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import gradio as gr | |
from scrape_3gpp import * | |
from excel_chat import * | |
from classification import * | |
from chart_generation import * | |
from charts_advanced import * | |
from users_management import * | |
from code_df_custom import * | |
# Categories | |
categories = [ | |
{ | |
"topic": "Confidentiality and Privacy Protection", | |
"description": "This topic covers the protection of confidentiality, privacy, and integrity in security systems. It also includes authentication and authorization processes.", | |
"experts": ["Mireille"] | |
}, | |
{ | |
"topic": "Distributed Trust and End-User Trust Models", | |
"description": "This topic focuses on distributed trust models and how end-users establish trust in secure systems.", | |
"experts": ["Mireille", "Khawla"] | |
}, | |
{ | |
"topic": "Secure Element and Key Provisioning", | |
"description": "This topic involves the secure element in systems and the process of key provisioning.", | |
"experts": ["Mireille"] | |
}, | |
{ | |
"topic": "Residential Gateway Security", | |
"description": "This topic covers the security aspects of Residential Gateways.", | |
"experts": ["Mireille"] | |
}, | |
{ | |
"topic": "Standalone Non-Public Network (SNPN) Inter-Connection and Cybersecurity", | |
"description": "This topic focuses on the inter-connection of Standalone Non-Public Networks and related cyber-security topics.", | |
"experts": ["Khawla"] | |
}, | |
{ | |
"topic": "Distributed Ledger and Blockchain in SNPN", | |
"description": "This topic covers the use of distributed ledger technology and blockchain in securing Standalone Non-Public Networks.", | |
"experts": ["Khawla"] | |
}, | |
{ | |
"topic": "Distributed Networks and Communication", | |
"description": "This topic involves distributed networks such as mesh networks, ad-hoc networks, and multi-hop networks, and their cyber-security aspects.", | |
"experts": ["Guillaume"] | |
}, | |
{ | |
"topic": "Swarm of Drones and Unmanned Aerial Vehicles Network Infrastructure", | |
"description": "This topic covers the network infrastructure deployed by Swarm of Drones and Unmanned Aerial Vehicles.", | |
"experts": ["Guillaume"] | |
}, | |
{ | |
"topic": "USIM and Over-the-Air Services", | |
"description": "This topic involves USIM and related over-the-air services such as Steering of Roaming, roaming services, network selection, and UE configuration.", | |
"experts": ["Vincent"] | |
}, | |
{ | |
"topic": "Eco-Design and Societal Impact of Technology", | |
"description": "This topic covers eco-design concepts, including energy saving, energy efficiency, carbon emissions, and the societal impact of technology.", | |
"experts": ["Pierre"] | |
}, | |
{ | |
"topic": "Service Requirements of New Services", | |
"description": "This topic involves defining service requirements for new services, detecting low signals of new trends and technologies, and assessing their impact on USIM services or over-the-air services.", | |
"experts": ["Ly-Thanh"] | |
}, | |
{ | |
"topic": "Satellite and Non Terrestrial Networks", | |
"description": "This topic covers satellite networks, Non Terrestrial Networks, Private Networks, IoT, Inter Satellite communication, and Radio Access Network.", | |
"experts": ["Nicolas"] | |
}, | |
{ | |
"topic": "Public Safety and Emergency Communication", | |
"description": "This topic involves Public Safety Communication, Military Communication, Emergency Calls, Emergency Services, Disaster Communication Access, and other related areas.", | |
"experts": ["Dorin"] | |
} | |
] | |
df_cate = pd.DataFrame(categories) | |
# def update_label(label1): | |
# return gr.update(choices=list(df.columns)) | |
with gr.Blocks() as demo: | |
with gr.Row(): | |
with gr.Column(): | |
gr.Markdown("## Extaction, Classification and AI tool") | |
with gr.Column(): | |
md_username = gr.Markdown(value='## Hi Guest!') | |
btn_logout = gr.Button("Logout") | |
with gr.Accordion(label="**Login** to keep user preferences", open=False): | |
st_user = gr.State(value={"name":"Guest", "hashed_password":"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "history": { "keywords": [ "value1", "value3", "value4"], "prompts": [] }}) | |
with gr.Column(): | |
tb_user = gr.Textbox(label='Username') | |
tb_pwd = gr.Textbox(label='Password', type='password') | |
with gr.Row(): | |
btn_login = gr.Button('Login') | |
with gr.Tab("File extraction"): | |
gr.Markdown("### This part aims to extract the most relevant content and information about every contribution from a 3gpp meeting") | |
gr.Markdown(" Put either just a link, or a link and an excel file with an 'Actions' column") | |
with gr.Row(): | |
dd_url = gr.Dropdown(label="(e.g. https://www.3gpp.org/ftp/TSG_SA/WG1_Serv/TSGS1_105_Athens/Docs)", multiselect=False, value="https://www.3gpp.org/ftp/", allow_custom_value=True, scale=9) | |
btn_search = gr.Button("Search") | |
with gr.Accordion("Filter by file status", open=False): | |
with gr.Row(): | |
dd_status = gr.Dropdown(label="Status to look for (Optional)", allow_custom_value=False, multiselect=True, scale=7) | |
btn_search_status = gr.Button("Search for status", scale=2) | |
btn_extract = gr.Button("Extract excel from URL") | |
with gr.Tab("Ask LLM"): | |
gr.Markdown("** This section utilizes Large Language Models (LLMs) to query rows in an Excel file **") | |
dd_source_ask = gr.Dropdown(label="Source Column(s)", multiselect=True) | |
tb_destcol = gr.Textbox(label="Destination column label (e.g. Summary, ELI5, PAB)") | |
dd_prompt = gr.Dropdown(label="Prompt", allow_custom_value=True, multiselect=True, max_choices=1) | |
dd_llm = gr.Dropdown(["Mistral Tiny","Mistral Small","Mistral Medium", "Claude Sonnet", "Claude Opus", "Groq (mixtral)"],value="Groq (mixtral)", label="Choose your LLM") | |
with gr.Accordion("Filters", open=False): | |
with gr.Row(): | |
dd_searchcol = gr.Dropdown(label="Column to look into (Optional)", value='[ALL]', multiselect=False, scale=4) | |
dd_keywords = gr.Dropdown(label="Words to look for (Optional)", multiselect=True, allow_custom_value=True, scale=5) | |
mist_button = gr.Button("Ask AI") | |
with gr.Tab("Classification by topic"): | |
gr.Markdown("**This section utilizes Large Language Models (LLMs) to query rows in an Excel file**") | |
dd_source_class = gr.Dropdown(label="Source Column", multiselect=False) | |
gr.Markdown("### The predefined categories can be modified at any time") | |
df_category = gr.DataFrame(label='categories', value=df_cate, interactive=True) | |
btn_classif = gr.Button("Categorize") | |
with gr.Tab(" Personalised Charts Generation"): | |
with gr.Row(): | |
dd_label1 = gr.Dropdown(label="Label 1", multiselect=False) | |
dd_label2 = gr.Dropdown(label="Label 2", value="", multiselect=False) | |
btn_chart = gr.Button("Generate Bar Plot") | |
plt_figure = gr.Plot() | |
with gr.Tab("Meeting Report (charts)"): | |
#gr.Markdown("## 🚧 Actuellement en maintenance 🚧") | |
with gr.Tab("Overall"): | |
btn_overall = gr.Button("Overall Review") | |
with gr.Tab("By Expert"): | |
dd_exp=gr.Dropdown(label="Experts", multiselect=False, allow_custom_value=True,) | |
btn_expert = gr.Button("Top 10 by expert") | |
with gr.Tab("By Company"): | |
tb_com=gr.Textbox(label="Company Name",info="You can write 1, 2 or 3 company names at the same time") | |
btn_type = gr.Button("Company info") | |
with gr.Row(): | |
plt_chart = gr.Plot(label="Graphique") | |
plt_chart2 = gr.Plot(label="Graphique") | |
plt_chart3 = gr.Plot(label="Graphique") | |
with gr.Tab("Code on your file"): | |
gr.Markdown("**This section lets you add your own code to add functions and filters to edit the files**") | |
with gr.Accordion("Input DataFrame Preview", open=False): | |
df_input = gr.DataFrame(interactive=False) | |
gr.Markdown("```python\ndf = pd.read_excel(YOUR_FILE)\n```") | |
cd_code = gr.Code(value="# Create a copy of the original DataFrame\nnew_df = df.copy()\n\n# Add a new column to the copy\nnew_df['NewColumn'] = 'New Value'", language='python') | |
gr.Markdown("```python\new_df.to_excel(YOUR_NEW_FILE)\nreturn YOUR_NEW_FILE\n```") | |
btn_run_code = gr.Button() | |
df_output_code = gr.DataFrame(interactive=False) | |
btn_export_df = gr.Button('Export df as excel') | |
st_filename = gr.State() | |
with gr.Accordion("Excel Preview", open=False): | |
df_output = gr.DataFrame() | |
fi_excel = gr.File(label="Excel File") | |
# authentication | |
btn_login.click(auth_user, inputs=[tb_user, tb_pwd], outputs=[st_user, md_username, dd_prompt, dd_keywords]) | |
tb_pwd.submit(auth_user, inputs=[tb_user, tb_pwd], outputs=[st_user, md_username, dd_prompt, dd_keywords]) | |
btn_logout.click(logout, inputs=None, outputs=[st_user, md_username, dd_prompt, dd_keywords]) | |
# 3GPP scraping | |
btn_search_status.click(extract_statuses, inputs=dd_url, outputs=dd_status) | |
btn_search.click(browse_folder, inputs=dd_url, outputs=dd_url) | |
dd_url.change(browse_folder, inputs=dd_url, outputs=dd_url) | |
#fi_excel.change(get_expert,inputs=fi_excel, outputs=dd_exp) | |
fi_excel.change(get_columns, inputs=[fi_excel], outputs=[dd_source_ask, dd_source_class, dd_label1, dd_label2, dd_searchcol, df_output,st_filename, df_input]) | |
btn_extract.click(extractionPrincipale, inputs=[dd_url, fi_excel, dd_status], outputs=[fi_excel]) | |
mist_button.click(chat_with_mistral, inputs=[dd_source_ask, tb_destcol, dd_prompt, fi_excel, dd_url, dd_searchcol, dd_keywords, dd_llm, st_user], outputs=[fi_excel, df_output, dd_prompt, dd_keywords, st_user]) | |
btn_classif.click(classification, inputs=[dd_source_class, fi_excel, df_category], outputs=[fi_excel, df_output]) | |
btn_chart.click(create_bar_plot, inputs=[fi_excel, dd_label1, dd_label2], outputs=[plt_figure]) | |
btn_run_code.click(run_code, inputs=[fi_excel, cd_code], outputs=[df_output_code]) | |
btn_export_df.click(export_df, inputs=[df_output_code, st_filename], outputs=fi_excel) | |
btn_overall.click(generate_company_chart,inputs=[fi_excel], outputs=[plt_chart]) | |
btn_overall.click(status_chart,inputs=[fi_excel], outputs=[plt_chart2]) | |
btn_overall.click(category_chart,inputs=[fi_excel], outputs=[plt_chart3]) | |
btn_expert.click(chart_by_expert,inputs=[fi_excel,dd_exp], outputs=[plt_chart]) | |
btn_type.click(company_document_type,inputs=[fi_excel,tb_com], outputs=[plt_chart]) | |
# dd_label1.change(update_label, inputs=[dd_label1], outputs=[dd_label2]) | |
demo.launch(debug=True) |