import gradio as gr from mistralai.client import MistralClient from mistralai.models.chat_completion import ChatMessage import os import pandas as pd import numpy as np import requests from bs4 import BeautifulSoup def browse_folder(url): if url.lower().endswith(('docs', 'docs/')): return gr.update(choices=[]) response = requests.get(url) response.raise_for_status() # This will raise an exception if there's an error soup = BeautifulSoup(response.text, 'html.parser') excel_links = [a['href'] + '/' for a in soup.find_all('a', href=True) if a['href'].startswith(url)] return gr.update(choices=excel_links) def chat_with_mistral(source_cols, dest_col, prompt, tdoc_name, excel_file, url): df = pd.read_excel(excel_file) api_key = os.environ["MISTRAL_API_KEY"] model = "mistral-small" # Use "Mistral-7B-v0.2" for "mistral-tiny" client = MistralClient(api_key=api_key) source_columns = source_cols#.split(", ") # Split input into multiple variables df[dest_col] = "" try: file_name = url.split("/")[-2] + ".xlsx" except: file_name = excel_file if tdoc_name != '': filtered_df = df[df['File'] == tdoc_name] if not filtered_df.empty: concatenated_content = "\n\n".join(f"{column_name}: {filtered_df[column_name].iloc[0]}" for column_name in source_columns) messages = [ChatMessage(role="user", content=f"Using the following content: {concatenated_content}"), ChatMessage(role="user", content=prompt)] chat_response = client.chat(model=model, messages=messages) filtered_df.loc[filtered_df.index[0], dest_col] = chat_response.choices[0].message.content # Update the DataFrame with the modified row df.update(filtered_df) # Write the updated DataFrame to the Excel file df.to_excel(file_name, index=False) return file_name, df.head(5) else: return file_name, df.head(5) else: for index, row in df.iterrows(): concatenated_content = "\n\n".join(f"{column_name}: {row[column_name]}" for column_name in source_columns) # Check if the concatenated content is not empty print('test') if not concatenated_content == "\n\n".join(f"{column_name}: nan" for column_name in source_columns): print('c bon') messages = [ChatMessage(role="user", content=f"Using the following content: {concatenated_content}"), ChatMessage(role="user", content=prompt)] chat_response = client.chat(model=model, messages=messages) df.at[index, dest_col] = chat_response.choices[0].message.content df.to_excel(file_name, index=False) return file_name, df.head(5) def get_columns(file): if file is not None: df = pd.read_excel(file) columns = list(df.columns) return gr.update(choices=columns), gr.update(choices=columns), gr.update(choices=columns), gr.update(choices=columns + [""]), df.head(5) else: return gr.update(choices=[]), gr.update(choices=[]), gr.update(choices=[]), gr.update(choices=[]), pd.DataFrame() # 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)