feat: gradio demo integration (#16)

Co-authored-by: Bo Liu <benjaminliu.eecs@gmail.com>
Co-authored-by: Haoyu Lu <ruclhy1998@163.com>

Makefile

@@ -4,7 +4,7 @@ COPYRIGHT      = "DeepSeek."
 PROJECT_PATH   = deepseek_vl
 SHELL          = /bin/bash
 SOURCE_FOLDERS = deepseek_vl
-PYTHON_FILES   = $(shell find $(SOURCE_FOLDERS) -type f -name "*.py" -o -name "*.pyi")
+PYTHON_FILES   = $(shell find $(SOURCE_FOLDERS) -type f -name "*.py" -o -name "*.pyi") cli_chat.py inference.py
 COMMIT_HASH    = $(shell git log -1 --format=%h)
 PATH           := $(HOME)/go/bin:$(PATH)
 PYTHON         ?= $(shell command -v python3 || command -v python)
@@ -86,7 +86,7 @@ format: py-format-install ruff-install addlicense-install
 	$(PYTHON) -m isort --project $(PROJECT_PATH) $(PYTHON_FILES)
 	$(PYTHON) -m black $(PYTHON_FILES)
 	$(PYTHON) -m ruff check . --fix --exit-zero
-	addlicense -c $(COPYRIGHT) -ignore tests/coverage.xml -l mit -y 2023-$(shell date +"%Y") $(SOURCE_FOLDERS)
+	addlicense -c $(COPYRIGHT) -ignore tests/coverage.xml -l mit -y 2023-$(shell date +"%Y") $(SOURCE_FOLDERS) cli_chat.py inference.py
 
 clean-py:
 	find . -type f -name  '*.py[co]' -delete

README.md

@@ -65,12 +65,17 @@ Introducing DeepSeek-VL, an open-source Vision-Language (VL) Model designed for
 
 [DeepSeek-VL: Towards Real-World Vision-Language Understanding](https://arxiv.org/abs/2403.05525)
 
-Haoyu Lu*, Wen Liu*, Bo Zhang**, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, Chong Ruan (*Equal Contribution, **Project Lead)
+Haoyu Lu*, Wen Liu*, Bo Zhang**, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, Chong Ruan (*Equal Contribution, **Project Lead)
 
 ![](https://github.com/deepseek-ai/DeepSeek-VL/blob/main/images/sample.jpg)
 
 ## 2. Release
 
+<details>
+<summary>✅ <b>2024-03-13</b>: Support DeepSeek-VL gradio demo.
+
+</details>
+
 <details>
 <summary>✅ <b>2024-03-11</b>: DeepSeek-VL family released, including <code>DeepSeek-VL-7B-base</code>, <code>DeepSeek-VL-7B-chat</code>, <code>DeepSeek-VL-1.3B-base</code>, and <code>DeepSeek-VL-1.3B-chat</code>.</summary>
 <br>The release includes a diverse set of models tailored for various applications within the DeepSeek-VL family. The models come in two sizes: 7B and 1.3B parameters, each offering base and chat variants to cater to different needs and integration scenarios.
@@ -170,6 +175,16 @@ python cli_chat.py --model_path "deepseek-ai/deepseek-vl-7b-chat"
 python cli_chat.py --model_path "local model path"
 ```
 
+### Gradio Demo
+```bash
+pip install -e .[gradio]
+
+python deepseek_vl/serve/app_deepseek.py
+```
+![](./images/gradio_demo.png)
+
+Have Fun!
+
 ## 5. License
 
 This code repository is licensed under [the MIT License](https://github.com/deepseek-ai/DeepSeek-LLM/blob/HEAD/LICENSE-CODE). The use of DeepSeek-VL Base/Chat models is subject to [DeepSeek Model License](https://github.com/deepseek-ai/DeepSeek-LLM/blob/HEAD/LICENSE-MODEL). DeepSeek-VL series (including Base and Chat) supports commercial use.
@@ -179,7 +194,7 @@ This code repository is licensed under [the MIT License](https://github.com/deep
 ```
 @misc{lu2024deepseekvl,
       title={DeepSeek-VL: Towards Real-World Vision-Language Understanding},
-      author={Haoyu Lu and Wen Liu and Bo Zhang and Bingxuan Wang and Kai Dong and Bo Liu and Jingxiang Sun and Tongzheng Ren and Zhuoshu Li and Yaofeng Sun and Chengqi Deng and Hanwei Xu and Zhenda Xie and Chong Ruan},
+      author={Haoyu Lu and Wen Liu and Bo Zhang and Bingxuan Wang and Kai Dong and Bo Liu and Jingxiang Sun and Tongzheng Ren and Zhuoshu Li and Hao Yang and Yaofeng Sun and Chengqi Deng and Hanwei Xu and Zhenda Xie and Chong Ruan},
       year={2024},
       eprint={2403.05525},
       archivePrefix={arXiv},

cli_chat.py

@@ -1,11 +1,31 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
 # -*- coding: utf-8 -*-
 
 import argparse
 import os
 import sys
-from PIL import Image
 from threading import Thread
+
 import torch
+from PIL import Image
 from transformers import TextIteratorStreamer
 
 from deepseek_vl.utils.io import load_pretrained_model
@@ -33,22 +53,19 @@ def get_help_message(image_token):
 
 
 @torch.inference_mode()
-def response(args, conv, pil_images, tokenizer, vl_chat_processor, vl_gpt, generation_config):
+def response(
-
+    args, conv, pil_images, tokenizer, vl_chat_processor, vl_gpt, generation_config
+):
     prompt = conv.get_prompt()
     prepare_inputs = vl_chat_processor.__call__(
-        prompt=prompt,
+        prompt=prompt, images=pil_images, force_batchify=True
-        images=pil_images,
-        force_batchify=True
     ).to(vl_gpt.device)
 
     # run image encoder to get the image embeddings
     inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
 
     streamer = TextIteratorStreamer(
-        tokenizer=tokenizer,
+        tokenizer=tokenizer, skip_prompt=True, skip_special_tokens=True
-        skip_prompt=True,
-        skip_special_tokens=True
     )
     generation_config["inputs_embeds"] = inputs_embeds
     generation_config["attention_mask"] = prepare_inputs.attention_mask
@@ -79,7 +96,6 @@ def chat(args, tokenizer, vl_chat_processor, vl_gpt, generation_config):
     help_msg = get_help_message(image_token)
 
     while True:
-
         print(help_msg)
 
         pil_images = []
@@ -87,9 +103,10 @@ def chat(args, tokenizer, vl_chat_processor, vl_gpt, generation_config):
         roles = conv.roles
 
         while True:
-
             # get user input
-            user_input = get_user_input(f"{roles[0]} [{image_token} indicates an image]: ")
+            user_input = get_user_input(
+                f"{roles[0]} [{image_token} indicates an image]: "
+            )
 
             if user_input == "exit":
                 print("Chat program exited.")
@@ -135,11 +152,21 @@ def chat(args, tokenizer, vl_chat_processor, vl_gpt, generation_config):
                         sys.exit(0)
 
                     else:
-                        print(f"File error, `{image_file}` does not exist. Please input the correct file path.")
+                        print(
+                            f"File error, `{image_file}` does not exist. Please input the correct file path."
+                        )
 
                 # get the answer by the model's prediction
                 answer = ""
-                answer_iter = response(args, conv, pil_images, tokenizer, vl_chat_processor, vl_gpt, generation_config)
+                answer_iter = response(
+                    args,
+                    conv,
+                    pil_images,
+                    tokenizer,
+                    vl_chat_processor,
+                    vl_gpt,
+                    generation_config,
+                )
                 sys.stdout.write(f"{conv.roles[1]}: ")
                 for char in answer_iter:
                     answer += char
@@ -153,7 +180,6 @@ def chat(args, tokenizer, vl_chat_processor, vl_gpt, generation_config):
 
 
 def main(args):
-
     # setup
     tokenizer, vl_chat_processor, vl_gpt = load_pretrained_model(args.model_path)
     generation_config = dict(
@@ -164,12 +190,14 @@ def main(args):
         use_cache=True,
     )
     if args.temperature > 0:
-        generation_config.update({
+        generation_config.update(
-            "do_sample": True,
+            {
-            "top_p": args.top_p,
+                "do_sample": True,
-            "temperature": args.temperature,
+                "top_p": args.top_p,
-            "repetition_penalty": args.repetition_penalty,
+                "temperature": args.temperature,
-        })
+                "repetition_penalty": args.repetition_penalty,
+            }
+        )
     else:
         generation_config.update({"do_sample": False})
 
@@ -178,12 +206,15 @@ def main(args):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument("--model_path", type=str, default="deepseek-ai/deepseek-vl-7b-chat",
+    parser.add_argument(
-                        help="the huggingface model name or the local path of the downloaded huggingface model.")
+        "--model_path",
+        type=str,
+        default="deepseek-ai/deepseek-vl-7b-chat",
+        help="the huggingface model name or the local path of the downloaded huggingface model.",
+    )
     parser.add_argument("--temperature", type=float, default=0.2)
     parser.add_argument("--top_p", type=float, default=0.95)
     parser.add_argument("--repetition_penalty", type=float, default=1.1)
     parser.add_argument("--max_gen_len", type=int, default=512)
     args = parser.parse_args()
     main(args)
-

deepseek_vl/serve/app_deepseek.py

@@ -0,0 +1,514 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+# -*- coding:utf-8 -*-
+
+import base64
+from io import BytesIO
+
+import gradio as gr
+import torch
+from app_modules.gradio_utils import (
+    cancel_outputing,
+    delete_last_conversation,
+    reset_state,
+    reset_textbox,
+    transfer_input,
+    wrap_gen_fn,
+)
+from app_modules.overwrites import reload_javascript
+from app_modules.presets import CONCURRENT_COUNT, description, description_top, title
+from app_modules.utils import configure_logger, is_variable_assigned, strip_stop_words
+
+from deepseek_vl.serve.inference import (
+    convert_conversation_to_prompts,
+    deepseek_generate,
+    load_model,
+)
+from deepseek_vl.utils.conversation import SeparatorStyle
+
+
+def load_models():
+    models = {
+        "DeepSeek-VL 7B": "deepseek-ai/deepseek-vl-7b-chat",
+    }
+
+    for model_name in models:
+        models[model_name] = load_model(models[model_name])
+
+    return models
+
+
+logger = configure_logger()
+models = load_models()
+MODELS = sorted(list(models.keys()))
+
+
+def generate_prompt_with_history(
+    text, image, history, vl_chat_processor, tokenizer, max_length=2048
+):
+    """
+    Generate a prompt with history for the deepseek application.
+
+    Args:
+        text (str): The text prompt.
+        image (str): The image prompt.
+        history (list): List of previous conversation messages.
+        tokenizer: The tokenizer used for encoding the prompt.
+        max_length (int): The maximum length of the prompt.
+
+    Returns:
+        tuple: A tuple containing the generated prompt, image list, conversation, and conversation copy. If the prompt could not be generated within the max_length limit, returns None.
+    """
+
+    sft_format = "deepseek"
+    user_role_ind = 0
+    bot_role_ind = 1
+
+    # Initialize conversation
+    conversation = vl_chat_processor.new_chat_template()
+
+    if history:
+        conversation.messages = history
+
+    if image is not None:
+        if "<image_placeholder>" not in text:
+            text = (
+                "<image_placeholder>" + "\n" + text
+            )  # append the <image_placeholder> in a new line after the text prompt
+        text = (text, image)
+
+    conversation.append_message(conversation.roles[user_role_ind], text)
+    conversation.append_message(conversation.roles[bot_role_ind], "")
+
+    # Create a copy of the conversation to avoid history truncation in the UI
+    conversation_copy = conversation.copy()
+    logger.info("=" * 80)
+    logger.info(get_prompt(conversation))
+
+    rounds = len(conversation.messages) // 2
+
+    for _ in range(rounds):
+        current_prompt = get_prompt(conversation)
+        current_prompt = (
+            current_prompt.replace("</s>", "")
+            if sft_format == "deepseek"
+            else current_prompt
+        )
+
+        if torch.tensor(tokenizer.encode(current_prompt)).size(-1) <= max_length:
+            return conversation_copy
+
+        if len(conversation.messages) % 2 != 0:
+            gr.Error("The messages between user and assistant are not paired.")
+            return
+
+        try:
+            for _ in range(2):  # pop out two messages in a row
+                conversation.messages.pop(0)
+        except IndexError:
+            gr.Error("Input text processing failed, unable to respond in this round.")
+            return None
+
+    gr.Error("Prompt could not be generated within max_length limit.")
+    return None
+
+
+def to_gradio_chatbot(conv):
+    """Convert the conversation to gradio chatbot format."""
+    ret = []
+    for i, (role, msg) in enumerate(conv.messages[conv.offset :]):
+        if i % 2 == 0:
+            if type(msg) is tuple:
+                msg, image = msg
+                if isinstance(image, str):
+                    with open(image, "rb") as f:
+                        data = f.read()
+                    img_b64_str = base64.b64encode(data).decode()
+                    image_str = f'<video src="data:video/mp4;base64,{img_b64_str}" controls width="426" height="240"></video>'
+                    msg = msg.replace("\n".join(["<image_placeholder>"] * 4), image_str)
+                else:
+                    max_hw, min_hw = max(image.size), min(image.size)
+                    aspect_ratio = max_hw / min_hw
+                    max_len, min_len = 800, 400
+                    shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
+                    longest_edge = int(shortest_edge * aspect_ratio)
+                    W, H = image.size
+                    if H > W:
+                        H, W = longest_edge, shortest_edge
+                    else:
+                        H, W = shortest_edge, longest_edge
+                    image = image.resize((W, H))
+                    buffered = BytesIO()
+                    image.save(buffered, format="JPEG")
+                    img_b64_str = base64.b64encode(buffered.getvalue()).decode()
+                    img_str = f'<img src="data:image/png;base64,{img_b64_str}" alt="user upload image" />'
+                    msg = msg.replace("<image_placeholder>", img_str)
+            ret.append([msg, None])
+        else:
+            ret[-1][-1] = msg
+    return ret
+
+
+def to_gradio_history(conv):
+    """Convert the conversation to gradio history state."""
+    return conv.messages[conv.offset :]
+
+
+def get_prompt(conv) -> str:
+    """Get the prompt for generation."""
+    system_prompt = conv.system_template.format(system_message=conv.system_message)
+    if conv.sep_style == SeparatorStyle.DeepSeek:
+        seps = [conv.sep, conv.sep2]
+        if system_prompt == "" or system_prompt is None:
+            ret = ""
+        else:
+            ret = system_prompt + seps[0]
+        for i, (role, message) in enumerate(conv.messages):
+            if message:
+                if type(message) is tuple:  # multimodal message
+                    message, _ = message
+                ret += role + ": " + message + seps[i % 2]
+            else:
+                ret += role + ":"
+        return ret
+    else:
+        return conv.get_prompt
+
+
+@wrap_gen_fn
+def predict(
+    text,
+    image,
+    chatbot,
+    history,
+    top_p,
+    temperature,
+    repetition_penalty,
+    max_length_tokens,
+    max_context_length_tokens,
+    model_select_dropdown,
+):
+    """
+    Function to predict the response based on the user's input and selected model.
+
+    Parameters:
+    user_text (str): The input text from the user.
+    user_image (str): The input image from the user.
+    chatbot (str): The chatbot's name.
+    history (str): The history of the chat.
+    top_p (float): The top-p parameter for the model.
+    temperature (float): The temperature parameter for the model.
+    max_length_tokens (int): The maximum length of tokens for the model.
+    max_context_length_tokens (int): The maximum length of context tokens for the model.
+    model_select_dropdown (str): The selected model from the dropdown.
+
+    Returns:
+    generator: A generator that yields the chatbot outputs, history, and status.
+    """
+    print("running the prediction function")
+    try:
+        tokenizer, vl_gpt, vl_chat_processor = models[model_select_dropdown]
+
+        if text == "":
+            yield chatbot, history, "Empty context."
+            return
+    except KeyError:
+        yield [[text, "No Model Found"]], [], "No Model Found"
+        return
+
+    conversation = generate_prompt_with_history(
+        text,
+        image,
+        history,
+        vl_chat_processor,
+        tokenizer,
+        max_length=max_context_length_tokens,
+    )
+    prompts = convert_conversation_to_prompts(conversation)
+
+    stop_words = conversation.stop_str
+    gradio_chatbot_output = to_gradio_chatbot(conversation)
+
+    full_response = ""
+    with torch.no_grad():
+        for x in deepseek_generate(
+            prompts=prompts,
+            vl_gpt=vl_gpt,
+            vl_chat_processor=vl_chat_processor,
+            tokenizer=tokenizer,
+            stop_words=stop_words,
+            max_length=max_length_tokens,
+            temperature=temperature,
+            repetition_penalty=repetition_penalty,
+            top_p=top_p,
+        ):
+            full_response += x
+            response = strip_stop_words(full_response, stop_words)
+            conversation.update_last_message(response)
+            gradio_chatbot_output[-1][1] = response
+            yield gradio_chatbot_output, to_gradio_history(
+                conversation
+            ), "Generating..."
+
+    print("flushed result to gradio")
+    torch.cuda.empty_cache()
+
+    if is_variable_assigned("x"):
+        print(f"{model_select_dropdown}:\n{text}\n{'-' * 80}\n{x}\n{'=' * 80}")
+        print(
+            f"temperature: {temperature}, top_p: {top_p}, repetition_penalty: {repetition_penalty}, max_length_tokens: {max_length_tokens}"
+        )
+
+    yield gradio_chatbot_output, to_gradio_history(conversation), "Generate: Success"
+
+
+def retry(
+    text,
+    image,
+    chatbot,
+    history,
+    top_p,
+    temperature,
+    repetition_penalty,
+    max_length_tokens,
+    max_context_length_tokens,
+    model_select_dropdown,
+):
+    if len(history) == 0:
+        yield (chatbot, history, "Empty context")
+        return
+
+    chatbot.pop()
+    history.pop()
+    text = history.pop()[-1]
+    if type(text) is tuple:
+        text, image = text
+
+    yield from predict(
+        text,
+        image,
+        chatbot,
+        history,
+        top_p,
+        temperature,
+        repetition_penalty,
+        max_length_tokens,
+        max_context_length_tokens,
+        model_select_dropdown,
+    )
+
+
+def build_demo(MODELS):
+    with open("deepseek_vl/serve/assets/custom.css", "r", encoding="utf-8") as f:
+        customCSS = f.read()
+
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+        history = gr.State([])
+        input_text = gr.State()
+        input_image = gr.State()
+
+        with gr.Row():
+            gr.HTML(title)
+            status_display = gr.Markdown("Success", elem_id="status_display")
+        gr.Markdown(description_top)
+
+        with gr.Row(equal_height=True):
+            with gr.Column(scale=4):
+                with gr.Row():
+                    chatbot = gr.Chatbot(
+                        elem_id="deepseek_chatbot",
+                        show_share_button=True,
+                        likeable=True,
+                        bubble_full_width=False,
+                        height=600,
+                    )
+                with gr.Row():
+                    with gr.Column(scale=4):
+                        text_box = gr.Textbox(
+                            show_label=False, placeholder="Enter text", container=False
+                        )
+                    with gr.Column(
+                        min_width=70,
+                    ):
+                        submitBtn = gr.Button("Send")
+                    with gr.Column(
+                        min_width=70,
+                    ):
+                        cancelBtn = gr.Button("Stop")
+                with gr.Row():
+                    emptyBtn = gr.Button(
+                        "🧹 New Conversation",
+                    )
+                    retryBtn = gr.Button("🔄 Regenerate")
+                    delLastBtn = gr.Button("🗑️ Remove Last Turn")
+
+            with gr.Column():
+                image_box = gr.Image(type="pil")
+
+                with gr.Tab(label="Parameter Setting") as parameter_row:
+                    top_p = gr.Slider(
+                        minimum=-0,
+                        maximum=1.0,
+                        value=0.95,
+                        step=0.05,
+                        interactive=True,
+                        label="Top-p",
+                    )
+                    temperature = gr.Slider(
+                        minimum=0,
+                        maximum=1.0,
+                        value=0.1,
+                        step=0.1,
+                        interactive=True,
+                        label="Temperature",
+                    )
+                    repetition_penalty = gr.Slider(
+                        minimum=0.0,
+                        maximum=2.0,
+                        value=1.1,
+                        step=0.1,
+                        interactive=True,
+                        label="Repetition penalty",
+                    )
+                    max_length_tokens = gr.Slider(
+                        minimum=0,
+                        maximum=4096,
+                        value=2048,
+                        step=8,
+                        interactive=True,
+                        label="Max Generation Tokens",
+                    )
+                    max_context_length_tokens = gr.Slider(
+                        minimum=0,
+                        maximum=4096,
+                        value=4096,
+                        step=128,
+                        interactive=True,
+                        label="Max History Tokens",
+                    )
+                    model_select_dropdown = gr.Dropdown(
+                        label="Select Models",
+                        choices=MODELS,
+                        multiselect=False,
+                        value=MODELS[0],
+                        interactive=True,
+                    )
+
+        examples_list = [
+            [
+                "deepseek_vl/serve/examples/rap.jpeg",
+                "Can you write me a master rap song that rhymes very well based on this image?",
+            ],
+            [
+                "deepseek_vl/serve/examples/app.png",
+                "What is this app about?",
+            ],
+            [
+                "deepseek_vl/serve/examples/pipeline.png",
+                "Help me write a python code based on the image.",
+            ],
+            [
+                "deepseek_vl/serve/examples/chart.png",
+                "Could you help me to re-draw this picture with python codes?",
+            ],
+            [
+                "deepseek_vl/serve/examples/mirror.png",
+                "How many people are there in the image. Why?",
+            ],
+            [
+                "deepseek_vl/serve/examples/puzzle.png",
+                "Can this 2 pieces combine together?",
+            ],
+        ]
+        gr.Examples(examples=examples_list, inputs=[image_box, text_box])
+        gr.Markdown(description)
+
+        input_widgets = [
+            input_text,
+            input_image,
+            chatbot,
+            history,
+            top_p,
+            temperature,
+            repetition_penalty,
+            max_length_tokens,
+            max_context_length_tokens,
+            model_select_dropdown,
+        ]
+        output_widgets = [chatbot, history, status_display]
+
+        transfer_input_args = dict(
+            fn=transfer_input,
+            inputs=[text_box, image_box],
+            outputs=[input_text, input_image, text_box, image_box, submitBtn],
+            show_progress=True,
+        )
+
+        predict_args = dict(
+            fn=predict,
+            inputs=input_widgets,
+            outputs=output_widgets,
+            show_progress=True,
+        )
+
+        retry_args = dict(
+            fn=retry,
+            inputs=input_widgets,
+            outputs=output_widgets,
+            show_progress=True,
+        )
+
+        reset_args = dict(
+            fn=reset_textbox, inputs=[], outputs=[text_box, status_display]
+        )
+
+        predict_events = [
+            text_box.submit(**transfer_input_args).then(**predict_args),
+            submitBtn.click(**transfer_input_args).then(**predict_args),
+        ]
+
+        emptyBtn.click(reset_state, outputs=output_widgets, show_progress=True)
+        emptyBtn.click(**reset_args)
+        retryBtn.click(**retry_args)
+
+        delLastBtn.click(
+            delete_last_conversation,
+            [chatbot, history],
+            output_widgets,
+            show_progress=True,
+        )
+
+        cancelBtn.click(cancel_outputing, [], [status_display], cancels=predict_events)
+
+    return demo
+
+
+if __name__ == "__main__":
+    demo = build_demo(MODELS)
+    demo.title = "DeepSeek-VL Chatbot"
+
+    reload_javascript()
+    demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
+        share=False,
+        favicon_path="deepseek_vl/serve/assets/favicon.ico",
+        inbrowser=False,
+        server_name="0.0.0.0",
+        server_port=8122,
+    )

deepseek_vl/serve/app_modules/gradio_utils.py

@@ -0,0 +1,94 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from functools import wraps
+
+import gradio as gr
+
+
+def wrap_gen_fn(gen_fn):
+    @wraps(gen_fn)
+    def wrapped_gen_fn(prompt, *args, **kwargs):
+        try:
+            yield from gen_fn(prompt, *args, **kwargs)
+        except gr.Error as g_err:
+            raise g_err
+        except Exception as e:
+            raise gr.Error(f"Failed to generate text: {e}") from e
+
+    return wrapped_gen_fn
+
+
+def delete_last_conversation(chatbot, history):
+    if len(history) % 2 != 0:
+        gr.Error("history length is not even")
+        return (
+            chatbot,
+            history,
+            "Delete Done",
+        )
+
+    if len(chatbot) > 0:
+        chatbot.pop()
+
+    if len(history) > 0 and len(history) % 2 == 0:
+        history.pop()
+        history.pop()
+
+    return (
+        chatbot,
+        history,
+        "Delete Done",
+    )
+
+
+def reset_state():
+    return [], [], None, "Reset Done"
+
+
+def reset_textbox():
+    return gr.update(value=""), ""
+
+
+def cancel_outputing():
+    return "Stop Done"
+
+
+def transfer_input(input_text, input_image):
+    print("transferring input text and input image")
+    return (
+        input_text,
+        input_image,
+        gr.update(value=""),
+        gr.update(value=None),
+        gr.Button(visible=True),
+    )
+
+
+class State:
+    interrupted = False
+
+    def interrupt(self):
+        self.interrupted = True
+
+    def recover(self):
+        self.interrupted = False
+
+
+shared_state = State()

deepseek_vl/serve/app_modules/overwrites.py

@@ -0,0 +1,81 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from __future__ import annotations
+
+import logging
+from typing import List, Tuple
+
+from app_modules.presets import gr
+from app_modules.utils import convert_asis, convert_mdtext, detect_converted_mark
+
+
+def compact_text_chunks(self, prompt, text_chunks: List[str]) -> List[str]:
+    logging.debug("Compacting text chunks...🚀🚀🚀")
+    combined_str = [c.strip() for c in text_chunks if c.strip()]
+    combined_str = [f"[{index+1}] {c}" for index, c in enumerate(combined_str)]
+    combined_str = "\n\n".join(combined_str)
+    # resplit based on self.max_chunk_overlap
+    text_splitter = self.get_text_splitter_given_prompt(prompt, 1, padding=1)
+    return text_splitter.split_text(combined_str)
+
+
+def postprocess(
+    self, y: List[Tuple[str | None, str | None]]
+) -> List[Tuple[str | None, str | None]]:
+    """
+    Parameters:
+        y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.
+    Returns:
+        List of tuples representing the message and response. Each message and response will be a string of HTML.
+    """
+    if y is None or y == []:
+        return []
+    temp = []
+    for x in y:
+        user, bot = x
+        if not detect_converted_mark(user):
+            user = convert_asis(user)
+        if not detect_converted_mark(bot):
+            bot = convert_mdtext(bot)
+        temp.append((user, bot))
+    return temp
+
+
+with open("deepseek_vl/serve/assets/custom.js", "r", encoding="utf-8") as f, open(
+    "deepseek_vl/serve/assets/Kelpy-Codos.js", "r", encoding="utf-8"
+) as f2:
+    customJS = f.read()
+    kelpyCodos = f2.read()
+
+
+def reload_javascript():
+    print("Reloading javascript...")
+    js = f"<script>{customJS}</script><script>{kelpyCodos}</script>"
+
+    def template_response(*args, **kwargs):
+        res = GradioTemplateResponseOriginal(*args, **kwargs)
+        res.body = res.body.replace(b"</html>", f"{js}</html>".encode("utf8"))
+        res.init_headers()
+        return res
+
+    gr.routes.templates.TemplateResponse = template_response
+
+
+GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse

deepseek_vl/serve/app_modules/presets.py

@@ -0,0 +1,96 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+# -*- coding:utf-8 -*-
+import gradio as gr
+
+title = """<h1 align="left" style="min-width:200px; margin-top:0;">Chat with DeepSeek-VL </h1>"""
+description_top = """"""
+description = """"""
+CONCURRENT_COUNT = 10
+
+
+ALREADY_CONVERTED_MARK = "<!-- ALREADY CONVERTED BY PARSER. -->"
+
+small_and_beautiful_theme = gr.themes.Soft(
+    primary_hue=gr.themes.Color(
+        c50="#EBFAF2",
+        c100="#CFF3E1",
+        c200="#A8EAC8",
+        c300="#77DEA9",
+        c400="#3FD086",
+        c500="#02C160",
+        c600="#06AE56",
+        c700="#05974E",
+        c800="#057F45",
+        c900="#04673D",
+        c950="#2E5541",
+        name="small_and_beautiful",
+    ),
+    secondary_hue=gr.themes.Color(
+        c50="#576b95",
+        c100="#576b95",
+        c200="#576b95",
+        c300="#576b95",
+        c400="#576b95",
+        c500="#576b95",
+        c600="#576b95",
+        c700="#576b95",
+        c800="#576b95",
+        c900="#576b95",
+        c950="#576b95",
+    ),
+    neutral_hue=gr.themes.Color(
+        name="gray",
+        c50="#f6f7f8",
+        # c100="#f3f4f6",
+        c100="#F2F2F2",
+        c200="#e5e7eb",
+        c300="#d1d5db",
+        c400="#B2B2B2",
+        c500="#808080",
+        c600="#636363",
+        c700="#515151",
+        c800="#393939",
+        # c900="#272727",
+        c900="#2B2B2B",
+        c950="#171717",
+    ),
+    radius_size=gr.themes.sizes.radius_sm,
+).set(
+    # button_primary_background_fill="*primary_500",
+    button_primary_background_fill_dark="*primary_600",
+    # button_primary_background_fill_hover="*primary_400",
+    # button_primary_border_color="*primary_500",
+    button_primary_border_color_dark="*primary_600",
+    button_primary_text_color="white",
+    button_primary_text_color_dark="white",
+    button_secondary_background_fill="*neutral_100",
+    button_secondary_background_fill_hover="*neutral_50",
+    button_secondary_background_fill_dark="*neutral_900",
+    button_secondary_text_color="*neutral_800",
+    button_secondary_text_color_dark="white",
+    # background_fill_primary="#F7F7F7",
+    # background_fill_primary_dark="#1F1F1F",
+    # block_title_text_color="*primary_500",
+    block_title_background_fill_dark="*primary_900",
+    block_label_background_fill_dark="*primary_900",
+    input_background_fill="#F6F6F6",
+    # chatbot_code_background_color_dark="*neutral_950",
+)

deepseek_vl/serve/app_modules/utils.py

@@ -0,0 +1,228 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+# -*- coding:utf-8 -*-
+from __future__ import annotations
+
+import html
+import logging
+import os
+import re
+import time
+
+import mdtex2html
+from app_modules.presets import ALREADY_CONVERTED_MARK
+from markdown import markdown
+from pygments import highlight
+from pygments.formatters import HtmlFormatter
+from pygments.lexers import ClassNotFound, get_lexer_by_name, guess_lexer
+
+logger = logging.getLogger("gradio_logger")
+
+
+def configure_logger():
+    logger = logging.getLogger("gradio_logger")
+    logger.setLevel(logging.DEBUG)
+
+    timestr = time.strftime("%Y%m%d-%H%M%S")
+    os.makedirs("deepseek_vl/serve/logs", exist_ok=True)
+    file_handler = logging.FileHandler(
+        f"deepseek_vl/serve/logs/{timestr}_gradio_log.log"
+    )
+    console_handler = logging.StreamHandler()
+
+    formatter = logging.Formatter(
+        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    )
+    console_handler.setFormatter(formatter)
+    file_handler.setFormatter(formatter)
+
+    console_handler.setLevel(logging.INFO)
+    file_handler.setLevel(logging.INFO)
+
+    logger.addHandler(console_handler)
+    logger.addHandler(file_handler)
+
+    return logger
+
+
+def strip_stop_words(x, stop_words):
+    for w in stop_words:
+        if w in x:
+            return x[: x.index(w)].strip()
+    return x.strip()
+
+
+def format_output(history, text, x):
+    updated_history = history + [[text, x]]
+    a = [[y[0], convert_to_markdown(y[1])] for y in updated_history]
+    return a, updated_history
+
+
+def markdown_to_html_with_syntax_highlight(md_str):  # deprecated
+    def replacer(match):
+        lang = match.group(1) or "text"
+        code = match.group(2)
+
+        try:
+            lexer = get_lexer_by_name(lang, stripall=True)
+        except ValueError:
+            lexer = get_lexer_by_name("text", stripall=True)
+
+        formatter = HtmlFormatter()
+        highlighted_code = highlight(code, lexer, formatter)
+
+        return f'<pre><code class="{lang}">{highlighted_code}</code></pre>'
+
+    code_block_pattern = r"```(\w+)?\n([\s\S]+?)\n```"
+    md_str = re.sub(code_block_pattern, replacer, md_str, flags=re.MULTILINE)
+
+    html_str = markdown(md_str)
+    return html_str
+
+
+def normalize_markdown(md_text: str) -> str:  # deprecated
+    lines = md_text.split("\n")
+    normalized_lines = []
+    inside_list = False
+
+    for i, line in enumerate(lines):
+        if re.match(r"^(\d+\.|-|\*|\+)\s", line.strip()):
+            if not inside_list and i > 0 and lines[i - 1].strip() != "":
+                normalized_lines.append("")
+            inside_list = True
+            normalized_lines.append(line)
+        elif inside_list and line.strip() == "":
+            if i < len(lines) - 1 and not re.match(
+                r"^(\d+\.|-|\*|\+)\s", lines[i + 1].strip()
+            ):
+                normalized_lines.append(line)
+            continue
+        else:
+            inside_list = False
+            normalized_lines.append(line)
+
+    return "\n".join(normalized_lines)
+
+
+def convert_mdtext(md_text):
+    code_block_pattern = re.compile(r"```(.*?)(?:```|$)", re.DOTALL)
+    inline_code_pattern = re.compile(r"`(.*?)`", re.DOTALL)
+    code_blocks = code_block_pattern.findall(md_text)
+    non_code_parts = code_block_pattern.split(md_text)[::2]
+
+    result = []
+    for non_code, code in zip(non_code_parts, code_blocks + [""]):
+        if non_code.strip():
+            non_code = normalize_markdown(non_code)
+            if inline_code_pattern.search(non_code):
+                result.append(markdown(non_code, extensions=["tables"]))
+            else:
+                result.append(mdtex2html.convert(non_code, extensions=["tables"]))
+        if code.strip():
+            code = f"\n```{code}\n\n```"
+            code = markdown_to_html_with_syntax_highlight(code)
+            result.append(code)
+    result = "".join(result)
+    result += ALREADY_CONVERTED_MARK
+    return result
+
+
+def convert_asis(userinput):
+    return f'<p style="white-space:pre-wrap;">{html.escape(userinput)}</p>{ALREADY_CONVERTED_MARK}'
+
+
+def is_stop_word_or_prefix(s: str, stop_words: list) -> bool:
+    return any(s.endswith(stop_word) for stop_word in stop_words)
+
+
+def detect_converted_mark(userinput):
+    return bool(userinput.endswith(ALREADY_CONVERTED_MARK))
+
+
+def detect_language(code):
+    first_line = "" if code.startswith("\n") else code.strip().split("\n", 1)[0]
+    language = first_line.lower() if first_line else ""
+    code_without_language = code[len(first_line) :].lstrip() if first_line else code
+    return language, code_without_language
+
+
+def convert_to_markdown(text):
+    text = text.replace("$", "&#36;")
+    text = text.replace("\r\n", "\n")
+
+    def replace_leading_tabs_and_spaces(line):
+        new_line = []
+
+        for char in line:
+            if char == "\t":
+                new_line.append("&#9;")
+            elif char == " ":
+                new_line.append("&nbsp;")
+            else:
+                break
+        return "".join(new_line) + line[len(new_line) :]
+
+    markdown_text = ""
+    lines = text.split("\n")
+    in_code_block = False
+
+    for line in lines:
+        if in_code_block is False and line.startswith("```"):
+            in_code_block = True
+            markdown_text += f"{line}\n"
+        elif in_code_block is True and line.startswith("```"):
+            in_code_block = False
+            markdown_text += f"{line}\n"
+        elif in_code_block:
+            markdown_text += f"{line}\n"
+        else:
+            line = replace_leading_tabs_and_spaces(line)
+            line = re.sub(r"^(#)", r"\\\1", line)
+            markdown_text += f"{line}  \n"
+
+    return markdown_text
+
+
+def add_language_tag(text):
+    def detect_language(code_block):
+        try:
+            lexer = guess_lexer(code_block)
+            return lexer.name.lower()
+        except ClassNotFound:
+            return ""
+
+    code_block_pattern = re.compile(r"(```)(\w*\n[^`]+```)", re.MULTILINE)
+
+    def replacement(match):
+        code_block = match.group(2)
+        if match.group(2).startswith("\n"):
+            language = detect_language(code_block)
+            return (
+                f"```{language}{code_block}```" if language else f"```\n{code_block}```"
+            )
+        else:
+            return match.group(1) + code_block + "```"
+
+    text2 = code_block_pattern.sub(replacement, text)
+    return text2
+
+
+def is_variable_assigned(var_name: str) -> bool:
+    return var_name in locals()

deepseek_vl/serve/assets/Kelpy-Codos.js

@@ -0,0 +1,100 @@
+/**
+ * Copyright (c) 2023-2024 DeepSeek.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy of
+ * this software and associated documentation files (the "Software"), to deal in
+ * the Software without restriction, including without limitation the rights to
+ * use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+ * the Software, and to permit persons to whom the Software is furnished to do so,
+ * subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+ * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+ * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+ * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+ * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+// ==UserScript==
+// @name         Kelpy Codos
+// @namespace    https://github.com/Keldos-Li/Kelpy-Codos
+// @version      1.0.5
+// @author       Keldos; https://keldos.me/
+// @description  Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially.
+//               Based on Chuanhu ChatGPT version: ac04408 (2023-3-22)
+// @license      GPL-3.0
+// @grant        none
+// ==/UserScript==
+
+(function () {
+  "use strict";
+
+  function addCopyButton(pre) {
+    var code = pre.querySelector("code");
+    if (!code) {
+      return; // 如果没有找到 <code> 元素，则不添加按钮
+    }
+    var firstChild = code.firstChild;
+    if (!firstChild) {
+      return; // 如果 <code> 元素没有子节点，则不添加按钮
+    }
+    var button = document.createElement("button");
+    button.textContent = "\uD83D\uDCCE"; // 使用 📎 符号作为“复制”按钮的文本
+    button.style.position = "relative";
+    button.style.float = "right";
+    button.style.fontSize = "1em"; // 可选：调整按钮大小
+    button.style.background = "none"; // 可选：去掉背景颜色
+    button.style.border = "none"; // 可选：去掉边框
+    button.style.cursor = "pointer"; // 可选：显示指针样式
+    button.addEventListener("click", function () {
+      var range = document.createRange();
+      range.selectNodeContents(code);
+      range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前
+      var selection = window.getSelection();
+      selection.removeAllRanges();
+      selection.addRange(range);
+
+      try {
+        var success = document.execCommand("copy");
+        if (success) {
+          button.textContent = "\u2714";
+          setTimeout(function () {
+            button.textContent = "\uD83D\uDCCE"; // 恢复按钮为“复制”
+          }, 2000);
+        } else {
+          button.textContent = "\u2716";
+        }
+      } catch (e) {
+        console.error(e);
+        button.textContent = "\u2716";
+      }
+
+      selection.removeAllRanges();
+    });
+    code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前
+  }
+
+  function handleNewElements(mutationsList, observer) {
+    for (var mutation of mutationsList) {
+      if (mutation.type === "childList") {
+        for (var node of mutation.addedNodes) {
+          if (node.nodeName === "PRE") {
+            addCopyButton(node);
+          }
+        }
+      }
+    }
+  }
+
+  var observer = new MutationObserver(handleNewElements);
+  observer.observe(document.documentElement, {
+    childList: true,
+    subtree: true,
+  });
+
+  document.querySelectorAll("pre").forEach(addCopyButton);
+})();

deepseek_vl/serve/assets/custom.css

@@ -0,0 +1,355 @@
+/**
+ * Copyright (c) 2023-2024 DeepSeek.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy of
+ * this software and associated documentation files (the "Software"), to deal in
+ * the Software without restriction, including without limitation the rights to
+ * use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+ * the Software, and to permit persons to whom the Software is furnished to do so,
+ * subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+ * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+ * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+ * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+ * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+:root {
+  --chatbot-color-light: #f3f3f3;
+  --chatbot-color-dark: #121111;
+}
+
+/* status_display */
+#status_display {
+  display: flex;
+  min-height: 2.5em;
+  align-items: flex-end;
+  justify-content: flex-end;
+}
+#status_display p {
+  font-size: 0.85em;
+  font-family: monospace;
+  color: var(--body-text-color-subdued);
+}
+
+/* usage_display */
+#usage_display {
+  height: 1em;
+}
+#usage_display p {
+  padding: 0 1em;
+  font-size: 0.85em;
+  font-family: monospace;
+  color: var(--body-text-color-subdued);
+}
+/* list */
+ol:not(.options),
+ul:not(.options) {
+  padding-inline-start: 2em !important;
+}
+
+/* Thank @Keldos-Li for fixing it */
+/* Light mode (default) */
+#deepseek_chatbot {
+  background-color: var(--chatbot-color-light) !important;
+  color: #000000 !important;
+}
+[data-testid="bot"] {
+  background-color: #ffffff !important;
+}
+[data-testid="user"] {
+  background-color: #95ec69 !important;
+}
+
+/* Dark mode */
+.dark #deepseek_chatbot {
+  background-color: var(--chatbot-color-dark) !important;
+  color: #ffffff !important;
+}
+.dark [data-testid="bot"] {
+  background-color: #2c2c2c !important;
+}
+.dark [data-testid="user"] {
+  background-color: #26b561 !important;
+}
+
+#deepseek_chatbot {
+  height: 100%;
+  min-height: 800px;
+  flex-grow: 1;
+  overflow: auto;
+}
+
+[class*="message"] {
+  border-radius: var(--radius-xl) !important;
+  border: none;
+  padding: var(--spacing-xl) !important;
+  font-size: var(--text-md) !important;
+  line-height: var(--line-md) !important;
+  min-height: calc(var(--text-md) * var(--line-md) + 2 * var(--spacing-xl));
+  min-width: calc(var(--text-md) * var(--line-md) + 2 * var(--spacing-xl));
+}
+[data-testid="bot"] {
+  max-width: 85%;
+  border-bottom-left-radius: 0 !important;
+}
+[data-testid="user"] {
+  max-width: 85%;
+  width: auto !important;
+  border-bottom-right-radius: 0 !important;
+}
+/* Table */
+table {
+  margin: 1em 0;
+  border-collapse: collapse;
+  empty-cells: show;
+}
+td,
+th {
+  border: 1.2px solid var(--border-color-primary) !important;
+  padding: 0.2em;
+}
+thead {
+  background-color: rgba(175, 184, 193, 0.2);
+}
+thead th {
+  padding: 0.5em 0.2em;
+}
+/* Inline code */
+#deepseek_chatbot code {
+  display: inline;
+  white-space: break-spaces;
+  border-radius: 6px;
+  margin: 0 2px 0 2px;
+  padding: 0.2em 0.4em 0.1em 0.4em;
+  background-color: rgba(175, 184, 193, 0.2);
+}
+/* Code block */
+#deepseek_chatbot pre code {
+  display: block;
+  overflow: auto;
+  white-space: pre;
+  background-color: #1c1d1e !important;
+  border-radius: 10px;
+  padding: 1.4em 1.2em 0em 1.4em;
+  margin: 1.2em 2em 1.2em 0.5em;
+  color: #fdf8f8;
+  box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
+}
+/* Hightlight */
+#deepseek_chatbot .highlight {
+  background-color: transparent;
+}
+#deepseek_chatbot .highlight .hll {
+  background-color: #49483e;
+}
+#deepseek_chatbot .highlight .c {
+  color: #75715e;
+} /* Comment */
+#deepseek_chatbot .highlight .err {
+  color: #960050;
+  background-color: #1e0010;
+} /* Error */
+#deepseek_chatbot .highlight .k {
+  color: #66d9ef;
+} /* Keyword */
+#deepseek_chatbot .highlight .l {
+  color: #ae81ff;
+} /* Literal */
+#deepseek_chatbot .highlight .n {
+  color: #f8f8f2;
+} /* Name */
+#deepseek_chatbot .highlight .o {
+  color: #f92672;
+} /* Operator */
+#deepseek_chatbot .highlight .p {
+  color: #f8f8f2;
+} /* Punctuation */
+#deepseek_chatbot .highlight .ch {
+  color: #75715e;
+} /* Comment.Hashbang */
+#deepseek_chatbot .highlight .cm {
+  color: #75715e;
+} /* Comment.Multiline */
+#deepseek_chatbot .highlight .cp {
+  color: #75715e;
+} /* Comment.Preproc */
+#deepseek_chatbot .highlight .cpf {
+  color: #75715e;
+} /* Comment.PreprocFile */
+#deepseek_chatbot .highlight .c1 {
+  color: #75715e;
+} /* Comment.Single */
+#deepseek_chatbot .highlight .cs {
+  color: #75715e;
+} /* Comment.Special */
+#deepseek_chatbot .highlight .gd {
+  color: #f92672;
+} /* Generic.Deleted */
+#deepseek_chatbot .highlight .ge {
+  font-style: italic;
+} /* Generic.Emph */
+#deepseek_chatbot .highlight .gi {
+  color: #a6e22e;
+} /* Generic.Inserted */
+#deepseek_chatbot .highlight .gs {
+  font-weight: bold;
+} /* Generic.Strong */
+#deepseek_chatbot .highlight .gu {
+  color: #75715e;
+} /* Generic.Subheading */
+#deepseek_chatbot .highlight .kc {
+  color: #66d9ef;
+} /* Keyword.Constant */
+#deepseek_chatbot .highlight .kd {
+  color: #66d9ef;
+} /* Keyword.Declaration */
+#deepseek_chatbot .highlight .kn {
+  color: #f92672;
+} /* Keyword.Namespace */
+#deepseek_chatbot .highlight .kp {
+  color: #66d9ef;
+} /* Keyword.Pseudo */
+#deepseek_chatbot .highlight .kr {
+  color: #66d9ef;
+} /* Keyword.Reserved */
+#deepseek_chatbot .highlight .kt {
+  color: #66d9ef;
+} /* Keyword.Type */
+#deepseek_chatbot .highlight .ld {
+  color: #e6db74;
+} /* Literal.Date */
+#deepseek_chatbot .highlight .m {
+  color: #ae81ff;
+} /* Literal.Number */
+#deepseek_chatbot .highlight .s {
+  color: #e6db74;
+} /* Literal.String */
+#deepseek_chatbot .highlight .na {
+  color: #a6e22e;
+} /* Name.Attribute */
+#deepseek_chatbot .highlight .nb {
+  color: #f8f8f2;
+} /* Name.Builtin */
+#deepseek_chatbot .highlight .nc {
+  color: #a6e22e;
+} /* Name.Class */
+#deepseek_chatbot .highlight .no {
+  color: #66d9ef;
+} /* Name.Constant */
+#deepseek_chatbot .highlight .nd {
+  color: #a6e22e;
+} /* Name.Decorator */
+#deepseek_chatbot .highlight .ni {
+  color: #f8f8f2;
+} /* Name.Entity */
+#deepseek_chatbot .highlight .ne {
+  color: #a6e22e;
+} /* Name.Exception */
+#deepseek_chatbot .highlight .nf {
+  color: #a6e22e;
+} /* Name.Function */
+#deepseek_chatbot .highlight .nl {
+  color: #f8f8f2;
+} /* Name.Label */
+#deepseek_chatbot .highlight .nn {
+  color: #f8f8f2;
+} /* Name.Namespace */
+#deepseek_chatbot .highlight .nx {
+  color: #a6e22e;
+} /* Name.Other */
+#deepseek_chatbot .highlight .py {
+  color: #f8f8f2;
+} /* Name.Property */
+#deepseek_chatbot .highlight .nt {
+  color: #f92672;
+} /* Name.Tag */
+#deepseek_chatbot .highlight .nv {
+  color: #f8f8f2;
+} /* Name.Variable */
+#deepseek_chatbot .highlight .ow {
+  color: #f92672;
+} /* Operator.Word */
+#deepseek_chatbot .highlight .w {
+  color: #f8f8f2;
+} /* Text.Whitespace */
+#deepseek_chatbot .highlight .mb {
+  color: #ae81ff;
+} /* Literal.Number.Bin */
+#deepseek_chatbot .highlight .mf {
+  color: #ae81ff;
+} /* Literal.Number.Float */
+#deepseek_chatbot .highlight .mh {
+  color: #ae81ff;
+} /* Literal.Number.Hex */
+#deepseek_chatbot .highlight .mi {
+  color: #ae81ff;
+} /* Literal.Number.Integer */
+#deepseek_chatbot .highlight .mo {
+  color: #ae81ff;
+} /* Literal.Number.Oct */
+#deepseek_chatbot .highlight .sa {
+  color: #e6db74;
+} /* Literal.String.Affix */
+#deepseek_chatbot .highlight .sb {
+  color: #e6db74;
+} /* Literal.String.Backtick */
+#deepseek_chatbot .highlight .sc {
+  color: #e6db74;
+} /* Literal.String.Char */
+#deepseek_chatbot .highlight .dl {
+  color: #e6db74;
+} /* Literal.String.Delimiter */
+#deepseek_chatbot .highlight .sd {
+  color: #e6db74;
+} /* Literal.String.Doc */
+#deepseek_chatbot .highlight .s2 {
+  color: #e6db74;
+} /* Literal.String.Double */
+#deepseek_chatbot .highlight .se {
+  color: #ae81ff;
+} /* Literal.String.Escape */
+#deepseek_chatbot .highlight .sh {
+  color: #e6db74;
+} /* Literal.String.Heredoc */
+#deepseek_chatbot .highlight .si {
+  color: #e6db74;
+} /* Literal.String.Interpol */
+#deepseek_chatbot .highlight .sx {
+  color: #e6db74;
+} /* Literal.String.Other */
+#deepseek_chatbot .highlight .sr {
+  color: #e6db74;
+} /* Literal.String.Regex */
+#deepseek_chatbot .highlight .s1 {
+  color: #e6db74;
+} /* Literal.String.Single */
+#deepseek_chatbot .highlight .ss {
+  color: #e6db74;
+} /* Literal.String.Symbol */
+#deepseek_chatbot .highlight .bp {
+  color: #f8f8f2;
+} /* Name.Builtin.Pseudo */
+#deepseek_chatbot .highlight .fm {
+  color: #a6e22e;
+} /* Name.Function.Magic */
+#deepseek_chatbot .highlight .vc {
+  color: #f8f8f2;
+} /* Name.Variable.Class */
+#deepseek_chatbot .highlight .vg {
+  color: #f8f8f2;
+} /* Name.Variable.Global */
+#deepseek_chatbot .highlight .vi {
+  color: #f8f8f2;
+} /* Name.Variable.Instance */
+#deepseek_chatbot .highlight .vm {
+  color: #f8f8f2;
+} /* Name.Variable.Magic */
+#deepseek_chatbot .highlight .il {
+  color: #ae81ff;
+} /* Literal.Number.Integer.Long */

deepseek_vl/serve/assets/custom.js

@@ -0,0 +1,22 @@
+/**
+ * Copyright (c) 2023-2024 DeepSeek.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy of
+ * this software and associated documentation files (the "Software"), to deal in
+ * the Software without restriction, including without limitation the rights to
+ * use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+ * the Software, and to permit persons to whom the Software is furnished to do so,
+ * subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+ * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+ * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+ * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+ * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+// custom javascript here

deepseek_vl/serve/inference.py

@@ -0,0 +1,168 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from threading import Thread
+from typing import List
+
+import torch
+import transformers
+from transformers import (
+    AutoModelForCausalLM,
+    StoppingCriteria,
+    StoppingCriteriaList,
+    TextIteratorStreamer,
+)
+
+from deepseek_vl.models import MultiModalityCausalLM, VLChatProcessor
+from deepseek_vl.utils.conversation import Conversation
+
+
+def load_model(model_path):
+    vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
+    tokenizer = vl_chat_processor.tokenizer
+    vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
+        model_path, trust_remote_code=True
+    )
+    vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
+    return tokenizer, vl_gpt, vl_chat_processor
+
+
+def convert_conversation_to_prompts(conversation: Conversation):
+    prompts = []
+    messages = conversation.messages
+
+    for i in range(0, len(messages), 2):
+        prompt = {
+            "role": messages[i][0],
+            "content": messages[i][1][0]
+            if isinstance(messages[i][1], tuple)
+            else messages[i][1],
+            "images": [messages[i][1][1]] if isinstance(messages[i][1], tuple) else [],
+        }
+        response = {"role": messages[i + 1][0], "content": messages[i + 1][1]}
+        prompts.extend([prompt, response])
+
+    return prompts
+
+
+class StoppingCriteriaSub(StoppingCriteria):
+    def __init__(self, stops=[], encounters=1):
+        super().__init__()
+        self.stops = [stop.to("cuda") for stop in stops]
+
+    def __call__(
+        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
+    ):
+        for stop in self.stops:
+            if input_ids.shape[-1] < len(stop):
+                continue
+            if torch.all((stop == input_ids[0][-len(stop) :])).item():
+                return True
+
+        return False
+
+
+@torch.inference_mode()
+def deepseek_generate(
+    prompts: list,
+    vl_gpt: torch.nn.Module,
+    vl_chat_processor,
+    tokenizer: transformers.PreTrainedTokenizer,
+    stop_words: list,
+    max_length: int = 256,
+    temperature: float = 1.0,
+    top_p: float = 1.0,
+    repetition_penalty=1.1,
+):
+    prompts = prompts
+    pil_images = list()
+    for message in prompts:
+        if "images" not in message:
+            continue
+        for pil_img in message["images"]:
+            pil_images.append(pil_img)
+
+    prepare_inputs = vl_chat_processor(
+        conversations=prompts, images=pil_images, force_batchify=True
+    ).to(vl_gpt.device)
+
+    return generate(
+        vl_gpt,
+        tokenizer,
+        prepare_inputs,
+        max_length,
+        temperature,
+        repetition_penalty,
+        top_p,
+        stop_words,
+    )
+
+
+@torch.inference_mode()
+def generate(
+    vl_gpt,
+    tokenizer,
+    prepare_inputs,
+    max_gen_len: int = 256,
+    temperature: float = 0,
+    repetition_penalty=1.1,
+    top_p: float = 0.95,
+    stop_words: List[str] = [],
+):
+    """Stream the text output from the multimodality model with prompt and image inputs."""
+    inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
+
+    streamer = TextIteratorStreamer(tokenizer)
+
+    stop_words_ids = [
+        torch.tensor(tokenizer.encode(stop_word)) for stop_word in stop_words
+    ]
+    stopping_criteria = StoppingCriteriaList(
+        [StoppingCriteriaSub(stops=stop_words_ids)]
+    )
+
+    generation_config = dict(
+        inputs_embeds=inputs_embeds,
+        attention_mask=prepare_inputs.attention_mask,
+        pad_token_id=tokenizer.eos_token_id,
+        bos_token_id=tokenizer.bos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        max_new_tokens=max_gen_len,
+        do_sample=True,
+        use_cache=True,
+        streamer=streamer,
+        stopping_criteria=stopping_criteria,
+    )
+
+    if temperature > 0:
+        generation_config.update(
+            {
+                "do_sample": True,
+                "top_p": top_p,
+                "temperature": temperature,
+                "repetition_penalty": repetition_penalty,
+            }
+        )
+    else:
+        generation_config["do_sample"] = False
+
+    thread = Thread(target=vl_gpt.language_model.generate, kwargs=generation_config)
+    thread.start()
+
+    yield from streamer

inference.py

@@ -1,37 +1,52 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
 import torch
 from transformers import AutoModelForCausalLM
 
-from deepseek_vl.models import VLChatProcessor, MultiModalityCausalLM
+from deepseek_vl.models import MultiModalityCausalLM, VLChatProcessor
 from deepseek_vl.utils.io import load_pil_images
 
-
 # specify the path to the model
 model_path = "deepseek-ai/deepseek-vl-7b-chat"
 vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
 tokenizer = vl_chat_processor.tokenizer
 
-vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
+vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
+    model_path, trust_remote_code=True
+)
 vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
 
 conversation = [
     {
         "role": "User",
         "content": "<image_placeholder>Describe each stage of this image.",
-        "images": ["./images/training_pipelines.jpg"]
+        "images": ["./images/training_pipelines.jpg"],
     },
-    {
+    {"role": "Assistant", "content": ""},
-        "role": "Assistant",
-        "content": ""
-    }
 ]
 
 
 # load images and prepare for inputs
 pil_images = load_pil_images(conversation)
 prepare_inputs = vl_chat_processor(
-    conversations=conversation,
+    conversations=conversation, images=pil_images, force_batchify=True
-    images=pil_images,
-    force_batchify=True
 ).to(vl_gpt.device)
 
 # run image encoder to get the image embeddings
@@ -46,7 +61,7 @@ outputs = vl_gpt.language_model.generate(
     eos_token_id=tokenizer.eos_token_id,
     max_new_tokens=512,
     do_sample=False,
-    use_cache=True
+    use_cache=True,
 )
 
 answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)

pyproject.toml

@@ -10,17 +10,30 @@ authors = [{name = "DeepSeek-AI"}]
 license = {file = "LICENSE-CODE"}
 urls = {homepage = "https://github.com/deepseek-ai/DeepSeek-VL"}
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.8, <3.10"
 dependencies = [
     "torch>=2.0.1",
     "transformers>=4.38.2",
     "timm>=0.9.16",
-    "gradio>=4.13.0",
     "accelerate",
     "sentencepiece",
     "attrdict",
     "einops",
 ]
 
+[project.optional-dependencies]
+gradio = [
+    "gradio==3.48.0",
+    "gradio-client==0.6.1",
+    "mdtex2html==1.3.0",
+    "pypinyin==0.50.0",
+    "tiktoken==0.5.2",
+    "tqdm==4.64.0",
+    "colorama==0.4.5",
+    "Pygments==2.12.0",
+    "markdown==3.4.1",
+    "SentencePiece==0.1.96"
+]
+
 [tool.setuptools]
 packages = {find = {exclude = ["images"]}}

requirements.txt

@@ -1,8 +1,19 @@
-torch>=2.0.1
+torch==2.0.1
 transformers>=4.38.2
 timm>=0.9.16
-gradio>=4.13.0
 accelerate
 sentencepiece
 attrdict
 einops
+
+# for gradio demo
+gradio==3.48.0
+gradio-client==0.6.1
+mdtex2html==1.3.0
+pypinyin==0.50.0
+tiktoken==0.5.2
+tqdm==4.64.0
+colorama==0.4.5
+Pygments==2.12.0
+markdown==3.4.1
+SentencePiece==0.1.96