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 from groq import Groq import anthropic from users_management import update_json, users #users = ['maksG', 'AlmaA', 'YchK'] def ask_llm(query, input, client_index, user): messages = [ { "role": "system", "content": f"You are a helpful assistant. Only show your final response to the **User Query**! Do not provide any explanations or details: \n# User Query:\n{query}." }, { "role": "user", "content": f"{input}", } ] systemC = f"You are a helpful assistant. Only show your final response to the **User Query**! Do not provide any explanations or details: \n# User Query:\n{query}." messageC=[ { "role": "user", "content": [ { "type": "text", "text": f"{input}" } ] } ] if client_index == "Groq": client = Groq(api_key=os.environ[user['api_keys']['groq']]) chat_completion = client.chat.completions.create( messages=messages, model='mixtral-8x7b-32768', ) elif client_index == "Mistral Small": client = MistralClient(api_key=os.environ[user['api_keys']['mistral']]) chat_completion = client.chat( messages=messages, model='mistral-small-latest', ) elif client_index == "Mistral Tiny": client = MistralClient(api_key=os.environ[user['api_keys']['mistral']]) chat_completion = client.chat( messages=messages, model='mistral-tiny', ) elif client_index == "Mistral Medium": client = MistralClient(api_key=os.environ[user['api_keys']['mistral']]) chat_completion = client.chat( messages=messages, model='mistral-medium', ) elif client_index == "Claude Opus": client = anthropic.Anthropic(api_key=os.environ[user['api_keys']['claude']) chat_completion = client.messages.create( model="claude-3-opus-20240229", max_tokens=350, temperature=0, system=systemC, messages=messageC ).content[0].text return chat_completion else: client = anthropic.Anthropic(api_key=os.environ[user['api_keys']['claude']]) chat_completion = client.messages.create( model="claude-3-sonnet-20240229", max_tokens=350, temperature=0, system=systemC, messages=messageC ).content[0].text return chat_completion return chat_completion.choices[0].message.content def filter_df(df, column_name, keywords): if len(keywords)>0: if column_name in df.columns: contains_keyword = lambda x: any(keyword.lower() in (x.lower() if type(x)==str else '') for keyword in keywords) filtered_df = df[df[column_name].apply(contains_keyword)] else: contains_keyword = lambda row: any(keyword.lower() in (str(cell).lower() if isinstance(cell, str) else '') for keyword in keywords for cell in row) filtered_df = df[df.apply(contains_keyword, axis=1)] else: filtered_df = df return filtered_df def chat_with_mistral(source_cols, dest_col, prompt, excel_file, url, search_col, keywords, client, user): new_prompts, new_keywords = update_json(user, prompt, keywords) print(f'xlsxfile = {excel_file}') df = pd.read_excel(excel_file) df[dest_col] = "" try: file_name = url.split("/")[-2] + ".xlsx" except: file_name = excel_file print(f"Keywords: {keywords}") filtred_df = filter_df(df, search_col, keywords) for index, row in filtred_df.iterrows(): concatenated_content = "\n\n".join(f"{column_name}: {str(row[column_name])}" for column_name in source_cols) print('test') if not concatenated_content == "\n\n".join(f"{column_name}: nan" for column_name in source_cols): print('c bon') llm_answer = ask_llm(prompt[0], concatenated_content, client, user) print(f"QUERY:\n{prompt[0]}\nCONTENT:\n{concatenated_content[:200]}...\n\nANSWER:\n{llm_answer}") df.at[index, dest_col] = llm_answer df.to_excel(file_name, index=False) return file_name, df.head(5), new_prompts, new_keywords def get_columns(file,progress=gr.Progress()): 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 + [""]), gr.update(choices=columns + ['[ALL]']), df.head(5) else: return gr.update(choices=[]), gr.update(choices=[]), gr.update(choices=[]), gr.update(choices=[]), gr.update(choices=[]), pd.DataFrame()