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Update app.py
#30
by
Jesuscarr
- opened
app.py
CHANGED
@@ -20,6 +20,12 @@ from minigpt4.runners import *
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from minigpt4.tasks import *
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def parse_args():
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parser = argparse.ArgumentParser(description="Demo")
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parser.add_argument("--cfg-path", type=str, default='eval_configs/minigpt4.yaml', help="path to configuration file.")
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parser.add_argument(
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@@ -32,8 +38,13 @@ def parse_args():
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args = parser.parse_args()
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return args
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def setup_seeds(config):
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seed = config.run_cfg.seed + get_rank()
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random.seed(seed)
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cudnn.benchmark = False
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cudnn.deterministic = True
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# ========================================
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# Model Initialization
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# ========================================
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cfg = Config(parse_args())
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vis_processor_cfg = cfg.datasets_cfg.cc_align.vis_processor.train
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vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
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chat = Chat(model, vis_processor)
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print('Initialization Finished')
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if chat_state is not None:
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chat_state.messages = []
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if img_list is not None:
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@@ -80,6 +93,17 @@ def gradio_reset(chat_state, img_list):
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return None, gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your image first', interactive=False), gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list
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def upload_img(gr_img, text_input, chat_state):
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if gr_img is None:
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return None, None, gr.update(interactive=True), chat_state, None
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chat_state = CONV_VISION.copy()
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@@ -88,67 +112,99 @@ def upload_img(gr_img, text_input, chat_state):
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return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list
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def gradio_ask(user_message, chatbot, chat_state):
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if len(user_message) == 0:
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return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
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chat.ask(user_message, chat_state)
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chatbot = chatbot + [[user_message, None]]
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return '', chatbot, chat_state
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def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature):
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llm_message = chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=temperature, max_length=2000)[0]
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chatbot[-1][1] = llm_message
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return chatbot, chat_state, img_list
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"""
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with gr.
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value=
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demo.launch(enable_queue=True)
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from minigpt4.tasks import *
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def parse_args():
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"""
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Parse command line arguments.
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Returns:
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argparse.Namespace: Parsed command line arguments.
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"""
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parser = argparse.ArgumentParser(description="Demo")
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parser.add_argument("--cfg-path", type=str, default='eval_configs/minigpt4.yaml', help="path to configuration file.")
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parser.add_argument(
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args = parser.parse_args()
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return args
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def setup_seeds(config):
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"""
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Set up random seeds for reproducibility.
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Parameters:
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config (Config): Configuration object.
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"""
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seed = config.run_cfg.seed + get_rank()
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random.seed(seed)
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cudnn.benchmark = False
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cudnn.deterministic = True
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def initialize_chat():
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"""
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Initialize the chat model.
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Returns:
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Chat: Initialized chat model.
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"""
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print('Initializing Chat')
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config = Config(parse_args())
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model_config = config.model_cfg
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model_cls = registry.get_model_class(model_config.arch)
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model = model_cls.from_config(model_config).to('cuda:0')
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vis_processor_cfg = config.datasets_cfg.cc_align.vis_processor.train
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vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
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chat = Chat(model, vis_processor)
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print('Initialization Finished')
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return chat
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def gradio_reset(chat_state, img_list):
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"""
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Reset the Gradio interface.
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Parameters:
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chat_state (gr.State): The current state of the chat.
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img_list (gr.State): The current list of images.
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Returns:
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tuple: Updated Gradio interface elements.
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"""
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if chat_state is not None:
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chat_state.messages = []
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if img_list is not None:
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return None, gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your image first', interactive=False), gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list
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def upload_img(gr_img, text_input, chat_state):
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"""
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Upload an image and update the Gradio interface.
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Parameters:
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gr_img (gr.Image): The uploaded image.
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text_input (gr.Textbox): The text input box.
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chat_state (gr.State): The current state of the chat.
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Returns:
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tuple: Updated Gradio interface elements.
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"""
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if gr_img is None:
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return None, None, gr.update(interactive=True), chat_state, None
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chat_state = CONV_VISION.copy()
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return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list
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def gradio_ask(user_message, chatbot, chat_state):
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"""
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Process user message and update the Gradio interface.
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Parameters:
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user_message (str): The message input by the user.
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chatbot (list): The current state of the chatbot.
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chat_state (gr.State): The current state of the chat.
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Returns:
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tuple: Updated Gradio interface elements.
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"""
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if len(user_message) == 0:
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return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
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chat.ask(user_message, chat_state)
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chatbot = chatbot + [[user_message, None]]
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return '', chatbot, chat_state
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def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature):
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"""
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Generate a chatbot answer and update the Gradio interface.
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Parameters:
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chatbot (list): The current state of the chatbot.
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chat_state (gr.State): The current state of the chat.
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img_list (gr.State): The current list of images.
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num_beams (int): The number of beams for the beam search.
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temperature (float): The temperature for the generation.
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Returns:
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tuple: Updated Gradio interface elements.
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"""
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llm_message = chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=temperature, max_length=2000)[0]
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chatbot[-1][1] = llm_message
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return chatbot, chat_state, img_list
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def main():
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"""
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Main function to run the Gradio interface.
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"""
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# Initialize the chat model
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chat = initialize_chat()
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# Set up the Gradio interface
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title = """<h1 align="center">Demo of MiniGPT-4</h1>"""
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description = """<h3>This is the demo of MiniGPT-4. Upload your images and start chatting!</h3>"""
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article = """<div style='display:flex; gap: 0.25rem; '><a href='https://minigpt-4.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a><a href='https://github.com/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/Github-Code-blue'></a><a href='https://github.com/TsuTikgiau/blip2-llm/blob/release_prepare/MiniGPT_4.pdf'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></div>
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"""
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with gr.Blocks() as demo:
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gr.Markdown(title)
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gr.Markdown(SHARED_UI_WARNING)
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gr.Markdown(description)
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gr.Markdown(article)
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with gr.Row():
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with gr.Column(scale=0.5):
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image = gr.Image(type="pil")
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upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary")
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clear = gr.Button("Restart")
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num_beams = gr.Slider(
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minimum=1,
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maximum=5,
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value=1,
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step=1,
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interactive=True,
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label="beam search numbers)",
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)
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temperature = gr.Slider(
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minimum=0.1,
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maximum=2.0,
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value=1.0,
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step=0.1,
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interactive=True,
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label="Temperature",
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)
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with gr.Column():
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chat_state = gr.State()
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img_list = gr.State()
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chatbot = gr.Chatbot(label='MiniGPT-4')
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text_input = gr.Textbox(label='User', placeholder='Please upload your image first', interactive=False)
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upload_button.click(upload_img, [image, text_input, chat_state], [image, text_input, upload_button, chat_state, img_list])
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text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
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gradio_answer, [chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list]
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)
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clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, upload_button, chat_state, img_list], queue=False)
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demo.launch(enable_queue=True)
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if __name__ == "__main__":
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main()
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