Update app.py

app.py

@@ -1,1797 +1,226 @@
-from io import BytesIO
-from math import inf
+# Copyright (c) Microsoft
+# Modified from Visual ChatGPT Project https://github.com/microsoft/TaskMatrix/blob/main/visual_chatgpt.py
+
 import os
-import base64
-import json
 import gradio as gr
-import numpy as np
-from gradio import processing_utils
-import requests
-from packaging import version
-from PIL import Image, ImageDraw
-import functools
-import emoji
-from langchain.llms.openai import OpenAI
-from caption_anything.model import CaptionAnything
-from caption_anything.utils.image_editing_utils import create_bubble_frame
-from caption_anything.utils.utils import mask_painter, seg_model_map, prepare_segmenter, image_resize
-from caption_anything.utils.parser import parse_augment
-from caption_anything.captioner import build_captioner
-from caption_anything.text_refiner import build_text_refiner
-from caption_anything.segmenter import build_segmenter
-from chatbox import ConversationBot, build_chatbot_tools, get_new_image_name
-from segment_anything import sam_model_registry
-import easyocr
 import re
-import edge_tts
-import asyncio
-import cv2
-# import tts 
-
-###############################################################################
-############# this part is for 3D generate #############
-###############################################################################
-
-
-# import spaces  #
-
-import os
-import imageio
+import uuid
+from PIL import Image, ImageDraw, ImageOps
 import numpy as np
-import torch
-import rembg
-from PIL import Image
-from torchvision.transforms import v2
-from pytorch_lightning import seed_everything
-from omegaconf import OmegaConf
-from einops import rearrange, repeat
-from tqdm import tqdm
-from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
-
-from src.utils.train_util import instantiate_from_config
-from src.utils.camera_util import (
-    FOV_to_intrinsics, 
-    get_zero123plus_input_cameras,
-    get_circular_camera_poses,
-)
-from src.utils.mesh_util import save_obj, save_glb
-from src.utils.infer_util import remove_background, resize_foreground, images_to_video
-
-import tempfile
-from functools import partial
-
-from huggingface_hub import hf_hub_download
-
-
-
-
-def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
-    """
-    Get the rendering camera parameters.
-    """
-    c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
-    if is_flexicubes:
-        cameras = torch.linalg.inv(c2ws)
-        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1)
-    else:
-        extrinsics = c2ws.flatten(-2)
-        intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2)
-        cameras = torch.cat([extrinsics, intrinsics], dim=-1)
-        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1)
-    return cameras
-
-
-def images_to_video(images, output_path, fps=30):
-    # images: (N, C, H, W)
-    os.makedirs(os.path.dirname(output_path), exist_ok=True)
-    frames = []
-    for i in range(images.shape[0]):
-        frame = (images[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8).clip(0, 255)
-        assert frame.shape[0] == images.shape[2] and frame.shape[1] == images.shape[3], \
-            f"Frame shape mismatch: {frame.shape} vs {images.shape}"
-        assert frame.min() >= 0 and frame.max() <= 255, \
-            f"Frame value out of range: {frame.min()} ~ {frame.max()}"
-        frames.append(frame)
-    imageio.mimwrite(output_path, np.stack(frames), fps=fps, codec='h264')
-
-
-###############################################################################
-# Configuration.
-###############################################################################
-
-import shutil
-
-def find_cuda():
-    # Check if CUDA_HOME or CUDA_PATH environment variables are set
-    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
-
-    if cuda_home and os.path.exists(cuda_home):
-        return cuda_home
-
-    # Search for the nvcc executable in the system's PATH
-    nvcc_path = shutil.which('nvcc')
-
-    if nvcc_path:
-        # Remove the 'bin/nvcc' part to get the CUDA installation path
-        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
-        return cuda_path
-
-    return None
-
-cuda_path = find_cuda()
-
-if cuda_path:
-    print(f"CUDA installation found at: {cuda_path}")
-else:
-    print("CUDA installation not found")
-
-config_path = 'configs/instant-nerf-base.yaml'
-config = OmegaConf.load(config_path)
-config_name = os.path.basename(config_path).replace('.yaml', '')
-model_config = config.model_config
-infer_config = config.infer_config
-
-IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False
-
-device = torch.device('cuda')
-
-# load diffusion model
-print('Loading diffusion model ...')
-pipeline = DiffusionPipeline.from_pretrained(
-    "sudo-ai/zero123plus-v1.2", 
-    custom_pipeline="zero123plus",
-    torch_dtype=torch.float16,
-)
-pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
-    pipeline.scheduler.config, timestep_spacing='trailing'
-)
-
-# load custom white-background UNet
-unet_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model")
-state_dict = torch.load(unet_ckpt_path, map_location='cpu')
-pipeline.unet.load_state_dict(state_dict, strict=True)
-
-pipeline = pipeline.to(device)
-
-# load reconstruction model
-print('Loading reconstruction model ...')
-model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="instant_nerf_base.ckpt", repo_type="model")
-model0 = instantiate_from_config(model_config)
-state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
-state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k}
-model0.load_state_dict(state_dict, strict=True)
-
-model0 = model0.to(device)
-
-print('Loading Finished!')
-
-
-def check_input_image(input_image):
-    if input_image is None:
-        raise gr.Error("No image uploaded!")
-        image = None
-    else:
-        image = Image.open(input_image)
-    return image
-
-def preprocess(input_image, do_remove_background):
-
-    rembg_session = rembg.new_session() if do_remove_background else None
-
-    if do_remove_background:
-        input_image = remove_background(input_image, rembg_session)
-        input_image = resize_foreground(input_image, 0.85)
-
-    return input_image
-
-
-# @spaces.GPU
-def generate_mvs(input_image, sample_steps, sample_seed):
-
-    seed_everything(sample_seed)
-    
-    # sampling
-    z123_image = pipeline(
-        input_image, 
-        num_inference_steps=sample_steps
-    ).images[0]
-
-    show_image = np.asarray(z123_image, dtype=np.uint8)
-    show_image = torch.from_numpy(show_image)     # (960, 640, 3)
-    show_image = rearrange(show_image, '(n h) (m w) c -> (n m) h w c', n=3, m=2)
-    show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3)
-    show_image = Image.fromarray(show_image.numpy())
-
-    return z123_image, show_image
-
-
-# @spaces.GPU
-def make3d(images):
-
-    global model0
-    if IS_FLEXICUBES:
-        model0.init_flexicubes_geometry(device)
-    model0 = model0.eval()
-
-    images = np.asarray(images, dtype=np.float32) / 255.0
-    images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float()     # (3, 960, 640)
-    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)        # (6, 3, 320, 320)
-
-    input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).to(device)
-    render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device)
-
-    images = images.unsqueeze(0).to(device)
-    images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1)
-
-    mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name
-    print(mesh_fpath)
-    mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
-    mesh_dirname = os.path.dirname(mesh_fpath)
-    video_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.mp4")
-    mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")
-
-    with torch.no_grad():
-        # get triplane
-        planes = model0.forward_planes(images, input_cameras)
-
-        # # get video
-        # chunk_size = 20 if IS_FLEXICUBES else 1
-        # render_size = 384
-        
-        # frames = []
-        # for i in tqdm(range(0, render_cameras.shape[1], chunk_size)):
-        #     if IS_FLEXICUBES:
-        #         frame = model.forward_geometry(
-        #             planes,
-        #             render_cameras[:, i:i+chunk_size],
-        #             render_size=render_size,
-        #         )['img']
-        #     else:
-        #         frame = model.synthesizer(
-        #             planes,
-        #             cameras=render_cameras[:, i:i+chunk_size],
-        #             render_size=render_size,
-        #         )['images_rgb']
-        #     frames.append(frame)
-        # frames = torch.cat(frames, dim=1)
-
-        # images_to_video(
-        #     frames[0],
-        #     video_fpath,
-        #     fps=30,
-        # )
-
-        # print(f"Video saved to {video_fpath}")
-
-        # get mesh
-        mesh_out = model0.extract_mesh(
-            planes,
-            use_texture_map=False,
-            **infer_config,
-        )
-
-        vertices, faces, vertex_colors = mesh_out
-        vertices = vertices[:, [1, 2, 0]]
-        
-        save_glb(vertices, faces, vertex_colors, mesh_glb_fpath)
-        save_obj(vertices, faces, vertex_colors, mesh_fpath)
-        
-        print(f"Mesh saved to {mesh_fpath}")
-
-    return mesh_fpath, mesh_glb_fpath
-
-
-###############################################################################
-############# above part is for 3D generate #############
-###############################################################################
-
-
-###############################################################################
-############# this part is for text to image #############
-###############################################################################
-
-# Use environment variables for flexibility
-MODEL_ID = os.getenv("MODEL_ID", "sd-community/sdxl-flash")
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
-BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))  # Allow generating multiple images at once
-
-# Determine device and load model outside of function for efficiency
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-pipe = StableDiffusionXLPipeline.from_pretrained(
-    MODEL_ID,
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    use_safetensors=True,
-    add_watermarker=False,
-).to(device)
-pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-
-# Torch compile for potential speedup (experimental)
-if USE_TORCH_COMPILE:
-    pipe.compile()
-
-# CPU offloading for larger RAM capacity (experimental)
-if ENABLE_CPU_OFFLOAD:
-    pipe.enable_model_cpu_offload()
-
-MAX_SEED = np.iinfo(np.int32).max
-
-def save_image(img):
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
+import argparse
+import inspect
 
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-# @spaces.GPU(duration=30, queue=False)
-def generate(
-    prompt: str,
-    negative_prompt: str = "",
-    use_negative_prompt: bool = False,
-    seed: int = 1,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 3,
-    num_inference_steps: int = 30,
-    randomize_seed: bool = False, 
-    num_images: int = 4,  # Number of images to generate
-    use_resolution_binning: bool = True,
-    progress=gr.Progress(track_tqdm=True),
-):
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator(device=device).manual_seed(seed)
-
-    # Improved options handling
-    options = {
-        "prompt": [prompt] * num_images,
-        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
-        "width": width,
-        "height": height,
-        "guidance_scale": guidance_scale,
-        "num_inference_steps": num_inference_steps,
-        "generator": generator,
-        "output_type": "pil",
-    }
-
-    # Use resolution binning for faster generation with less VRAM usage
-    # if use_resolution_binning:
-    #     options["use_resolution_binning"] = True
-
-    # Generate images potentially in batches
-    images = []
-    for i in range(0, num_images, BATCH_SIZE):
-        batch_options = options.copy()
-        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
-        if "negative_prompt" in batch_options:
-            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
-        images.extend(pipe(**batch_options).images)
+from langchain.agents.initialize import initialize_agent
+from langchain.agents.tools import Tool
+from langchain.chains.conversation.memory import ConversationBufferMemory
+from langchain.llms.openai import OpenAIChat
+import torch
+from PIL import Image, ImageDraw, ImageOps
+from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering
 
-    image_paths = [save_image(img) for img in images]
-    return image_paths, seed
+# openai.api_version = '2020-11-07' 
+os.environ["OPENAI_API_VERSION"] = '2020-11-07'
 
-examples = [
-    "a cat eating a piece of cheese",
-    "a ROBOT riding a BLUE horse on Mars, photorealistic, 4k",
-    "Ironman VS Hulk, ultrarealistic",
-    "Astronaut in a jungle, cold color palette, oil pastel, detailed, 8k",
-    "An alien holding a sign board containing the word 'Flash', futuristic, neonpunk",
-    "Kids going to school, Anime style"
-]
+VISUAL_CHATGPT_PREFIX = """
+    I want you to act as an art connoisseur, providing in-depth and insightful analysis on various artworks. Your responses should reflect a deep understanding of art history, techniques, and cultural contexts, offering users a rich and nuanced perspective.
 
+You can engage in natural-sounding conversations, generate human-like text based on input, and provide relevant, coherent responses on art-related topics."""
 
 
+# TOOLS:
+# ------
 
-###############################################################################
-############# above part is for text to image #############
-###############################################################################
+# Visual ChatGPT  has access to the following tools:"""
 
+VISUAL_CHATGPT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format:
 
-css = """
-#warning {background-color: #FFCCCB}
-.chatbot {
-        padding: 0 !important;
-        margin: 0 !important;
-    }
-"""
-filtered_language_dict = {
-    'English': 'en-US-JennyNeural',
-    'Chinese': 'zh-CN-XiaoxiaoNeural',
-    'French': 'fr-FR-DeniseNeural',
-    'Spanish': 'es-MX-DaliaNeural',
-    'Arabic': 'ar-SA-ZariyahNeural',
-    'Portuguese': 'pt-BR-FranciscaNeural',
-    'Cantonese': 'zh-HK-HiuGaaiNeural'
-}
+"Thought: Do I need to use a tool? Yes
+Action: the action to take, should be one of [{tool_names}], remember the action must to be one tool
+Action Input: the input to the action
+Observation: the result of the action"
 
-focus_map = {
-"CFV-D":0, 
-"CFV-DA":1, 
-"CFV-DAI":2,
-"PFV-DDA":3
-}
+When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
 
-'''
-prompt_list = [
-'Wiki_caption: {Wiki_caption}, you have to generate a caption according to the image and wiki caption. Around {length} words of {sentiment} sentiment in {language}.',
-'Wiki_caption: {Wiki_caption}, you have to select sentences from wiki caption that describe the surrounding objects that may be associated with the picture object. Around {length} words of {sentiment} sentiment in {language}.',
-'Wiki_caption: {Wiki_caption}. You have to choose sentences from the wiki caption that describe unrelated objects to the image. Around {length} words of {sentiment} sentiment in {language}.',
-'Wiki_caption: {Wiki_caption}. You have to choose sentences from the wiki caption that describe unrelated objects to the image. Around {length} words of {sentiment} sentiment in {language}.'
-]
+"Thought: Do I need to use a tool? No
+{ai_prefix}: [your response here]"
 
-prompt_list = [
-'Wiki_caption: {Wiki_caption}, you have to help me understand what is about the selected object and list one fact (describes the object but does not include analysis)as markdown outline with appropriate emojis that describes what you see according to the image and wiki caption. Around {length} words of {sentiment} sentiment in {language}.',
-'Wiki_caption: {Wiki_caption}, you have to help me understand what is about the selected object and list one fact and one analysis as markdown outline with appropriate emojis that describes what you see according to the image and wiki caption. Around {length} words of {sentiment} sentiment in {language}.',
-'Wiki_caption: {Wiki_caption}, you have to help me understand what is about the selected object and list one fact and one analysis and one interpret as markdown outline with appropriate emojis that describes what you see according to the image and wiki caption. Around {length} words of {sentiment} sentiment in {language}.',
-'Wiki_caption: {Wiki_caption}, you have to help me understand what is about the selected object and the objects that may be related to the selected object and list one fact of selected object, one fact of related object and one analysis as markdown outline with appropriate emojis that describes what you see according to the image and wiki caption. Around {length} words of {sentiment} sentiment in {language}.'
-]
-'''
-prompt_list = [
-'Wiki_caption: {Wiki_caption}, you have to help me understand what is about the selected object and list one fact (describes the selected object but does not include analysis)as markdown outline with appropriate emojis that describes what you see according to the image and wiki caption. Each point listed is to be in {language} language, with a response length of about {length} words.',
-'Wiki_caption: {Wiki_caption}, you have to help me understand what is about the selected object and list one fact and one analysis as markdown outline with appropriate emojis that describes what you see according to the image and wiki caption. Each point listed is to be in {language} language, with a response length of about {length} words.',
-'Wiki_caption: {Wiki_caption}, you have to help me understand what is about the selected object and list one fact and one analysis and one interpret as markdown outline with appropriate emojis that describes what you see according to the image and wiki caption. Each point listed is to be in {language} language, with a response length of about {length} words.',
-'Wiki_caption: {Wiki_caption}, you have to help me understand what is about the selected object and the objects that may be related to the selected object and list one fact of selected object, one fact of related object and one analysis as markdown outline with appropriate emojis that describes what you see according to the image and wiki caption. Each point listed is to be in {language} language, with a response length of about {length} words.'
-]
-    
-    
-gpt_state = 0
-VOICE = "en-GB-SoniaNeural"
-article = """
-<div style='margin:20px auto;'>
-<p>By using this demo you agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml</p>
-</div>
 """
 
-args = parse_augment()
-args.segmenter = "huge"
-args.segmenter_checkpoint = "sam_vit_h_4b8939.pth"
-args.clip_filter = True
-if args.segmenter_checkpoint is None:
-    _, segmenter_checkpoint = prepare_segmenter(args.segmenter)
-else:
-    segmenter_checkpoint = args.segmenter_checkpoint
-    
-shared_captioner = build_captioner(args.captioner, args.device, args)
-shared_sam_model = sam_model_registry[seg_model_map[args.segmenter]](checkpoint=segmenter_checkpoint).to(args.device)
-ocr_lang = ["ch_tra", "en"]
-shared_ocr_reader = easyocr.Reader(ocr_lang)
-tools_dict = {e.split('_')[0].strip(): e.split('_')[1].strip() for e in args.chat_tools_dict.split(',')}
-shared_chatbot_tools = build_chatbot_tools(tools_dict)
-
-
-class ImageSketcher(gr.Image):
-    """
-    Fix the bug of gradio.Image that cannot upload with tool == 'sketch'.
-    """
-
-    is_template = True  # Magic to make this work with gradio.Block, don't remove unless you know what you're doing.
-
-    def __init__(self, **kwargs):
-        super().__init__(tool="sketch", **kwargs)
-
-    def preprocess(self, x):
-        if self.tool == 'sketch' and self.source in ["upload", "webcam"]:
-            assert isinstance(x, dict)
-            if x['mask'] is None:
-                decode_image = processing_utils.decode_base64_to_image(x['image'])
-                width, height = decode_image.size
-                mask = np.zeros((height, width, 4), dtype=np.uint8)
-                mask[..., -1] = 255
-                mask = self.postprocess(mask)
-                x['mask'] = mask
+VISUAL_CHATGPT_SUFFIX = """
+Begin Chatting!
+
+Previous conversation history:
+{chat_history}
+
+New input: {input}
+As a language model, you must repeatly to use VQA tools to observe images. You response should be consistent with the outputs of the VQA tool instead of imagination. Do not repeat asking the same question. 
+
+Thought: Do I need to use a tool? {agent_scratchpad} (You are strictly to use the aforementioned "Thought/Action/Action Input/Observation" format as the answer.)"""
+
+os.makedirs('chat_image', exist_ok=True)
+
+
+def prompts(name, description):
+    def decorator(func):
+        func.name = name
+        func.description = description
+        return func
+    return decorator
+
+def cut_dialogue_history(history_memory, keep_last_n_words=500):
+    if history_memory is None or len(history_memory) == 0:
+        return history_memory
+    tokens = history_memory.split()
+    n_tokens = len(tokens)
+    print(f"history_memory:{history_memory}, n_tokens: {n_tokens}")
+    if n_tokens < keep_last_n_words:
+        return history_memory
+    paragraphs = history_memory.split('\n')
+    last_n_tokens = n_tokens
+    while last_n_tokens >= keep_last_n_words:
+        last_n_tokens -= len(paragraphs[0].split(' '))
+        paragraphs = paragraphs[1:]
+    return '\n' + '\n'.join(paragraphs)
+
+def get_new_image_name(folder='chat_image', func_name="update"):
+    this_new_uuid = str(uuid.uuid4())[:8]
+    new_file_name = f'{func_name}_{this_new_uuid}.png'
+    return os.path.join(folder, new_file_name)
+
+class VisualQuestionAnswering:
+    def __init__(self, device):
+        print(f"Initializing VisualQuestionAnswering to {device}")
+        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
+        self.device = device
+        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
+        self.model = BlipForQuestionAnswering.from_pretrained(
+            "Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype).to(self.device)
+        # self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-capfilt-large")
+        # self.model = BlipForQuestionAnswering.from_pretrained(
+            # "Salesforce/blip-vqa-capfilt-large", torch_dtype=self.torch_dtype).to(self.device)
+
+    @prompts(name="Answer Question About The Image",
+             description="VQA tool is useful when you need an answer for a question based on an image. "
+                         "like: what is the color of an object, how many cats in this figure, where is the child sitting, what does the cat doing, why is he laughing."
+                         "The input to this tool should be a comma separated string of two, representing the image path and the question.")
+    def inference(self, inputs):
+        image_path, question = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
+        raw_image = Image.open(image_path).convert('RGB')
+        inputs = self.processor(raw_image, question, return_tensors="pt").to(self.device, self.torch_dtype)
+        out = self.model.generate(**inputs)
+        answer = self.processor.decode(out[0], skip_special_tokens=True)
+        print(f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input Question: {question}, "
+              f"Output Answer: {answer}")
+        return answer
+    
+def build_chatbot_tools(load_dict):
+    print(f"Initializing ChatBot, load_dict={load_dict}")
+    models = {}
+    # Load Basic Foundation Models
+    for class_name, device in load_dict.items():
+        models[class_name] = globals()[class_name](device=device)
+
+    # Load Template Foundation Models
+    for class_name, module in globals().items():
+        if getattr(module, 'template_model', False):
+            template_required_names = {k for k in inspect.signature(module.__init__).parameters.keys() if k!='self'}
+            loaded_names = set([type(e).__name__ for e in models.values()])
+            if template_required_names.issubset(loaded_names):
+                models[class_name] = globals()[class_name](
+                    **{name: models[name] for name in template_required_names})
                 
-        return super().preprocess(x)
-
-
-def build_caption_anything_with_models(args, api_key="", captioner=None, sam_model=None, ocr_reader=None, text_refiner=None,
-                                       session_id=None):
-    segmenter = build_segmenter(args.segmenter, args.device, args, model=sam_model)
-    captioner = captioner
-    if session_id is not None:
-        print('Init caption anything for session {}'.format(session_id))
-    return CaptionAnything(args, api_key, captioner=captioner, segmenter=segmenter, ocr_reader=ocr_reader, text_refiner=text_refiner)
-
-
-def validate_api_key(api_key):
-    api_key = str(api_key).strip()
-    print(api_key)
-    try:
-        test_llm = OpenAI(model_name="gpt-3.5-turbo", temperature=0, openai_api_key=api_key)
-        response = test_llm("Test API call")
-        print(response)
-        return True
-    except Exception as e:
-        print(f"API key validation failed: {e}")
-        return False
-
-
-def init_openai_api_key(api_key=""):
-    text_refiner = None
-    visual_chatgpt = None
-    if api_key and len(api_key) > 30:
-        print(api_key)
-        if validate_api_key(api_key):
-            try:
-                text_refiner = build_text_refiner(args.text_refiner, args.device, args, api_key)
-                assert len(text_refiner.llm('hi')) > 0 # test
-                visual_chatgpt = ConversationBot(shared_chatbot_tools, api_key)
-            except Exception as e:
-                print(f"Error initializing TextRefiner or ConversationBot: {e}")
-                text_refiner = None
-                visual_chatgpt = None
-        else:
-            print("Invalid API key.")
-    else:
-        print("API key is too short.")
-    print(text_refiner)
-    openai_available = text_refiner is not None
-    if openai_available:
-
-        global gpt_state
-        gpt_state=1
-        # return [gr.update(visible=True)]+[gr.update(visible=False)]+[gr.update(visible=True)]*3+[gr.update(visible=False)]+ [gr.update(visible=False)]*3 + [text_refiner, visual_chatgpt, None]+[gr.update(visible=True)]*3
-        return [gr.update(visible=True)]+[gr.update(visible=False)]+[gr.update(visible=True)]*3+[gr.update(visible=False)]+ [gr.update(visible=False)]*3 + [text_refiner, visual_chatgpt, None]+[gr.update(visible=True)]*2
-    else:
-        gpt_state=0
-        # return [gr.update(visible=False)]*7 + [gr.update(visible=True)]*2 + [text_refiner, visual_chatgpt, 'Your OpenAI API Key is not available']+[gr.update(visible=False)]*3
-        return [gr.update(visible=False)]*7 + [gr.update(visible=True)]*2 + [text_refiner, visual_chatgpt, 'Your OpenAI API Key is not available']+[gr.update(visible=False)]*2
-        
-def init_wo_openai_api_key():
-        global gpt_state
-        gpt_state=0
-        # return  [gr.update(visible=False)]*4 + [gr.update(visible=True)]+ [gr.update(visible=False)]+[gr.update(visible=True)]+[gr.update(visible=False)]*2 + [None, None, None]+[gr.update(visible=False)]*3
-        return  [gr.update(visible=False)]*4 + [gr.update(visible=True)]+ [gr.update(visible=False)]+[gr.update(visible=True)]+[gr.update(visible=False)]*2 + [None, None, None]+[gr.update(visible=False)]*2
-
-def get_click_prompt(chat_input, click_state, click_mode):
-    inputs = json.loads(chat_input)
-    if click_mode == 'Continuous':
-        points = click_state[0]
-        labels = click_state[1]
-        for input in inputs:
-            points.append(input[:2])
-            labels.append(input[2])
-    elif click_mode == 'Single':
-        points = []
-        labels = []
-        for input in inputs:
-            points.append(input[:2])
-            labels.append(input[2])
-        click_state[0] = points
-        click_state[1] = labels
-    else:
-        raise NotImplementedError
-
-    prompt = {
-        "prompt_type": ["click"],
-        "input_point": click_state[0],
-        "input_label": click_state[1],
-        "multimask_output": "True",
-    }
-    return prompt
-
-
-def update_click_state(click_state, caption, click_mode):
-    if click_mode == 'Continuous':
-        click_state[2].append(caption)
-    elif click_mode == 'Single':
-        click_state[2] = [caption]
-    else:
-        raise NotImplementedError
-
-async def chat_input_callback(*args):
-    visual_chatgpt, chat_input, click_state, state, aux_state ,language , autoplay = args
-    if visual_chatgpt is not None:
-        state, _, aux_state, _ = visual_chatgpt.run_text(chat_input, state, aux_state)
-        last_text, last_response = state[-1]
-        print("last response",last_response)
-        audio = await texttospeech(last_response,language,autoplay)
-        return state, state, aux_state,audio
-    else:
-        response = "Text refiner is not initilzed, please input openai api key."
-        state = state + [(chat_input, response)]
-        audio = await texttospeech(response,language,autoplay)
-        return state, state, None,audio
-
-
-        
-def upload_callback(image_input, state, visual_chatgpt=None, openai_api_key=None,language="English"):
-    if isinstance(image_input, dict):  # if upload from sketcher_input, input contains image and mask
-        image_input, mask = image_input['image'], image_input['mask']
-
-    click_state = [[], [], []]
-    image_input = image_resize(image_input, res=1024)
-
-    model = build_caption_anything_with_models(
-        args,
-        api_key="",
-        captioner=shared_captioner,
-        sam_model=shared_sam_model,
-        ocr_reader=shared_ocr_reader,
-        session_id=iface.app_id
-    )
-    model.segmenter.set_image(image_input)
-    image_embedding = model.image_embedding
-    original_size = model.original_size
-    input_size = model.input_size
-
-    if visual_chatgpt is not None:
-        print('upload_callback: add caption to chatGPT memory')
-        new_image_path = get_new_image_name('chat_image', func_name='upload')
-        image_input.save(new_image_path)
-        visual_chatgpt.current_image = new_image_path
-        img_caption = model.captioner.inference(image_input, filter=False, args={'text_prompt':''})['caption']
-        Human_prompt = f'\nHuman: The description of the image with path {new_image_path} is: {img_caption}. This information helps you to understand this image, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\". \n'
-        AI_prompt = "Received."
-        visual_chatgpt.global_prompt = Human_prompt + 'AI: ' + AI_prompt
-        visual_chatgpt.agent.memory.buffer = visual_chatgpt.agent.memory.buffer + visual_chatgpt.global_prompt 
-        parsed_data = get_image_gpt(openai_api_key, new_image_path,"Please provide the name, artist, year of creation, and material used for this painting. Return the information in dictionary format without any newline characters. If any information is unavailable, return \"None\" for that field. Format as follows: { \"name\": \"Name of the painting\",\"artist\": \"Name of the artist\", \"year\": \"Year of creation\", \"material\": \"Material used in the painting\" }.")
-        parsed_data = json.loads(parsed_data.replace("'", "\""))
-        name, artist, year, material= parsed_data["name"],parsed_data["artist"],parsed_data["year"], parsed_data["material"]
-        # artwork_info = f"<div>Painting: {name}<br>Artist name: {artist}<br>Year: {year}<br>Material: {material}</div>"
-        paragraph = get_image_gpt(openai_api_key, new_image_path,f"What's going on in this picture? in {language}")
-    
-    state = [
-    (
-        None,
-        f"🤖 Hi, I am EyeSee. Let's explore this painting {name} together. You can click on the area you're interested in and choose from four types of information: Description, Analysis, Interpretation, and Judgment. Based on your selection, I will provide you with the relevant information."
-    )
-]
-
-    return state, state, image_input, click_state, image_input, image_input, image_input, image_embedding, \
-        original_size, input_size, f"Name: {name}", f"Artist: {artist}", f"Year: {year}", f"Material: {material}",f"Name: {name}", f"Artist: {artist}", f"Year: {year}", f"Material: {material}",paragraph
-
-
+    tools = []
+    for instance in models.values():
+        for e in dir(instance):
+            if e.startswith('inference'):
+                func = getattr(instance, e)
+                tools.append(Tool(name=func.name, description=func.description, func=func))
+    return tools
+
+class ConversationBot:
+    def __init__(self, tools, api_key=""):
+        # load_dict = {'VisualQuestionAnswering':'cuda:0', 'ImageCaptioning':'cuda:1',...}
+        llm = OpenAIChat(model_name="gpt-4o", temperature=0.7, openai_api_key=api_key, model_kwargs={"api_version": "2020-11-07"})
+        self.llm = llm
+        self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output')
+        self.tools = tools
+        self.current_image = None
+        self.point_prompt = ""
+        self.global_prompt = ""
+        self.agent = initialize_agent(
+            self.tools,
+            self.llm,
+            agent="conversational-react-description",
+            verbose=True,
+            memory=self.memory,
+            return_intermediate_steps=True,
+            agent_kwargs={'prefix': VISUAL_CHATGPT_PREFIX, 'format_instructions': VISUAL_CHATGPT_FORMAT_INSTRUCTIONS,
+                          'suffix': VISUAL_CHATGPT_SUFFIX}, )
+
+    def constructe_intermediate_steps(self, agent_res):
+        ans = []
+        for action, output in agent_res:
+            if hasattr(action, "tool_input"):
+                use_tool = "Yes"
+                act = (f"Thought: Do I need to use a tool? {use_tool}\nAction: {action.tool}\nAction Input: {action.tool_input}", f"Observation: {output}")
+            else:
+                use_tool = "No"
+                act = (f"Thought: Do I need to use a tool? {use_tool}", f"AI: {output}")
+            act= list(map(lambda x: x.replace('\n', '<br>'), act))
+            ans.append(act)
+        return ans
+    
+    def run_text(self, text, state, aux_state):
+        self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500)
+        if self.point_prompt != "":
+            Human_prompt = f'\nHuman: {self.point_prompt}\n'
+            AI_prompt = 'Ok'
+            self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt
+            self.point_prompt = ""
+        res = self.agent({"input": text})
+        res['output'] = res['output'].replace("\\", "/")
+        response = re.sub('(chat_image/\S*png)', lambda m: f'![](/file={m.group(0)})*{m.group(0)}*', res['output'])
+        state = state + [(text, response)]
+        
+        aux_state = aux_state + [(f"User Input: {text}", None)]
+        aux_state = aux_state + self.constructe_intermediate_steps(res['intermediate_steps'])
+        print(f"\nProcessed run_text, Input text: {text}\nCurrent state: {state}\n"
+              f"Current Memory: {self.agent.memory.buffer}\n"
+              f"Aux state: {aux_state}\n"
+              )
+        return state, state, aux_state, aux_state
 
 
-def inference_click(image_input, point_prompt, click_mode, enable_wiki, language, sentiment, factuality,
-                    length, image_embedding, state, click_state, original_size, input_size, text_refiner, visual_chatgpt,
-                    out_state, click_index_state, input_mask_state, input_points_state, input_labels_state, evt: gr.SelectData):
-    click_index = evt.index
-
-    if point_prompt == 'Positive':
-        coordinate = "[[{}, {}, 1]]".format(str(click_index[0]), str(click_index[1]))
-    else:
-        coordinate = "[[{}, {}, 0]]".format(str(click_index[0]), str(click_index[1]))
-
-    prompt = get_click_prompt(coordinate, click_state, click_mode)
-    input_points = prompt['input_point']
-    input_labels = prompt['input_label']
-
-    controls = {'length': length,
-                'sentiment': sentiment,
-                'factuality': factuality,
-                'language': language}
-
-    model = build_caption_anything_with_models(
-        args,
-        api_key="",
-        captioner=shared_captioner,
-        sam_model=shared_sam_model,
-        ocr_reader=shared_ocr_reader,
-        text_refiner=text_refiner,
-        session_id=iface.app_id
-    )
-
-    model.setup(image_embedding, original_size, input_size, is_image_set=True)
-    
-    enable_wiki = True if enable_wiki in ['True', 'TRUE', 'true', True, 'Yes', 'YES', 'yes'] else False
-    out = model.inference(image_input, prompt, controls, disable_gpt=True, enable_wiki=enable_wiki, verbose=True, args={'clip_filter': False})[0]
-
-    state = state + [("Image point: {}, Input label: {}".format(prompt["input_point"], prompt["input_label"]), None)]
-    update_click_state(click_state, out['generated_captions']['raw_caption'], click_mode)
-    text = out['generated_captions']['raw_caption']
-    input_mask = np.array(out['mask'].convert('P'))
-    image_input_nobackground = mask_painter(np.array(image_input), input_mask,background_alpha=0)
-    image_input_withbackground=mask_painter(np.array(image_input), input_mask)
-
-    click_index_state = click_index
-    input_mask_state = input_mask
-    input_points_state = input_points
-    input_labels_state = input_labels
-    out_state = out  
-
-    if visual_chatgpt is not None:
-        print('inference_click: add caption to chatGPT memory')
-        new_crop_save_path = get_new_image_name('chat_image', func_name='crop')
-        Image.open(out["crop_save_path"]).save(new_crop_save_path)
-        point_prompt = f'You should primarly use tools on the selected regional image (description: {text}, path: {new_crop_save_path}), which is a part of the whole image (path: {visual_chatgpt.current_image}). If human mentioned some objects not in the selected region, you can use tools on the whole image.'
-        visual_chatgpt.point_prompt = point_prompt
-
- 
-    print("new crop save",new_crop_save_path)
-
-    yield state, state, click_state, image_input_nobackground, image_input_withbackground, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state,new_crop_save_path,image_input_nobackground
-    
-
-
-
-
-async def submit_caption(state, text_refiner, length, sentiment, factuality, language, 
-                   out_state, click_index_state, input_mask_state, input_points_state, input_labels_state,
-                   autoplay,paragraph,focus_type,openai_api_key,new_crop_save_path):
-    print("state",state)
-
-    click_index = click_index_state
-    
-    # if pre_click_index==click_index:
-    #     click_index = (click_index[0] - 1, click_index[1] - 1)
-    #     pre_click_index = click_index
-    # else:
-    #     pre_click_index = click_index
-    print("click_index",click_index)
-    print("input_points_state",input_points_state)
-    print("input_labels_state",input_labels_state)
-        
-    prompt=generate_prompt(paragraph,focus_type,length,sentiment,factuality,language)
-    
-    print("Prompt:", prompt)
-    print("click",click_index)
-    
-    # image_input = create_bubble_frame(np.array(image_input), generated_caption, click_index, input_mask,
-    #                                   input_points=input_points, input_labels=input_labels)
-
-
-    if not args.disable_gpt and text_refiner:
-        print("new crop save",new_crop_save_path)
-        focus_info=get_image_gpt(openai_api_key,new_crop_save_path,prompt)
-        if focus_info.startswith('"') and focus_info.endswith('"'):
-            focus_info=focus_info[1:-1]
-        focus_info=focus_info.replace('#', '')
-        # state = state + [(None, f"Wiki: {paragraph}")]
-        state = state + [(None, f"{focus_info}")]
-        print("new_cap",focus_info)
-        read_info = re.sub(r'[#[\]!*]','',focus_info)
-        read_info = emoji.replace_emoji(read_info,replace="")    
-        print("read info",read_info)
-
-        # refined_image_input = create_bubble_frame(np.array(origin_image_input), focus_info, click_index, input_mask,
-        #                                           input_points=input_points, input_labels=input_labels)
-        try:
-            audio_output = await texttospeech(read_info, language,autoplay)
-            # return state, state, refined_image_input, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state, waveform_visual, audio_output
-            return state, state, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state, audio_output
-
-        except Exception as e:
-            state = state + [(None, f"Error during TTS prediction: {str(e)}")]
-            print(f"Error during TTS prediction: {str(e)}")
-            # return state, state, refined_image_input, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state, None, None
-            return state, state, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state, audio_output
-
-    else:
-        try:
-            audio_output = await texttospeech(focus_info, language, autoplay)
-            # waveform_visual, audio_output = tts.predict(generated_caption, input_language, input_audio, input_mic, use_mic, agree)
-            # return state, state, image_input, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state, waveform_visual, audio_output
-            return state, state, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state, audio_output
-
-        except Exception as e:
-            state = state + [(None, f"Error during TTS prediction: {str(e)}")]
-            print(f"Error during TTS prediction: {str(e)}")
-            return state, state, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state, None, None
-
-def generate_prompt(focus_type, paragraph,length, sentiment, factuality, language):
-    
-    mapped_value = focus_map.get(focus_type, -1)
-
-    controls = {
-        'length': length,
-        'sentiment': sentiment,
-        'factuality': factuality,
-        'language': language
-    }
-
-    if mapped_value != -1:
-        prompt = prompt_list[mapped_value].format(
-            Wiki_caption=paragraph,
-            length=controls['length'],
-            sentiment=controls['sentiment'],
-            language=controls['language']
-        )
-    else:
-        prompt = "Invalid focus type."
-
-    if controls['factuality'] == "Imagination":
-        prompt += " Assuming that I am someone who has viewed a lot of art and has a lot of experience viewing art. Explain artistic features (composition, color, style, or use of light) and discuss the symbolism of the content and its influence on later artistic movements."
-
-    return prompt
-
-
-def encode_image(image_path):
-    with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode('utf-8')
-    
-def get_image_gpt(api_key, image_path,prompt,enable_wiki=None):
-    # Getting the base64 string
-    base64_image = encode_image(image_path)
-    
-    
-
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {api_key}"
-    }
-
-    prompt_text = prompt
-
-    payload = {
-        "model": "gpt-4o",
-        "messages": [
-            {
-                "role": "user",
-                "content": [
-                    {
-                        "type": "text",
-                        "text": prompt_text
-                    },
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:image/jpeg;base64,{base64_image}"
-                        }
-                    }
-                ]
-            }
-        ],
-        "max_tokens": 300
-    }
-
-    # Sending the request to the OpenAI API
-    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
-    result = response.json()
-    print(result)
-    content = result['choices'][0]['message']['content']
-    # Assume the model returns a valid JSON string in 'content'
-    try:
-        return content
-    except json.JSONDecodeError:
-        return {"error": "Failed to parse model output"}
-
-
-
-
-def get_sketch_prompt(mask: Image.Image):
-    """
-    Get the prompt for the sketcher.
-    TODO: This is a temporary solution. We should cluster the sketch and get the bounding box of each cluster.
-    """
-
-    mask = np.asarray(mask)[..., 0]
-
-    # Get the bounding box of the sketch
-    y, x = np.where(mask != 0)
-    x1, y1 = np.min(x), np.min(y)
-    x2, y2 = np.max(x), np.max(y)
-
-    prompt = {
-        'prompt_type': ['box'],
-        'input_boxes': [
-            [x1, y1, x2, y2]
-        ]
-    }
-
-    return prompt
-
-submit_traj=0
-
-async def inference_traject(origin_image,sketcher_image, enable_wiki, language, sentiment, factuality, length, image_embedding, state,
-                      original_size, input_size, text_refiner,focus_type,paragraph,openai_api_key,autoplay,trace_type):
-    image_input, mask = sketcher_image['image'], sketcher_image['mask']
-    
-    crop_save_path=""
-    
-    prompt = get_sketch_prompt(mask)
-    boxes = prompt['input_boxes']
-    boxes = boxes[0]
-    global submit_traj
-    submit_traj=1
-
-    controls = {'length': length,
-                'sentiment': sentiment,
-                'factuality': factuality,
-                'language': language}
-
-    model = build_caption_anything_with_models(
-        args,
-        api_key="",
-        captioner=shared_captioner,
-        sam_model=shared_sam_model,
-        ocr_reader=shared_ocr_reader,
-        text_refiner=text_refiner,
-        session_id=iface.app_id
-    )
-
-    model.setup(image_embedding, original_size, input_size, is_image_set=True)
-
-    enable_wiki = True if enable_wiki in ['True', 'TRUE', 'true', True, 'Yes', 'YES', 'yes'] else False
-    out = model.inference(image_input, prompt, controls, disable_gpt=True, enable_wiki=enable_wiki,verbose=True)[0]
-    
-    print(trace_type)
-    
-    if trace_type=="Trace+Seg":
-        input_mask = np.array(out['mask'].convert('P'))
-        image_input = mask_painter(np.array(image_input), input_mask, background_alpha=0 )
-        crop_save_path=out['crop_save_path']
-    
-    else:
-        image_input = Image.fromarray(np.array(origin_image))
-        draw = ImageDraw.Draw(image_input)
-        draw.rectangle(boxes, outline='red', width=2) 
-        cropped_image = origin_image.crop(boxes)
-        cropped_image.save('temp.png')
-        crop_save_path='temp.png'
-    
-    print("crop_svae_path",out['crop_save_path'])
-        
-    # Update components and states
-    state.append((f'Box: {boxes}', None))
-    
-    # fake_click_index = (int((boxes[0][0] + boxes[0][2]) / 2), int((boxes[0][1] + boxes[0][3]) / 2))
-    # image_input = create_bubble_frame(image_input, "", fake_click_index, input_mask)
-
-    prompt=generate_prompt(focus_type, paragraph, length, sentiment, factuality, language)
-    width, height = sketcher_image['image'].size
-    sketcher_image['mask'] = np.zeros((height, width, 4), dtype=np.uint8)
-    sketcher_image['mask'][..., -1] = 255
-    sketcher_image['image']=image_input
-    
-    
-    if not args.disable_gpt and text_refiner:
-        focus_info=get_image_gpt(openai_api_key,crop_save_path,prompt)
-        if focus_info.startswith('"') and focus_info.endswith('"'):
-            focus_info=focus_info[1:-1]
-        focus_info=focus_info.replace('#', '')
-        state = state + [(None, f"{focus_info}")]
-        print("new_cap",focus_info)
-        read_info = re.sub(r'[#[\]!*]','',focus_info)
-        read_info = emoji.replace_emoji(read_info,replace="")    
-        print("read info",read_info)
-
-        # refined_image_input = create_bubble_frame(np.array(origin_image_input), focus_info, click_index, input_mask,
-        #                                           input_points=input_points, input_labels=input_labels)
-        try:
-            audio_output = await texttospeech(read_info, language,autoplay)
-            # return state, state, refined_image_input, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state, waveform_visual, audio_output
-            return state, state,image_input,audio_output
-
-
-        except Exception as e:
-            state = state + [(None, f"Error during TTS prediction: {str(e)}")]
-            print(f"Error during TTS prediction: {str(e)}")
-            # return state, state, refined_image_input, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state, None, None
-            return state, state, image_input,audio_output
-
-
-    else:
-        try:
-            audio_output = await texttospeech(focus_info, language, autoplay)
-            # waveform_visual, audio_output = tts.predict(generated_caption, input_language, input_audio, input_mic, use_mic, agree)
-            # return state, state, image_input, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state, waveform_visual, audio_output
-            return state, state, image_input,audio_output
-
-
-        except Exception as e:
-            state = state + [(None, f"Error during TTS prediction: {str(e)}")]
-            print(f"Error during TTS prediction: {str(e)}")
-            return state, state, image_input,audio_output
-
-
-def clear_chat_memory(visual_chatgpt, keep_global=False):
-    if visual_chatgpt is not None:
-        visual_chatgpt.memory.clear()
-        visual_chatgpt.point_prompt = ""
-        if keep_global:
-            visual_chatgpt.agent.memory.buffer = visual_chatgpt.global_prompt
-        else:
-            visual_chatgpt.current_image = None
-            visual_chatgpt.global_prompt = ""
-
-
-def export_chat_log(chat_state):
-    try:
-        if not chat_state:
-            return None
-        chat_log = "\n".join(f"{entry[0]}\n{entry[1]}" for entry in chat_state if entry)
-        print("export log...")
-        print("chat_log", chat_log)
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".txt") as temp_file:
-            temp_file.write(chat_log.encode('utf-8'))
-            temp_file_path = temp_file.name
-            print(temp_file_path)
-        return temp_file_path
-    except Exception as e:
-        print(f"An error occurred while exporting the chat log: {e}")
-        return None
-
-
-async def cap_everything(paragraph, visual_chatgpt,language,autoplay):
-
-    # state = state + [(None, f"Caption Everything: {paragraph}")]  
-    Human_prompt = f'\nThe description of the image with path {visual_chatgpt.current_image} is:\n{paragraph}\nThis information helps you to understand this image, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\". \n'
-    AI_prompt = "Received."
-    visual_chatgpt.global_prompt = Human_prompt + 'AI: ' + AI_prompt
-    visual_chatgpt.agent.memory.buffer = visual_chatgpt.agent.memory.buffer + visual_chatgpt.global_prompt 
-    # waveform_visual, audio_output=tts.predict(paragraph, input_language, input_audio, input_mic, use_mic, agree)
-    audio_output=await texttospeech(paragraph,language,autoplay)
-    return paragraph,audio_output
-
-def cap_everything_withoutsound(image_input, visual_chatgpt, text_refiner,paragraph):
-    
-    model = build_caption_anything_with_models(
-        args,
-        api_key="",
-        captioner=shared_captioner,
-        sam_model=shared_sam_model,
-        ocr_reader=shared_ocr_reader,
-        text_refiner=text_refiner,
-        session_id=iface.app_id
-    )
-    paragraph = model.inference_cap_everything(image_input, verbose=True)
-    # state = state + [(None, f"Caption Everything: {paragraph}")]  
-    Human_prompt = f'\nThe description of the image with path {visual_chatgpt.current_image} is:\n{paragraph}\nThis information helps you to understand this image, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\". \n'
-    AI_prompt = "Received."
-    visual_chatgpt.global_prompt = Human_prompt + 'AI: ' + AI_prompt
-    visual_chatgpt.agent.memory.buffer = visual_chatgpt.agent.memory.buffer + visual_chatgpt.global_prompt 
-    return paragraph
-
-
-    
-def get_style():
-    current_version = version.parse(gr.__version__)
-    if current_version <= version.parse('3.24.1'):
-        style = '''
-        #image_sketcher{min-height:500px}
-        #image_sketcher [data-testid="image"], #image_sketcher [data-testid="image"] > div{min-height: 500px}
-        #image_upload{min-height:500px}
-        #image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 500px}
-        .custom-language {
-            width: 20%; 
-        }
-
-        .custom-autoplay {
-            width: 40%; 
-        }
-
-        .custom-output {
-            width: 30%; 
-        }
-
-        '''
-    elif current_version <= version.parse('3.27'):
-        style = '''
-        #image_sketcher{min-height:500px}
-        #image_upload{min-height:500px}
-        .custom-language {
-            width: 20%; 
-        }
-
-        .custom-autoplay {
-            width: 40%; 
-        }
-
-        .custom-output {
-            width: 30%; 
-        }
-        '''
-    else:
-        style = None
-
-    return style
-
-# def handle_like_dislike(like_data, like_state, dislike_state):
-#     if like_data.liked:
-#         if like_data.index not in like_state:
-#             like_state.append(like_data.index)
-#             message = f"Liked: {like_data.value} at index {like_data.index}"
-#         else:
-#             message = "You already liked this item"
-#     else:
-#         if like_data.index not in dislike_state:
-#             dislike_state.append(like_data.index)
-#             message = f"Disliked: {like_data.value} at index {like_data.index}"
-#         else:
-#             message = "You already disliked this item"
-    
-#     return like_state, dislike_state
-
-async def texttospeech(text,language,autoplay):
-    voice=filtered_language_dict[language]
-    communicate = edge_tts.Communicate(text, voice)
-    file_path="output.wav"
-    await communicate.save(file_path)
-    with open(file_path, "rb") as audio_file:
-            audio_bytes = BytesIO(audio_file.read())
-    audio = base64.b64encode(audio_bytes.read()).decode("utf-8")
-    print("tts....")
-    audio_style = 'style="width:210px;"'  
-    if autoplay:
-        audio_player = f'<audio src="data:audio/wav;base64,{audio}" controls autoplay {audio_style}></audio>'
-    else:
-        audio_player=None
-        # audio_player = f'<audio src="data:audio/wav;base64,{audio}" controls {audio_style}></audio>'
-    return audio_player
-
-
-def create_ui():
-    title = """<p><h1 align="center">EyeSee Anything in Art</h1></p>
-    """
-    description = """<p>Gradio demo for EyeSee Anything in Art, image to dense captioning generation with various language styles. To use it, simply upload your image, or click one of the examples to load them. """
-
-    examples = [
-        ["test_images/ambass.jpg"],
-        ["test_images/pearl.jpg"],
-        ["test_images/Picture0.png"],
-        ["test_images/Picture1.png"],
-        ["test_images/Picture2.png"],
-        ["test_images/Picture3.png"],
-        ["test_images/Picture4.png"],
-        ["test_images/Picture5.png"],
-        
-    ]
-
-    with gr.Blocks(
-            css=get_style(),
-            theme=gr.themes.Base()
-    ) as iface:
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--load', type=str, default="VisualQuestionAnswering_cuda:0")
+    parser.add_argument('--port', type=int, default=1015)
+    
+    args = parser.parse_args()
+    load_dict = {e.split('_')[0].strip(): e.split('_')[1].strip() for e in args.load.split(',')}
+    tools = build_chatbot_tools(load_dict)
+    bot = ConversationBot(tools)
+    with gr.Blocks(css="#chatbot .overflow-y-auto{height:500px}") as demo:
+        with gr.Row():
+            chatbot = gr.Chatbot(elem_id="chatbot", label="CATchat").style(height=1000,scale=0.5)
+            auxwindow = gr.Chatbot(elem_id="chatbot", label="Aux Window").style(height=1000,scale=0.5)
         state = gr.State([])
-        out_state = gr.State(None)
-        click_state = gr.State([[], [], []])
-        origin_image = gr.State(None)
-        image_embedding = gr.State(None)
-        text_refiner = gr.State(None)
-        visual_chatgpt = gr.State(None)
-        original_size = gr.State(None)
-        input_size = gr.State(None)
-        paragraph = gr.State("")
         aux_state = gr.State([])
-        click_index_state = gr.State((0, 0))
-        input_mask_state = gr.State(np.zeros((1, 1)))
-        input_points_state = gr.State([])
-        input_labels_state = gr.State([])
-        new_crop_save_path = gr.State(None)
-        image_input_nobackground = gr.State(None)
-
-        gr.Markdown(title)
-        gr.Markdown(description)
-        with gr.Row(align="right", visible=False, elem_id="top_row") as top_row:  
-            language = gr.Dropdown(
-            ['English', 'Chinese', 'French', "Spanish", "Arabic", "Portuguese", "Cantonese"],
-            value="English", label="Language", interactive=True, scale=0.2, elem_classes="custom-language"
-        )
-            auto_play = gr.Checkbox(
-            label="Check to autoplay audio", value=False, scale=0.4, elem_classes="custom-autoplay"
-        )
-            output_audio = gr.HTML(
-                label="Synthesised Audio", scale=0.3, elem_classes="custom-output"
-            )
-
-
-        # with gr.Row(align="right",visible=False) as language_select:
-        #     language = gr.Dropdown(
-        #         ['English', 'Chinese', 'French', "Spanish", "Arabic", "Portuguese", "Cantonese"],
-        #         value="English", label="Language", interactive=True)
-    
-        # with gr.Row(align="right",visible=False) as autoplay:
-        #     auto_play = gr.Checkbox(label="Check to autoplay audio", value=False,scale=0.4)
-        #     output_audio = gr.HTML(label="Synthesised Audio",scale=0.6)
-
-        with gr.Row():                        
-            
-            with gr.Column(scale=1.0):
-                with gr.Column(visible=False) as modules_not_need_gpt:
-                    with gr.Tab("Base(GPT Power)",visible=False) as base_tab:
-                        image_input_base = gr.Image(type="pil", interactive=True, elem_id="image_upload")
-                        example_image = gr.Image(type="pil", interactive=False, visible=False)
-                        with gr.Row():
-                            name_label_base = gr.Button(value="Name: ")
-                            artist_label_base = gr.Button(value="Artist: ")
-                            year_label_base = gr.Button(value="Year: ")
-                            material_label_base = gr.Button(value="Material: ")   
-
-                    with gr.Tab("Click") as click_tab:
-                        image_input = gr.Image(type="pil", interactive=True, elem_id="image_upload")
-                        example_image = gr.Image(type="pil", interactive=False, visible=False)
-                        with gr.Row():
-                            name_label = gr.Button(value="Name: ")
-                            artist_label = gr.Button(value="Artist: ")
-                            year_label = gr.Button(value="Year: ")
-                            material_label = gr.Button(value="Material: ")
-                        with gr.Row(scale=1.0):
-                            with gr.Row(scale=0.8):
-                                focus_type = gr.Radio(
-                                        choices=["CFV-D", "CFV-DA", "CFV-DAI","PFV-DDA"],
-                                        value="CFV-D",
-                                        label="Information Type",
-                                        interactive=True)
-                            with gr.Row(scale=0.2):
-                                submit_button_click=gr.Button(value="Submit", interactive=True,variant='primary',size="sm")
-                        with gr.Row(scale=1.0):
-                            with gr.Row(scale=0.4):
-                                point_prompt = gr.Radio(
-                                    choices=["Positive", "Negative"],
-                                    value="Positive",
-                                    label="Point Prompt",
-                                    interactive=True)
-                                click_mode = gr.Radio(
-                                    choices=["Continuous", "Single"],
-                                    value="Continuous",
-                                    label="Clicking Mode",
-                                    interactive=True)
-                            with gr.Row(scale=0.4):
-                                clear_button_click = gr.Button(value="Clear Clicks", interactive=True)
-                                clear_button_image = gr.Button(value="Clear Image", interactive=True)
-                                
-                    with gr.Tab("Trajectory (beta)") as traj_tab:
-                        sketcher_input = ImageSketcher(type="pil", interactive=True, brush_radius=10, 
-                                                       elem_id="image_sketcher")
-                        example_image = gr.Image(type="pil", interactive=False, visible=False)
-                        with gr.Row():                   
-                            submit_button_sketcher = gr.Button(value="Submit", interactive=True)
-                            clear_button_sketcher = gr.Button(value="Clear Sketch", interactive=True)
-                        with gr.Row(scale=1.0):
-                            with gr.Row(scale=0.8):
-                                focus_type_sketch = gr.Radio(
-                                        choices=["CFV-D", "CFV-DA", "CFV-DAI","PFV-DDA"],
-                                        value="CFV-D",
-                                        label="Information Type",
-                                        interactive=True)
-                                Input_sketch = gr.Radio(
-                                        choices=["Trace+Seg", "Trace"],
-                                        value="Trace+Seg",
-                                        label="Trace Type",
-                                        interactive=True)
-
-                    with gr.Column(visible=False) as modules_need_gpt1:
-                        with gr.Row(scale=1.0):
-                            sentiment = gr.Radio(
-                                choices=["Positive", "Natural", "Negative"],
-                                value="Natural",
-                                label="Sentiment",
-                                interactive=True,
-                            )
-                        with gr.Row(scale=1.0):
-                            factuality = gr.Radio(
-                                choices=["Factual", "Imagination"],
-                                value="Factual",
-                                label="Factuality",
-                                interactive=True,
-                            )
-                            length = gr.Slider(
-                                minimum=10,
-                                maximum=80,
-                                value=10,
-                                step=1,
-                                interactive=True,
-                                label="Generated Caption Length",
-                            )
-                            # 是否启用wiki内容整合到caption中
-                            enable_wiki = gr.Radio(
-                                choices=["Yes", "No"],
-                                value="No",
-                                label="Expert",
-                                interactive=True)
-                with gr.Column(visible=True) as modules_not_need_gpt3:
-                    gr.Examples(
-                examples=examples,
-                inputs=[example_image],
-            )
-
-
-                
-                
-
-            with gr.Column(scale=0.5):                    
-                with gr.Column(visible=True) as module_key_input:
-                    openai_api_key = gr.Textbox(
-                        placeholder="Input openAI API key",
-                        show_label=False,
-                        label="OpenAI API Key",
-                        lines=1,
-                        type="password")
-                    with gr.Row(scale=0.5):
-                        enable_chatGPT_button = gr.Button(value="Run with ChatGPT", interactive=True, variant='primary')
-                        disable_chatGPT_button = gr.Button(value="Run without ChatGPT (Faster)", interactive=True,
-                                                        variant='primary')
-                with gr.Column(visible=False) as module_notification_box:
-                    notification_box = gr.Textbox(lines=1, label="Notification", max_lines=5, show_label=False)
-                
-                with gr.Column() as modules_need_gpt0:
-                    with gr.Column(visible=False,scale=1.0) as modules_need_gpt2: 
-                        paragraph_output = gr.Textbox(lines=16, label="Describe Everything", max_lines=16)
-                        cap_everything_button = gr.Button(value="Caption Everything in a Paragraph", interactive=True)
-                
-                with gr.Column(visible=False) as modules_not_need_gpt2: 
-                    with gr.Blocks():
-                        chatbot = gr.Chatbot(label="Chatbox", elem_classes="chatbot",likeable=True).style(height=600, scale=0.5)
-                        with gr.Column(visible=False) as modules_need_gpt3:
-                            chat_input = gr.Textbox(show_label=False, placeholder="Enter text and press Enter").style(
-                                container=False)
-                            with gr.Row():
-                                clear_button_text = gr.Button(value="Clear Text", interactive=True)
-                                submit_button_text = gr.Button(value="Send", interactive=True, variant="primary")
-                                upvote_btn = gr.Button(value="👍 Upvote", interactive=True) 
-                                downvote_btn = gr.Button(value="👎 Downvote", interactive=True) 
-                                
-                            with gr.Row():
-                                export_button = gr.Button(value="Export Chat Log", interactive=True, variant="primary")
-                            with gr.Row():
-                                chat_log_file = gr.File(label="Download Chat Log")
-            
-                # TTS interface hidden initially
-            with gr.Column(visible=False) as tts_interface:
-                input_text = gr.Textbox(label="Text Prompt", value="Hello, World !, here is an example of light voice cloning. Try to upload your best audio samples quality")
-                input_language = gr.Dropdown(label="Language", choices=["en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru", "nl", "cs", "ar", "zh-cn"], value="en")
-                input_audio = gr.Audio(label="Reference Audio", type="filepath", value="examples/female.wav")
-                input_mic = gr.Audio(source="microphone", type="filepath", label="Use Microphone for Reference")
-                use_mic = gr.Checkbox(label="Check to use Microphone as Reference", value=False)
-                agree = gr.Checkbox(label="Agree", value=True)
-                output_waveform = gr.Video(label="Waveform Visual")
-                # output_audio = gr.HTML(label="Synthesised Audio")
-
-                with gr.Row():
-                    submit_tts = gr.Button(value="Submit", interactive=True)
-                    clear_tts = gr.Button(value="Clear", interactive=True)
-        ###############################################################################
-        ############# this part is for text to image #############
-        ###############################################################################
-        
-        with gr.Row(variant="panel") as text2image_model:
-
-            with gr.Column():
-            
-                with gr.Row():
-                    prompt = gr.Text(
-                        label="Prompt",
-                        show_label=False,
-                        max_lines=1,
-                        placeholder="Enter your prompt",
-                        container=False,
-                    )
-                    run_button = gr.Button("Run", scale=0)
-                
-                with gr.Accordion("Advanced options", open=True):
-                    num_images = gr.Slider(
-                        label="Number of Images",
-                        minimum=1,
-                        maximum=4,
-                        step=1,
-                        value=4,
-                    )
-                    with gr.Row():
-                        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
-                        negative_prompt = gr.Text(
-                            label="Negative prompt",
-                            max_lines=5,
-                            lines=4,
-                            placeholder="Enter a negative prompt",
-                            value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
-                            visible=True,
-                        )
-                    seed = gr.Slider(
-                        label="Seed",
-                        minimum=0,
-                        maximum=MAX_SEED,
-                        step=1,
-                        value=0,
-                    )
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    with gr.Row(visible=True):
-                        width = gr.Slider(
-                            label="Width",
-                            minimum=512,
-                            maximum=MAX_IMAGE_SIZE,
-                            step=64,
-                            value=1024,
-                        )
-                        height = gr.Slider(
-                            label="Height",
-                            minimum=512,
-                            maximum=MAX_IMAGE_SIZE,
-                            step=64,
-                            value=1024,
-                        )
-                    with gr.Row():
-                        guidance_scale = gr.Slider(
-                            label="Guidance Scale",
-                            minimum=0.1,
-                            maximum=6,
-                            step=0.1,
-                            value=3.0,
-                        )
-                        num_inference_steps = gr.Slider(
-                            label="Number of inference steps",
-                            minimum=1,
-                            maximum=15,
-                            step=1,
-                            value=8,
-                        )
-            with gr.Column():
-                result = gr.Gallery(
-                    label="Result", 
-                    columns=[2], 
-                    rows=[2], 
-                    show_label=False, 
-                    allow_preview=True, 
-                    object_fit="contain", 
-                    height="auto", 
-                    preview=True, 
-                    show_share_button=True, 
-                    show_download_button=True
-                )
-
-
-
-        # gr.Examples(
-        #     examples=examples,
-        #     inputs=prompt,
-        #     cache_examples=False
-        # )
-    
-        use_negative_prompt.change(
-            fn=lambda x: gr.update(visible=x),
-            inputs=use_negative_prompt,
-            outputs=negative_prompt,
-            api_name=False,
-        )
-    
-        # gr.on(
-        #     triggers=[
-        #         prompt.submit,
-        #         negative_prompt.submit,
-        #         run_button.click,
-        #     ],
-        #     fn=generate,
-        #     inputs=[
-        #         prompt,
-        #         negative_prompt,
-        #         use_negative_prompt,
-        #         seed,
-        #         width,
-        #         height,
-        #         guidance_scale,
-        #         num_inference_steps,
-        #         randomize_seed,
-        #         num_images
-        #     ],
-        #     outputs=[result, seed],
-        #     api_name="run",
-        # )
-        run_button.click(
-            fn=generate,
-            inputs=[
-                prompt,
-                negative_prompt,
-                use_negative_prompt,
-                seed,
-                width,
-                height,
-                guidance_scale,
-                num_inference_steps,
-                randomize_seed,
-                num_images
-            ],
-            outputs=[result, seed]
-            )
-        
-        ###############################################################################
-        ############# above part is for text to image #############
-        ###############################################################################
-
-        
-        ###############################################################################
-        # this part is for 3d generate.
-        ###############################################################################
-        
-        with gr.Row(variant="panel",visible=False) as d3_model:
-            with gr.Column():
-                with gr.Row():
-                    input_image = gr.Image(
-                        label="Input Image",
-                        image_mode="RGBA",
-                        sources="upload",
-                        #width=256,
-                        #height=256,
-                        type="pil",
-                        elem_id="content_image",
-                    )
-                    processed_image = gr.Image(
-                        label="Processed Image", 
-                        image_mode="RGBA", 
-                        #width=256,
-                        #height=256,
-                        type="pil", 
-                        interactive=False
-                    )
-                with gr.Row():
-                    with gr.Group():
-                        do_remove_background = gr.Checkbox(
-                            label="Remove Background", value=True
-                        )
-                        sample_seed = gr.Number(value=42, label="Seed Value", precision=0)
-    
-                        sample_steps = gr.Slider(
-                            label="Sample Steps",
-                            minimum=30,
-                            maximum=75,
-                            value=75,
-                            step=5
-                        )
-    
-                with gr.Row():
-                    submit = gr.Button("Generate", elem_id="generate", variant="primary")
-    
-                with gr.Row(variant="panel"):
-                    gr.Examples(
-                        examples=[
-                            os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
-                        ],
-                        inputs=[input_image],
-                        label="Examples",
-                        cache_examples=False,
-                        examples_per_page=16
-                    )
-
-            with gr.Column():
-    
-                with gr.Row():
-    
-                    with gr.Column():
-                        mv_show_images = gr.Image(
-                            label="Generated Multi-views",
-                            type="pil",
-                            width=379,
-                            interactive=False
-                        )
-    
-                    # with gr.Column():
-                    #     output_video = gr.Video(
-                    #         label="video", format="mp4",
-                    #         width=379,
-                    #         autoplay=True,
-                    #         interactive=False
-                    #     )
-    
-                with gr.Row():
-                    with gr.Tab("OBJ"):
-                        output_model_obj = gr.Model3D(
-                            label="Output Model (OBJ Format)",
-                            interactive=False,
-                        )
-                        gr.Markdown("Note: Downloaded .obj model will be flipped. Export .glb instead or manually flip it before usage.")
-                    with gr.Tab("GLB"):
-                        output_model_glb = gr.Model3D(
-                            label="Output Model (GLB Format)",
-                            interactive=False,
-                        )
-                        gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.")
-    
-                
-        
-        
-        mv_images = gr.State()
-        
-        # chatbot.like(handle_like_dislike, inputs=[like_state, dislike_state], outputs=[like_state, dislike_state])
-
-        submit.click(fn=check_input_image, inputs=[new_crop_save_path], outputs=[processed_image]).success(
-            fn=generate_mvs,
-            inputs=[processed_image, sample_steps, sample_seed],
-            outputs=[mv_images, mv_show_images]
-            
-        ).success(
-            fn=make3d,
-            inputs=[mv_images],
-            outputs=[output_model_obj, output_model_glb]
-        )
-        
-        ###############################################################################
-        # above part is for 3d generate.
-        ###############################################################################
-       
-        
-        def clear_tts_fields():
-            return [gr.update(value=""), gr.update(value=""), None, None, gr.update(value=False), gr.update(value=True), None, None]
-
-        # submit_tts.click(
-        #     tts.predict,
-        #     inputs=[input_text, input_language, input_audio, input_mic, use_mic, agree],
-        #     outputs=[output_waveform, output_audio],
-        #     queue=True
-        # )
-
-        clear_tts.click(
-            clear_tts_fields,
-            inputs=None,
-            outputs=[input_text, input_language, input_audio, input_mic, use_mic, agree, output_waveform, output_audio],
-            queue=False
-        )
-        
-
-
-        
-        clear_button_sketcher.click(
-            lambda x: (x),
-            [origin_image],
-            [sketcher_input],
-            queue=False,
-            show_progress=False
-        )
-
-    
-
-
-        
-        openai_api_key.submit(init_openai_api_key, inputs=[openai_api_key],
-                              outputs=[modules_need_gpt0, modules_need_gpt1, modules_need_gpt2, modules_need_gpt3, modules_not_need_gpt,
-                                       modules_not_need_gpt2, tts_interface,module_key_input ,module_notification_box, text_refiner, visual_chatgpt, notification_box,d3_model,top_row])
-        enable_chatGPT_button.click(init_openai_api_key, inputs=[openai_api_key],
-                                    outputs=[modules_need_gpt0, modules_need_gpt1, modules_need_gpt2, modules_need_gpt3,
-                                             modules_not_need_gpt,
-                                             modules_not_need_gpt2, tts_interface,module_key_input,module_notification_box, text_refiner, visual_chatgpt, notification_box,d3_model,top_row])
-        disable_chatGPT_button.click(init_wo_openai_api_key,
-                                     outputs=[modules_need_gpt0, modules_need_gpt1, modules_need_gpt2, modules_need_gpt3,
-                                              modules_not_need_gpt,
-                                              modules_not_need_gpt2, tts_interface,module_key_input, module_notification_box, text_refiner, visual_chatgpt, notification_box,d3_model,top_row])
-        
-        enable_chatGPT_button.click(
-            lambda: (None, [], [], [[], [], []], "", "", ""),
-            [],
-            [image_input, chatbot, state, click_state, paragraph_output, origin_image],
-            queue=False,
-            show_progress=False
-        )
-        openai_api_key.submit(
-            lambda: (None, [], [], [[], [], []], "", "", ""),
-            [],
-            [image_input, chatbot, state, click_state, paragraph_output, origin_image],
-            queue=False,
-            show_progress=False
-        )
-
-        cap_everything_button.click(cap_everything, [paragraph, visual_chatgpt, language,auto_play], 
-                                    [paragraph_output,output_audio])
-        
-        clear_button_click.click(
-            lambda x: ([[], [], []], x),
-            [origin_image],
-            [click_state, image_input],
-            queue=False,
-            show_progress=False
-        )
-        clear_button_click.click(functools.partial(clear_chat_memory, keep_global=True), inputs=[visual_chatgpt])
-        clear_button_image.click(
-            lambda: (None, [], [], [[], [], []], "", "", ""),
-            [],
-            [image_input, chatbot, state, click_state, paragraph_output, origin_image],
-            queue=False,
-            show_progress=False
-        )
-        clear_button_image.click(clear_chat_memory, inputs=[visual_chatgpt])
-        clear_button_text.click(
-            lambda: ([], [], [[], [], [], []]),
-            [],
-            [chatbot, state, click_state],
-            queue=False,
-            show_progress=False
-        )
-        clear_button_text.click(clear_chat_memory, inputs=[visual_chatgpt])
-        
-        image_input.clear(
-            lambda: (None, [], [], [[], [], []], "", "", ""),
-            [],
-            [image_input, chatbot, state, click_state, paragraph_output, origin_image],
-            queue=False,
-            show_progress=False
-        )
-
-        image_input.clear(clear_chat_memory, inputs=[visual_chatgpt])
-
-
-        
-
-        image_input_base.upload(upload_callback, [image_input_base, state, visual_chatgpt,openai_api_key],
-                           [chatbot, state, origin_image, click_state, image_input, image_input_base, sketcher_input,
-                            image_embedding, original_size, input_size,name_label,artist_label,year_label,material_label,name_label_base, artist_label_base, year_label_base, material_label_base,paragraph])
-        
-        image_input.upload(upload_callback, [image_input, state, visual_chatgpt, openai_api_key],
-                           [chatbot, state, origin_image, click_state, image_input, image_input_base, sketcher_input,
-                            image_embedding, original_size, input_size,name_label,artist_label,year_label,material_label,name_label_base, artist_label_base, year_label_base, material_label_base,paragraph])
-        sketcher_input.upload(upload_callback, [sketcher_input, state, visual_chatgpt, openai_api_key],
-                              [chatbot, state, origin_image, click_state, image_input, image_input_base, sketcher_input,
-                               image_embedding, original_size, input_size,name_label,artist_label,year_label,material_label,name_label_base, artist_label_base, year_label_base, material_label_base,paragraph])
-        chat_input.submit(chat_input_callback, [visual_chatgpt, chat_input, click_state, state, aux_state,language,auto_play],
-                          [chatbot, state, aux_state,output_audio])
-        chat_input.submit(lambda: "", None, chat_input)
-        submit_button_text.click(chat_input_callback, [visual_chatgpt, chat_input, click_state, state, aux_state,language,auto_play],
-                          [chatbot, state, aux_state,output_audio])
-        submit_button_text.click(lambda: "", None, chat_input)
-        example_image.change(upload_callback, [example_image, state, visual_chatgpt, openai_api_key],
-                             [chatbot, state, origin_image, click_state, image_input, image_input_base, sketcher_input,
-                              image_embedding, original_size, input_size,name_label,artist_label,year_label,material_label,name_label_base, artist_label_base, year_label_base, material_label_base,paragraph])
-
-        example_image.change(clear_chat_memory, inputs=[visual_chatgpt])
-
-        def on_click_tab_selected():
-            if gpt_state ==1:
-                print(gpt_state)
-                print("using gpt")
-                return [gr.update(visible=True)]*2+[gr.update(visible=False)]*2
-            else: 
-                print("no gpt")
-                print("gpt_state",gpt_state)
-                return [gr.update(visible=False)]+[gr.update(visible=True)]+[gr.update(visible=False)]*2
-        
-        def on_base_selected():
-            if gpt_state ==1:
-                print(gpt_state)
-                print("using gpt")
-                return [gr.update(visible=True)]*2+[gr.update(visible=False)]*2
-            else: 
-                print("no gpt")
-                return [gr.update(visible=False)]*4
-        
-
-        traj_tab.select(on_click_tab_selected, outputs=[modules_need_gpt1,modules_not_need_gpt2,modules_need_gpt0,modules_need_gpt2])
-        click_tab.select(on_click_tab_selected, outputs=[modules_need_gpt1,modules_not_need_gpt2,modules_need_gpt0,modules_need_gpt2])
-        base_tab.select(on_base_selected, outputs=[modules_need_gpt0,modules_need_gpt2,modules_not_need_gpt2,modules_need_gpt1])
-
-  
-  
-
-        image_input.select(
-            inference_click,
-            inputs=[
-                origin_image, point_prompt, click_mode, enable_wiki, language, sentiment, factuality, length,
-                image_embedding, state, click_state, original_size, input_size, text_refiner, visual_chatgpt,
-                out_state, click_index_state, input_mask_state, input_points_state, input_labels_state,
-            ],
-            outputs=[chatbot, state, click_state, image_input, input_image, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state,new_crop_save_path,image_input_nobackground],
-            show_progress=False, queue=True
-        )
-
-
-        submit_button_click.click(
-            submit_caption,
-            inputs=[
-        state, text_refiner,length, sentiment, factuality, language, 
-        out_state, click_index_state, input_mask_state, input_points_state, input_labels_state,
-        auto_play,paragraph,focus_type,openai_api_key,new_crop_save_path
-    ],
-            outputs=[
-                chatbot, state, click_index_state, input_mask_state, input_points_state, input_labels_state, out_state,
-                output_audio 
-            ],
-            show_progress=True,
-            queue=True
-        )
-    
-
-        submit_button_sketcher.click(
-            inference_traject,
-            inputs=[
-                origin_image,sketcher_input, enable_wiki, language, sentiment, factuality, length, image_embedding, state,
-                original_size, input_size, text_refiner,focus_type_sketch,paragraph,openai_api_key,auto_play,Input_sketch
-            ],
-            outputs=[chatbot, state, sketcher_input,output_audio],
-            show_progress=False, queue=True
-        )
-        
-        export_button.click(
-    export_chat_log,
-    inputs=[state],
-    outputs=[chat_log_file],
-    queue=True
-)
-
-
-
-
-
-        return iface
-
-
-if __name__ == '__main__':
-    iface = create_ui()
-    iface.queue(concurrency_count=5, api_open=False, max_size=10)
-    iface.launch(server_name="0.0.0.0", enable_queue=True)
+        with gr.Row():
+            with gr.Column(scale=0.7):
+                txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter, or upload an image").style(
+                    container=False)
+            with gr.Column(scale=0.15, min_width=0):
+                clear = gr.Button("Clear")
+            with gr.Column(scale=0.15, min_width=0):
+                btn = gr.UploadButton("Upload", file_types=["image"])
+
+        txt.submit(bot.run_text, [txt, state, aux_state], [chatbot, state, aux_state, auxwindow])
+        txt.submit(lambda: "", None, txt)
+        btn.upload(bot.run_image, [btn, state, txt, aux_state], [chatbot, state, txt, aux_state, auxwindow])
+        clear.click(bot.memory.clear)
+        clear.click(lambda: [], None, chatbot)
+        clear.click(lambda: [], None, auxwindow)
+        clear.click(lambda: [], None, state)
+        clear.click(lambda: [], None, aux_state)
+        demo.launch(server_name="0.0.0.0", server_port=args.port, share=True)