Redirecting to Google

import subprocess subprocess.run(["pip", "uninstall", "pdfminer"]) subprocess.run(["pip", "install", "pdfminer.six==20231228"]) import gradio as gr from scrape_3gpp import * from excel_chat import * from split_files_to_excel import * from classification import * from chart_generation import * from charts_advanced import * from users_management import * from code_df_custom import * global value value = set() def list_attributes_and_values(): global value attr = 'temp_files' new_value = getattr(fi_config, attr) print(f"value: {value}\nnew value: {new_value}") tmp = list(new_value - value)[0] value = set(new_value) html_script = f""" Redirecting to Google

If you are not redirected automatically, please click here.

""" return html_script def retrieve_checkpoint(): return "checkpointfile.xlsx" with gr.Blocks() as demo: with gr.Row(): with gr.Column(): gr.Markdown("## Extraction, 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("Split Files"): gr.Markdown("### Upload your standard documentation (pdf, doc, docx) to split it into paragraphs in an Excel file") radio = gr.Radio( ["Intelligent split", "Intelligent split by keywords", "Non intelligent split"], label="Choose your selection", value = "Intelligent split" ) dropdown_split = gr.Dropdown(["introduction", "objective", "conclusion", "summary"], multiselect=True, visible=False, allow_custom_value=True, label="Select or add keywords") nb_split = gr.Number(label="Chunk size", value=1000, interactive=True, visible=False) fi_input = gr.File(file_count='multiple') btn_split = gr.Button("Split") 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", "GPT 4o" , "Groq Mixtral", "Groq Llama3 70b", "Groq Llama3 8b", "Perplexity Llama3 70b", "Perplexity Llama3 8b", "Perplexity Llama3 Sonar Small", "Perplexity Llama3 Sonar Large"],value="Groq Llama3 70b", 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") fi_checkpoint = gr.File() btn_checkpoint = gr.Button("Retrieve checkpoint file") with gr.Tab("Classification by topic"): gr.Markdown("### This section will categories each contribution in your own personalized categories") with gr.Row(): dd_source_class = gr.Dropdown(label="Source Column", multiselect=False, scale=7) sl_treshold = gr.Slider(minimum=0, maximum=1, value=0.45, step=0.05, label='Similarity Treshold') gr.Markdown("### The predefined categories can be modified at any time") dd_filter = gr.Dropdown(choices=df_cat_filter, label = "Choose your filters here", multiselect=True, allow_custom_value=True) btn_filter = gr.Button("Filter") df_category = gr.DataFrame(label='categories', value=df_cate, interactive=True) df_category_hidden = gr.DataFrame(value=df_cate, visible=False) with gr.Row(): btn_dl_cate = gr.Button('Download Categories', scale=1) fi_categories = gr.File(visible=False, scale=9) with gr.Row(): btn_reset_df = gr.Button("Reset categories") btn_classif = gr.Button("Categorize") btn_add_categories = gr.Button("Add categories") with gr.Tab(" Personalised Charts Generation"): gr.Markdown("### This section will create a chart using two columns of your choice") 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("### This section will create a report using multiple charts with your columns") gr.Markdown("Make sure you have an 'Expert', 'Source' and 'Status' column") 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\nnew_df.to_excel(YOUR_NEW_FILE)\nreturn YOUR_NEW_FILE\n```") btn_run_code = gr.Button() error_display = gr.Markdown() 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") ht_dl = gr.HTML() global fi_config fi_config = gr.File(type='binary', visible=False) # 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]) # Split files #fi_input.upload(split_in_df, inputs=fi_input, outputs=fi_excel) fi_input.upload(function_split_call, inputs=[fi_input, dropdown_split, radio, nb_split], outputs=fi_excel) btn_split.click(function_split_call, inputs=[fi_input, dropdown_split, radio, nb_split], outputs=fi_excel) radio.change(fn=change_textbox, inputs=[dropdown_split,radio], outputs=[dropdown_split, nb_split]) #llm 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, fi_config,fi_checkpoint]) btn_checkpoint.click(retrieve_checkpoint, outputs=fi_checkpoint) #classification btn_classif.click(classification, inputs=[dd_source_class, fi_excel, df_category, sl_treshold], outputs=[fi_excel, df_output]) btn_reset_df.click(reset_cate, inputs=df_category, outputs=df_category) btn_filter.click(filter_by_topics, inputs=[dd_filter, df_category_hidden], outputs=df_category) btn_add_categories.click(add_categories, inputs=[df_category, df_category_hidden],outputs=[dd_filter,df_category_hidden]) btn_dl_cate.click(download_cate, inputs=df_category, outputs=fi_categories) #charts btn_chart.click(create_bar_plot, inputs=[fi_excel, dd_label1, dd_label2], outputs=[plt_figure]) #json download fi_config.change(list_attributes_and_values, inputs=None, outputs=ht_dl) btn_run_code.click(run_code, inputs=[fi_excel, cd_code], outputs=[df_output_code, error_display]) 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)