import gradio as gr
from utils import *
from chains.openai_model import OpenAIModel
from config import SEVER, PORT, DEBUG, DEPLOYMENT_ID, SAVE_DIR
from vector_db import delete_all, delete_file, handle_upload_file, update_file
# Get and load new model
def get_model(llm_model_name, temperature=0., top_p=1.0):
model = OpenAIModel(llm_model_name=llm_model_name,
condense_model_name=llm_model_name, temperature=temperature, top_p=top_p)
return model
def create_new_model():
return get_model(llm_model_name=DEPLOYMENT_ID)
def update_database(files_src):
message = handle_upload_file(files_src)
saved_file = os.listdir(SAVE_DIR)
# saved_file = os.listdir(SAVE_DIR)
# if files_src is None:
# return gr.update(choices=[])
# for file in files_src:
# file_path = file.name
# file_name = os.path.basename(file_path)
# if file_name in os.listdir(SAVE_DIR):
# continue
# else:
# saved_file.extend([file_name])
return gr.update(choices=saved_file), message
# Gradio app
title = """Docs FPT ๐ค
"""
with gr.Blocks() as demo:
user_name = gr.State("")
history = gr.State([])
current_model = gr.State(create_new_model)
with gr.Row():
with gr.Column(scale=1):
gr.HTML(title)
status_text = ""
status_display = gr.Markdown(status_text, elem_id="status_display")
with gr.Row().style(equal_height=True):
with gr.Column(scale=5):
with gr.Row():
chatbot = gr.Chatbot([], elem_id="chatbot").style(height="100%")
with gr.Row():
with gr.Column(min_width=225, scale=12):
user_input = gr.Textbox(
show_label=False, placeholder="Enter here"
).style(container=False)
# ask_examples_hidden = gr.Textbox(elem_id="hidden-message")
examples_questions = gr.Examples(
[
"What are the possible ranks of Fsoft projects?",
"What is the rank of a project that has budgeted MM 100 man-month?",
"What is the definition of a rank A project?",
"ๆ
ๅ ฑใปใญใฅใชใใฃใซ้ขใใไบๆ
ใ่ตทใใๅ ดๅใ่ชฐใซ้ๅ ฑใใใฐใใใงใใ",
"ไบๆ
ใใผใฟใใผในใฏใฉใฎใใใใฎๆ้ไฟๅญใใใพใใ?",
"ใปใญใฅใชใใฃไบๆ
ใ้ๅคงใใฉใใใฏใฉใใใใฐใใใใพใใ?"
],
[user_input],
examples_per_page=6,
)
with gr.Column(min_width=42, scale=1):
submitBtn = gr.Button("Send", variant="primary")
with gr.Row():
emptyBtn = gr.Button(
"๐งน New conversation", elem_id="empty_btn")
retryBtn = gr.Button("๐ Retry")
rec = gr.Button("โบ๏ธRecord")
record_audio = gr.inputs.Audio(source="microphone", type="filepath")
with gr.Row():
gr.Markdown(
"""
## ๐ป Key Feature
- Chat with an AI chatbot powered by OpenAI's chat API, using the **content of your research document**.
- Get **semantic search** answers from your document using **vector databases**.
- Perform a **Google search** within the app
- **Verify sources** for all generated results.
- Support converting **speech to text** for easy input.
### Pine cone
Pinecone makes it easy to provide long-term memory for high-performance AI applications.
It's a managed, cloud-native vector database with a simple API and no infrastructure hassles. Pinecone serves fresh, filtered query results with low latency at the scale of billions of vectors.
https://www.pinecone.io/blog/azure/
### Azure OpenAI Service
https://learn.microsoft.com/en-us/legal/cognitive-services/openai/data-privacy
## ๐ง Contact
This tool has been developed by the R&D lab at **QAI** (FPT Software, Ha Noi, Viet Nam)
If you have any questions or feature requests, please feel free to reach us out at khangnvt1@fpt.com.
"""
)
with gr.Column(min_width=50, scale=1.5):
with gr.Tab(label="ChatGPT"):
# gr.Markdown(f'Azure OpenAI Service:here
')
index_files = gr.Files(label="Files", type="file", multiple=True)
use_websearch = gr.Checkbox(label="Google search", value=False, elem_classes="switch_checkbox")
custom_websearch = gr.Checkbox(label="Custom web search", value=False, elem_classes="switch_checkbox")
with gr.Tab(label="Configuration"):
gr.Markdown(
"โ ๏ธBe careful to change โ ๏ธ\n\nIf you can't use it, please restore the default settings")
with gr.Accordion("Parameter", open=False):
temperature_slider = gr.Slider(
minimum=-0,
maximum=1.0,
value=0.0,
step=0.1,
interactive=True,
label="Temperature",
)
top_p_slider = gr.Slider(
minimum=-0,
maximum=1.0,
value=1.0,
step=0.1,
interactive=True,
label="Top_p",
)
user_identifier = gr.Textbox(
show_label=True,
placeholder="Enter here",
label="User name",
value=user_name.value,
lines=1,
)
loadHistoryBtn = gr.Button("๐พ Load History")
with gr.Tab(label="Knowledge DB"):
all_files = gr.Dropdown(
label="All available files:", multiselect=True, choices=os.listdir(SAVE_DIR), interactive=True
)
with gr.Column():
delete_btn = gr.Button("๐๏ธ Delete")
with gr.Column():
delete_all_btn = gr.Button("๐๏ธ Delete all")
update_btn = gr.Button("๐๏ธ Update DB")
index_files.change(update_database, [index_files], [all_files, status_display])
delete_all_btn.click(delete_all, None, [all_files, status_display, index_files])
delete_btn.click(delete_file, [all_files], [all_files, status_display, index_files])
update_btn.click(update_file, None, [status_display])
emptyBtn.click(
reset,
inputs=[current_model],
outputs=[chatbot],
show_progress=True,
)
retryBtn.click(retry, [chatbot, current_model, use_websearch, custom_websearch], [chatbot])
loadHistoryBtn.click(load_chat_history, [current_model], [chatbot])
rec.click(transcribe, [current_model, record_audio], [user_input])
user_identifier.change(set_user_indentifier, [current_model, user_identifier], None)
user_input.submit(predict, [chatbot, current_model, user_input, use_websearch, custom_websearch], [chatbot, status_display], show_progress=True)
user_input.submit(lambda: "", None, user_input)
submitBtn.click(predict, [chatbot, current_model, user_input, use_websearch, custom_websearch], [chatbot, status_display], show_progress=True)
submitBtn.click(lambda: "", None, user_input)
demo.queue(concurrency_count=10).launch(
server_name=SEVER, server_port=PORT, debug=DEBUG)