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import gradio as gr
from langchain_core.messages import HumanMessage, AIMessage
from llm import DeepSeekLLM, OpenRouterLLM
from config import settings
deep_seek_llm = DeepSeekLLM(api_key=settings.deep_seek_api_key)
open_router_llm = OpenRouterLLM(api_key=settings.open_router_api_key)
def init_chat():
return deep_seek_llm.get_chat_engine()
def predict(message, history, chat):
if chat is None:
chat = init_chat()
history_messages = []
for human, assistant in history:
history_messages.append(HumanMessage(content=human))
history_messages.append(AIMessage(content=assistant))
history_messages.append(HumanMessage(content=message.text))
response_message = ''
for chunk in chat.stream(history_messages):
response_message = response_message + chunk.content
yield response_message
def update_chat(_provider: str, _chat, _model: str, _temperature: float, _max_tokens: int):
print('?????', _provider, _chat, _model, _temperature, _max_tokens)
if _provider == 'DeepSeek':
_chat = deep_seek_llm.get_chat_engine(model=_model, temperature=_temperature, max_tokens=_max_tokens)
if _provider == 'OpenRouter':
_chat = open_router_llm.get_chat_engine(model=_model, temperature=_temperature, max_tokens=_max_tokens)
return _chat
with gr.Blocks() as app:
with gr.Tab('聊天'):
chat_engine = gr.State(value=None)
with gr.Row():
with gr.Column(scale=2, min_width=600):
chatbot = gr.ChatInterface(
predict,
multimodal=True,
chatbot=gr.Chatbot(elem_id="chatbot", height=600, show_share_button=False),
textbox=gr.MultimodalTextbox(lines=1),
additional_inputs=[chat_engine]
)
with gr.Column(scale=1, min_width=300):
with gr.Accordion('Select Model', open=True):
with gr.Column():
provider = gr.Dropdown(label='Provider', choices=['DeepSeek', 'OpenRouter'], value='DeepSeek')
@gr.render(inputs=provider)
def show_model_config_panel(_provider):
if _provider == 'DeepSeek':
with gr.Column():
model = gr.Dropdown(
label='模型',
choices=deep_seek_llm.support_models,
value=deep_seek_llm.default_model
)
temperature = gr.Slider(
minimum=0.0,
maximum=1.0,
step=0.1,
value=deep_seek_llm.default_temperature,
label="Temperature",
key="temperature",
)
max_tokens = gr.Number(
minimum=1024,
maximum=1024 * 20,
step=128,
value=deep_seek_llm.default_max_tokens,
label="Max Tokens",
key="max_tokens",
)
model.change(
fn=update_chat,
inputs=[provider, chat_engine, model, temperature, max_tokens],
outputs=[chat_engine],
)
temperature.change(
fn=update_chat,
inputs=[provider, chat_engine, model, temperature, max_tokens],
outputs=[chat_engine],
)
max_tokens.change(
fn=update_chat,
inputs=[provider, chat_engine, model, temperature, max_tokens],
outputs=[chat_engine],
)
if _provider == 'OpenRouter':
with gr.Column():
model = gr.Dropdown(
label='模型',
choices=open_router_llm.support_models,
value=open_router_llm.default_model
)
temperature = gr.Slider(
minimum=0.0,
maximum=1.0,
step=0.1,
value=open_router_llm.default_temperature,
label="Temperature",
key="temperature",
)
max_tokens = gr.Number(
minimum=1024,
maximum=1024 * 20,
step=128,
value=open_router_llm.default_max_tokens,
label="Max Tokens",
key="max_tokens",
)
model.change(
fn=update_chat,
inputs=[provider, chat_engine, model, temperature, max_tokens],
outputs=[chat_engine],
)
temperature.change(
fn=update_chat,
inputs=[provider, chat_engine, model, temperature, max_tokens],
outputs=[chat_engine],
)
max_tokens.change(
fn=update_chat,
inputs=[provider, chat_engine, model, temperature, max_tokens],
outputs=[chat_engine],
)
with gr.Tab('画图'):
with gr.Row():
with gr.Column(scale=2, min_width=600):
gr.Image(label="Input Image")
with gr.Column(scale=1, min_width=300):
gr.Textbox(label="LoRA")
app.launch(debug=settings.debug, show_api=False)
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