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from transformers import AutoModel, AutoTokenizer
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from copy import deepcopy
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import gradio as gr
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import mdtex2html
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from model.openlamm import LAMMPEFTModel
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import torch
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import json
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args = {
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'model': 'openllama_peft',
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'imagebind_ckpt_path': '../model_zoo/imagebind_ckpt',
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'vicuna_ckpt_path': './pretrained_ckpt/llm_7b_v0',
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'delta_ckpt_path': './pretrained_ckpt/llm7b_lora32_lamm186k/pytorch_model.pt',
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'stage': 2,
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'max_tgt_len': 128,
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'lora_r': 32,
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'lora_alpha': 32,
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'lora_dropout': 0.1,
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'lora_target_modules': ['q_proj', 'k_proj', 'v_proj', 'o_proj'],
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'vision_type': 'image',
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'vision_feature_type': 'local',
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'num_vision_token': 256,
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'encoder_pretrain': 'clip',
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'system_header': True,
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}
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model = LAMMPEFTModel(**args)
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delta_ckpt = torch.load(args['delta_ckpt_path'], map_location=torch.device('cpu'))
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model.load_state_dict(delta_ckpt, strict=False)
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model = model.eval().half().cuda()
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print(f'[!] init the 13b model over ...')
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"""Override Chatbot.postprocess"""
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def postprocess(self, y):
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if y is None:
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return []
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for i, (message, response) in enumerate(y):
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y[i] = (
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None if message is None else mdtex2html.convert((message)),
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None if response is None else mdtex2html.convert(response),
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)
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return y
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gr.Chatbot.postprocess = postprocess
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def parse_text(text):
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"""copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
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lines = text.split("\n")
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lines = [line for line in lines if line != ""]
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count = 0
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for i, line in enumerate(lines):
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if "```" in line:
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count += 1
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items = line.split('`')
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if count % 2 == 1:
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lines[i] = f'<pre><code class="language-{items[-1]}">'
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else:
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lines[i] = f'<br></code></pre>'
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else:
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if i > 0:
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if count % 2 == 1:
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line = line.replace("`", "\`")
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line = line.replace("<", "<")
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line = line.replace(">", ">")
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line = line.replace(" ", " ")
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line = line.replace("*", "*")
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line = line.replace("_", "_")
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line = line.replace("-", "-")
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line = line.replace(".", ".")
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line = line.replace("!", "!")
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line = line.replace("(", "(")
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line = line.replace(")", ")")
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line = line.replace("$", "$")
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lines[i] = "<br>"+line
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text = "".join(lines)
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if text.endswith("##"):
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text = text[:-2]
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return text
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def re_predict(
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input,
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image_path,
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chatbot,
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max_length,
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top_p,
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temperature,
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history,
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modality_cache,
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):
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q, a = history.pop()
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chatbot.pop()
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return predict(q, image_path, chatbot, max_length, top_p, temperature, history, modality_cache)
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def predict(
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input,
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image_path,
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chatbot,
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max_length,
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top_p,
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temperature,
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history,
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modality_cache,
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):
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if image_path is None:
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return [(input, "There is no input data provided! Please upload your data and start the conversation.")]
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else:
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print(f'[!] image path: {image_path}\n')
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prompt_text = ''
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for idx, (q, a) in enumerate(history):
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if idx == 0:
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prompt_text += f'{q}\n### Assistant: {a}\n###'
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else:
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prompt_text += f' Human: {q}\n### Assistant: {a}\n###'
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if len(history) == 0:
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prompt_text += f'{input}'
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else:
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prompt_text += f' Human: {input}'
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response = model.generate({
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'prompt': [prompt_text] if not isinstance(prompt_text, list) else prompt_text,
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'image_paths': [image_path] if image_path else [],
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'top_p': top_p,
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'temperature': temperature,
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'max_tgt_len': max_length,
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'modality_embeds': modality_cache
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})
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if isinstance(response, list):
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response = response[0]
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chatbot.append((parse_text(input), parse_text(response)))
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history.append((input, response))
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return chatbot, history, modality_cache
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def reset_user_input():
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return gr.update(value='')
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def reset_dialog():
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return [], []
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def reset_state():
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return None, [], [], []
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with gr.Blocks(scale=4) as demo:
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gr.Image("./images/lamm_title.png", show_label=False, height=50)
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gr.HTML(
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"""
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<p>
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<p align="center">
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<font size='4'>
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<a href="https://openlamm.github.io/" target="_blank">🏠 Home Page</a> • <a href="https://github.com/OpenLAMM/LAMM" target="_blank">🌏 Github</a> • <a href="https://arxiv.org/pdf/2306.06687.pdf" target="_blank">📰 Paper</a> • <a href="https://www.youtube.com/watch?v=M7XlIe8hhPk" target="_blank">▶️ YouTube </a> • <a href="https://www.bilibili.com/video/BV1kN411D7kt/?share_source=copy_web&vd_source=ab4c734425ed0114898300f2c037ac0b" target="_blank"> 📺 Bilibili • <a href="https://opendatalab.com/LAMM" target="_blank">📀 Data</a> • <a href="https://huggingface.co/openlamm" target="_blank">📦 LAMM Models</a>
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</font>
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</p>
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</p>
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"""
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)
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with gr.Row(scale=1):
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with gr.Column(scale=1):
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image_path = gr.Image(type="filepath", label="Image", value=None).style(height=600)
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chatbot = gr.Chatbot(scale=1).style(height=600)
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with gr.Row():
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with gr.Column(scale=4):
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with gr.Column(scale=12):
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user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(container=False)
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with gr.Column(min_width=32, scale=1):
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with gr.Row(scale=1):
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submitBtn = gr.Button("Submit", variant="primary")
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with gr.Row(scale=1):
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resubmitBtn = gr.Button("Resubmit", variant="primary")
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with gr.Column(scale=1):
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emptyBtn = gr.Button("Clear History")
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max_length = gr.Slider(0, 600, value=256, step=1.0, label="Maximum length", interactive=True)
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top_p = gr.Slider(0, 1, value=0.01, step=0.01, label="Top P", interactive=True)
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temperature = gr.Slider(0, 1, value=0.9, step=0.01, label="Temperature", interactive=True)
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history = gr.State([])
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modality_cache = gr.State([])
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submitBtn.click(
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predict, [
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user_input,
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image_path,
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chatbot,
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max_length,
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top_p,
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temperature,
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history,
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modality_cache,
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], [
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chatbot,
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history,
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modality_cache
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],
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show_progress=True
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)
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resubmitBtn.click(
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re_predict, [
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user_input,
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image_path,
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chatbot,
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max_length,
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top_p,
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temperature,
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history,
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modality_cache,
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], [
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chatbot,
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history,
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modality_cache
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],
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show_progress=True
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)
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submitBtn.click(reset_user_input, [], [user_input])
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emptyBtn.click(reset_state, outputs=[
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image_path,
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chatbot,
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history,
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modality_cache
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], show_progress=True)
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demo.queue().launch(enable_queue=True)
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