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| 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', 'Alma', 'YchK'] | |
| def ask_llm(query, input, client_index): | |
| 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["GROQ_API_KEY"]) | |
| chat_completion = client.chat.completions.create( | |
| messages=messages, | |
| model='mixtral-8x7b-32768', | |
| ) | |
| elif client_index == "Mistral": | |
| client = MistralClient(api_key=os.environ['MISTRAL_API_KEY']) | |
| chat_completion = client.chat( | |
| messages=messages, | |
| model='mistral-small-latest', | |
| ) | |
| else: | |
| client = anthropic.Anthropic(api_key=os.environ['CLAUDE_API_KEY']) | |
| 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): | |
| #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, concatenated_content, client) | |
| print(f"QUERY:\n{prompt}\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) | |
| 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() |