Agent
Tips before using Agent:
Please click clear🗑️
(top-right) to remove previous context before sumbmitting a new question.
Click retry🔄 (below message) to get multiple versions of the answer.
"""
css = """
h1 {
text-align: center;
display: block;
}
#duplicate-button {
margin: auto;
color: white;
background: #1565c0;
border-radius: 100vh;
}
.small-button button {
font-size: 12px !important;
padding: 4px 8px !important;
height: 6px !important;
width: 4px !important;
}
.gradio-accordion {
margin-top: 0px !important;
margin-bottom: 0px !important;
}
"""
chat_css = """
.gr-button { font-size: 20px !important; } /* Enlarges button icons */
.gr-button svg { width: 32px !important; height: 32px !important; } /* Enlarges SVG icons */
"""
model_name = ''
os.environ["TOKENIZERS_PARALLELISM"] = "false"
question_examples = [
# ['What is the IC50 values for the drug Abemaciclib in the cell line A549?'],
["What's the MoA of the drug Ponatinib on the HCT15 colon cancer cell line? Please synthesize results from the Tahoe-100M dataset, the jump dataset, and the IC50 dataset."],
["Natural perturbation: find the disease perturbation that has the similar effect to Glycyrrhizic acid on CVCL_0334? use the result and what you know to explain the mechanism of action."],
["Mechanism of action: give me the mechanism of action for drug name Abemaciclib provided by Tahoe."],
["Vision scores: what are the top 5 vision scores for cell line A549 and drug name Abemaciclib"]
]
new_tool_files = {
'new_tool': os.path.join(current_dir, 'data', 'new_tool.json'),
}
config_path = "/home/ubuntu/.lambda_api_config.yaml"
agent = SigSpace(config_path)
# agent.init_model()
def update_model_parameters(enable_finish, enable_rag, enable_summary,
init_rag_num, step_rag_num, skip_last_k,
summary_mode, summary_skip_last_k, summary_context_length, force_finish, seed):
# Update model instance parameters dynamically
updated_params = agent.update_parameters(
enable_finish=enable_finish,
enable_rag=enable_rag,
enable_summary=enable_summary,
init_rag_num=init_rag_num,
step_rag_num=step_rag_num,
skip_last_k=skip_last_k,
summary_mode=summary_mode,
summary_skip_last_k=summary_skip_last_k,
summary_context_length=summary_context_length,
force_finish=force_finish,
seed=seed,
)
return updated_params
def update_seed():
# Update model instance parameters dynamically
seed = random.randint(0, 10000)
updated_params = agent.update_parameters(
seed=seed,
)
return updated_params
def handle_retry(history, retry_data: gr.RetryData, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round):
print("Updated seed:", update_seed())
new_history = history[:retry_data.index]
previous_prompt = history[retry_data.index]['content']
print("previous_prompt", previous_prompt)
yield from agent.run_gradio_chat(new_history + [{"role": "user", "content": previous_prompt}], temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round)
PASSWORD = "mypassword"
# Function to check if the password is correct
def check_password(input_password):
if input_password == PASSWORD:
return gr.update(visible=True), ""
else:
return gr.update(visible=False), "Incorrect password, try again!"
conversation_state = gr.State([])
# Gradio block
chatbot = gr.Chatbot(height=400, placeholder=PLACEHOLDER,
label='SigSpace', type="messages", show_copy_button=True)
with gr.Blocks(css=css) as demo:
gr.Markdown(DESCRIPTION)
# gr.Markdown(INTRO)
gr.Image(value=display_image(img_path), label="", show_label=False, height=600, width=600)
default_temperature = 0.3
default_max_new_tokens = 1024
default_max_tokens = 81920
default_max_round = 30
temperature_state = gr.State(value=default_temperature)
max_new_tokens_state = gr.State(value=default_max_new_tokens)
max_tokens_state = gr.State(value=default_max_tokens)
max_round_state = gr.State(value=default_max_round)
chatbot.retry(handle_retry, chatbot, chatbot, temperature_state, max_new_tokens_state,
max_tokens_state, gr.Checkbox(value=False, render=False), conversation_state, max_round_state)
gr.ChatInterface(
fn=agent.run_gradio_chat,
chatbot=chatbot,
fill_height=False, fill_width=False, stop_btn=True,
additional_inputs_accordion=gr.Accordion(
label="⚙️ Inference Parameters", open=False, render=False),
additional_inputs=[
temperature_state, max_new_tokens_state, max_tokens_state,
gr.Checkbox(
label="Activate X", value=False, render=False),
conversation_state,
max_round_state,
gr.Number(label="Seed", value=100, render=False)
],
examples=question_examples,
cache_examples=False,
css=chat_css,
)
with gr.Accordion("Settings", open=False):
# Define the sliders
temperature_slider = gr.Slider(
minimum=0,
maximum=1,
step=0.1,
value=default_temperature,
label="Temperature"
)
max_new_tokens_slider = gr.Slider(
minimum=128,
maximum=4096,
step=1,
value=default_max_new_tokens,
label="Max new tokens"
)
max_tokens_slider = gr.Slider(
minimum=128,
maximum=32000,
step=1,
value=default_max_tokens,
label="Max tokens"
)
max_round_slider = gr.Slider(
minimum=0,
maximum=50,
step=1,
value=default_max_round,
label="Max round")
# Automatically update states when slider values change
temperature_slider.change(
lambda x: x, inputs=temperature_slider, outputs=temperature_state)
max_new_tokens_slider.change(
lambda x: x, inputs=max_new_tokens_slider, outputs=max_new_tokens_state)
max_tokens_slider.change(
lambda x: x, inputs=max_tokens_slider, outputs=max_tokens_state)
max_round_slider.change(
lambda x: x, inputs=max_round_slider, outputs=max_round_state)
# password_input = gr.Textbox(
# label="Enter Password for More Settings", type="password")
# incorrect_message = gr.Textbox(visible=False, interactive=False)
# with gr.Accordion("⚙️ Settings", open=False, visible=False) as protected_accordion:
# with gr.Row():
# with gr.Column(scale=1):
# with gr.Accordion("⚙️ Model Loading", open=False):
# model_name_input = gr.Textbox(
# label="Enter model path", value=model_name)
# load_model_btn = gr.Button(value="Load Model")
# load_model_btn.click(
# agent.load_models, inputs=model_name_input, outputs=gr.Textbox(label="Status"))
# with gr.Column(scale=1):
# with gr.Accordion("⚙️ Functional Parameters", open=False):
# # Create Gradio components for parameter inputs
# enable_finish = gr.Checkbox(
# label="Enable Finish", value=True)
# enable_rag = gr.Checkbox(
# label="Enable RAG", value=True)
# enable_summary = gr.Checkbox(
# label="Enable Summary", value=False)
# init_rag_num = gr.Number(
# label="Initial RAG Num", value=0)
# step_rag_num = gr.Number(
# label="Step RAG Num", value=10)
# skip_last_k = gr.Number(label="Skip Last K", value=0)
# summary_mode = gr.Textbox(
# label="Summary Mode", value='step')
# summary_skip_last_k = gr.Number(
# label="Summary Skip Last K", value=0)
# summary_context_length = gr.Number(
# label="Summary Context Length", value=None)
# force_finish = gr.Checkbox(
# label="Force FinalAnswer", value=True)
# seed = gr.Number(label="Seed", value=100)
# # Button to submit and update parameters
# submit_btn = gr.Button("Update Parameters")
# # Display the updated parameters
# updated_parameters_output = gr.JSON()
# # When button is clicked, update parameters
# submit_btn.click(fn=update_model_parameters,
# inputs=[enable_finish, enable_rag, enable_summary, init_rag_num, step_rag_num, skip_last_k,
# summary_mode, summary_skip_last_k, summary_context_length, force_finish, seed],
# outputs=updated_parameters_output)
# Button to submit the password
# submit_button = gr.Button("Submit")
# # When the button is clicked, check if the password is correct
# submit_button.click(
# check_password,
# inputs=password_input,
# outputs=[protected_accordion, incorrect_message]
# )
gr.Markdown(LICENSE)
if __name__ == "__main__":
demo.launch(share=True)