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import gradio as gr
import numpy as np
import random
import time
import os
import shutil
import codecs
# How to RUN code ==> gradio gradio_llm_example.py
# Define text and title information
title1 = "## </br> </br> </br> 🤗💬 QA App"
title2 = " ## </br> </br> </br> Gradio QA Bot"
intro = """ Welcome! This is not just any bot, it's a special one equipped with state-of-the-art natural language processing capabilities, and ready to answer your queries.
Ready to explore? Let's get started!
* Step 1: Upload a PDF document.
* Step 2: Type in a question related to your document's content.
* Step 3: Get your answer!
Push clear cache before uploading a new doc!
"""
about = """
## </br> About
This app is an LLM-powered chatbot built using:
- [Streamlit](<https://streamlit.io/>)
- [HugChat](<https://github.com/Soulter/hugging-chat-api>)
- Chat Model = llama2-chat-hf 7B
- Retreiver model = all-MiniLM-L6-v2
</br>
💡 Note: No API key required!
</br>
Made with ❤️ by us
"""
# Define theme ==> see gr.themes.builder()
theme = gr.themes.Soft(
primary_hue="emerald",
secondary_hue="emerald",
neutral_hue="slate",
).set(
body_background_fill_dark='*primary_50',
shadow_drop='*shadow_spread',
button_border_width='*block_border_width',
button_border_width_dark='*block_label_border_width'
)
def upload_file(files_obj):
""" Upload several files from drag and drop, and save them in local temp folder
files_obj (type:list) : list of tempfile._TemporaryFileWrapper
return checkbox to display uploaded documents """
# Create local copy
temp_file_path = "./temp"
if not os.path.exists(temp_file_path):
os.makedirs(temp_file_path)
# Save each file among list of given files
file_name_list = list()
for file_obj in files_obj :
file_name = os.path.basename(file_obj.name)
file_name_list.append(file_name)
shutil.copyfile(file_obj.name, os.path.join(temp_file_path, file_name))
return {uploaded_check : gr.Radio(choices=file_name_list, visible=True),
choose_btn : gr.Button(value="Choose", visible=True)}
def read_content(file_name):
print(file_name, type(file_name))
temp_file_path = "./temp"
file_path = os.path.join(temp_file_path, file_name)
with open(file_path, "rb") as file:
try:
content = file.read()
print(content)
print(codecs.decode(content, 'utf-8'))
return {error_box: gr.Textbox(value=f"File ready to be used. \n You can ask a question about the uploaded PDF document.", visible=True)}
except Exception as e:
print(f"Error occurred while writing the file: {e}")
return {error_box: gr.Textbox(value=f"Error occurred while writing the file: {e}", visible=True)}
def respond(message, chat_history,
language_choice, max_length, temperature,
num_return_sequences, top_p, no_repeat_ngram_size):
#No LLM here, just respond with a random pre-made message
if content == "":
bot_message = f"j'ai {max_length}" + random.choice(["Tell me more about it",
"Cool, but I'm not interested",
"Hmmmm, ok then"])
else:
bot_message = " j'ai besoin d'un modèle pour lire le {content[:3]}"
chat_history.append((message, bot_message))
return "", chat_history
# Layout
with gr.Blocks(theme=gr.themes.Soft()) as gradioApp:
with gr.Row():
with gr.Column(scale=1, min_width=100):
logo_gr = gr.Markdown(""" <img src="file/logo_neovision.png" alt="logo" style="width:400px;"/>""")
# gr.Image("./logo_neovision.png")
about_gr = gr.Markdown(about)
with gr.Column(scale=2, min_width=500):
title1_gr= gr.Markdown(title1)
intro_gr = gr.Markdown(intro)
# Upload several documents
content = ""
upload_button = gr.UploadButton("Browse files", label="Drag and drop your documents here",
size="lg", scale=0, min_width=100,
file_types=["pdf"], file_count="multiple")
uploaded_check = gr.Radio(label="Uploaded documents", visible=False,
info="Do you want to use a supporting document?")
choose_btn = gr.Button(value="Choose", visible=False)
upload_button.upload(upload_file, upload_button, [uploaded_check, choose_btn])
# Read only one document
error_box = gr.Textbox(label="Reading files... ", visible=False)
choose_btn.click(read_content, inputs=uploaded_check, outputs=error_box)
# Select advanced options
gr.Markdown(""" ## Toolbox """)
with gr.Accordion(label="Select advanced options",open=False):
language_choice = gr.Dropdown(["English", "French"], label="Language", info="Choose your language")
max_length = gr.Slider(label="Token length", minimum=1, maximum=100, value=50, step=1)
temperature= gr.Slider(label="Temperature", minimum=0.1, maximum=1, value=0.8, step=0.1)
num_return_sequences= gr.Slider(label="Temperature", minimum=0.1, maximum=50, value=1, step=0.1)
top_p= gr.Slider(label="Temperature", minimum=0.1, maximum=1, value=0.8, step=0.1)
no_repeat_ngram_size= gr.Slider(label="Temperature", minimum=0.1, maximum=1, value=3, step=0.1)
# Chat
with gr.Column(scale=2, min_width=600):
title2_gr = gr.Markdown(title2)
chatbot = gr.Chatbot(label="Bot", height=500)
msg = gr.Textbox(label="User", placeholder="Ask any question.")
chatbot_btn = gr.Button("Submit")
chatbot_btn.click(respond, inputs=[msg, chatbot,
language_choice, max_length, temperature,
num_return_sequences, top_p, no_repeat_ngram_size],
outputs=[msg, chatbot])
clear = gr.ClearButton(components=[msg, chatbot], value="Clear console")
gr.close_all()
gradioApp.launch(share=True, enable_queue=True)
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