr.Row(): with gr.Column(scale=8): textbox = gr.Textbox( show_label=False, placeholder="Enter text and add to prompt", visible=True, container=False, ) with gr.Column(scale=1, min_width=60): add_btn = gr.Button(value="Add") with gr.Row(visible=True) as button_row: # upvote_btn = gr.Button(value="👍 Upvote", interactive=False) # downvote_btn = gr.Button(value="👎 Downvote", interactive=False) # regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=False) clear_btn = gr.Button(value="🗑️ Clear History") generate_btn = gr.Button(value="Generate") clear_btn.click(clear_history, inputs=state, outputs=[chatbot, state]) textbox.submit( submit, inputs=[ state, enable_grd, left, top, right, bottom, imagebox, textbox, ], outputs=[ chatbot, state, enable_grd, left, top, right, bottom, imagebox, textbox, ], ) add_btn.click( submit, inputs=[ state, enable_grd, left, top, right, bottom, imagebox, textbox, ], outputs=[ chatbot, state, enable_grd, left, top, right, bottom, imagebox, textbox, ], ) generate_btn.click( generate, inputs=[ state, cfg, steps, ], outputs=[ chatbot, state, ] ) return demo