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import gradio as gr
from utils.upload_file import UploadFile
from utils.chatbot import ChatBot
from utils.ui_settings import UISettings
from utils.load_config import LoadConfig
APPCFG = LoadConfig()
# # Prepare the LLm and Tokenizer
# tokenizer = AutoTokenizer.from_pretrained(
# APPCFG.llm_engine, token=APPCFG.gemma_token, device=APPCFG.device)
# model = model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path="google/gemma-7b-it",
# token=APPCFG.gemma_token,
# torch_dtype=torch.float16,
# device_map=APPCFG.device
# )
# app_pipeline = pipeline(
# "text-generation",
# model=model,
# tokenizer=tokenizer
# )
with gr.Blocks() as demo:
with gr.Tabs():
with gr.TabItem("Med-App"):
##############
# First ROW:
##############
with gr.Row() as row_one:
with gr.Column(visible=False) as reference_bar:
ref_output = gr.Markdown()
with gr.Column() as chatbot_output:
chatbot = gr.Chatbot(
[],
elem_id="chatbot",
bubble_full_width=False,
height=500,
avatar_images=(
("images/test.png"), "images/Gemma-logo.png"),
# render=False
)
# **Adding like/dislike icons
chatbot.like(UISettings.feedback, None, None)
##############
# SECOND ROW:
##############
with gr.Row():
input_txt = gr.Textbox(
lines=4,
scale=8,
placeholder="Enter text and press enter, or upload PDF files",
container=False,
)
##############
# Third ROW:
##############
with gr.Row() as row_two:
text_submit_btn = gr.Button(value="Submit text")
sidebar_state = gr.State(False)
btn_toggle_sidebar = gr.Button(
value="References")
btn_toggle_sidebar.click(UISettings.toggle_sidebar, [sidebar_state], [
reference_bar, sidebar_state])
upload_btn = gr.UploadButton(
"📁 Upload PDF or doc files", file_types=[
'.pdf',
'.doc'
],
file_count="multiple")
clear_button = gr.ClearButton([input_txt, chatbot])
rag_with_dropdown = gr.Dropdown(
label="RAG with", choices=["Preprocessed doc", "Upload doc: Process for RAG"], value="Preprocessed doc")
##############
# Fourth ROW:
##############
with gr.Row() as row_four:
temperature_bar = gr.Slider(minimum=0.1, maximum=1, value=0.1, step=0.1,
label="Temperature", info="Increasing the temperature will make the model answer more creatively.")
top_k = gr.Slider(minimum=0.0,
maximum=100.0,
step=1,
label="top_k",
value=50,
info="A lower value (e.g. 10) will result in more conservative answers.")
top_p = gr.Slider(minimum=0.0,
maximum=1.0,
step=0.01,
label="top_p",
value=0.95,
info=" Works together with top-k. lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.0)")
##############
# Process:
##############
file_msg = upload_btn.upload(fn=UploadFile.process_uploaded_files, inputs=[
upload_btn, chatbot, rag_with_dropdown], outputs=[input_txt, chatbot], queue=False)
txt_msg = input_txt.submit(fn=ChatBot.respond,
inputs=[chatbot, input_txt,
rag_with_dropdown, temperature_bar, top_k, top_p],
outputs=[input_txt,
chatbot, ref_output],
queue=False).then(lambda: gr.Textbox(interactive=True),
None, [input_txt], queue=False)
txt_msg = text_submit_btn.click(fn=ChatBot.respond,
inputs=[chatbot, input_txt,
rag_with_dropdown, temperature_bar, top_k, top_p],
outputs=[input_txt,
chatbot, ref_output],
queue=False).then(lambda: gr.Textbox(interactive=True),
None, [input_txt], queue=False)
if __name__ == "__main__":
demo.launch()
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