DeepSeek-VL-7B / app.py
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# 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 spaces
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 inference import (
convert_conversation_to_prompts,
deepseek_generate,
load_model,
)
from app_modules.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
@spaces.GPU
@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("assets/custom.css", "r", encoding="utf-8") as f:
customCSS = f.read()
with gr.Blocks(theme=gr.themes.Soft(spacing_size="md")) 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 = [
[
"examples/rap.jpeg",
"Can you write me a master rap song that rhymes very well based on this image?",
],
[
"examples/app.png",
"What is this app about?",
],
[
"examples/pipeline.png",
"Help me write a python code based on the image.",
],
[
"examples/chart.png",
"Could you help me to re-draw this picture with python codes?",
],
[
"examples/mirror.png",
"How many people are there in the image. Why?",
],
[
"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(max_size=20).launch(
share=False,
favicon_path="assets/favicon.ico",
)