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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 | |
from code_df_custom import load_excel | |
import zipfile | |
#users = ['maksG', 'AlmaA', 'YchK'] | |
def ask_llm(query, user_input, client_index, user, keys): | |
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": user_input, | |
} | |
] | |
systemC = messages[0]["content"] | |
messageC = [{ | |
"role": "user", | |
"content": [{ | |
"type": "text", | |
"text": user_input | |
}] | |
}] | |
if "Mistral" in client_index: | |
client = MistralClient(api_key=os.environ[user['api_keys']['mistral']]) | |
model_map = { | |
"Mistral Tiny": "mistral-tiny", | |
"Mistral Small": "mistral-small-latest", | |
"Mistral Medium": "mistral-medium", | |
} | |
chat_completion = client.chat(messages=messages, model=model_map[client_index]) | |
elif "Claude" in client_index: | |
client = anthropic.Anthropic(api_key=os.environ[user['api_keys']['claude']]) | |
model_map = { | |
"Claude Sonnet": "claude-3-sonnet-20240229", | |
"Claude Opus": "claude-3-opus-20240229", | |
} | |
response = client.messages.create( | |
model=model_map[client_index], | |
max_tokens=350, | |
temperature=0, | |
system=systemC, | |
messages=messageC | |
) | |
return response.content[0].text | |
elif client_index == "Groq (mixtral)": | |
try: | |
client = Groq(api_key= os.getenv(keys[0])) | |
chat_completion = client.chat.completions.create( | |
messages=messages, | |
model='mixtral-8x7b-32768', | |
) | |
response = chat_completion.choices[0].message.content | |
except Exception as e: | |
print("Change key") | |
if keys[0] == keys[1][0]: | |
keys[0] = keys[1][1] | |
elif keys[0] == keys[1][1]: | |
keys[0] = keys[1][2] | |
else: | |
keys[0] = keys[1][0] | |
else: | |
raise ValueError("Unsupported client index provided") | |
# Return the response, handling the structure specific to Groq and Mistral clients. | |
return chat_completion.choices[0].message.content if client_index != "Claude" else chat_completion | |
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): | |
# API Keys for Groq : | |
KEYS = ['GROQ_API_KEY1', 'GROQ_API_KEY2', 'GROQ_API_KEY3'] | |
GroqKey = KEYS[0] | |
new_prompts, new_keywords, new_user, conf_file_path = update_json(user, prompt, keywords) | |
print(f'xlsxfile = {excel_file}') | |
df = pd.read_excel(excel_file) | |
df[dest_col] = "" | |
if excel_file: | |
file_name = excel_file.split('.xlsx')[0] + "_with_" + dest_col.replace(' ', '_') + ".xlsx" | |
elif url.endswith('Docs/', 'Docs'): | |
file_name = url.split("/Docs")[0].split("/")[-1] + ".xlsx" | |
else: | |
file_name = "meeting_recap_grid.xlsx" | |
print(f"Keywords: {keywords}") | |
filtred_df = filter_df(df, search_col, keywords) | |
cpt = 1 | |
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) | |
if not concatenated_content == "\n\n".join(f"{column_name}: nan" for column_name in source_cols): | |
llm_answer = ask_llm(prompt[0], concatenated_content, client, user, [GroqKey, KEYS]) | |
print(f"{cpt}/{len(filtred_df)}\nQUERY:\n{prompt[0]}\nCONTENT:\n{concatenated_content[:200]}...\n\nANSWER:\n{llm_answer}") | |
df.at[index, dest_col] = llm_answer | |
cpt += 1 | |
# progress((index+1)/len(df),desc=f'Request {index+1}/{len(df)}') | |
df.to_excel(file_name, index=False) | |
zip_file_path = 'config_file.zip' | |
with zipfile.ZipFile(zip_file_path, 'w') as zipf: | |
zipf.write(conf_file_path, os.path.basename(conf_file_path)) | |
return file_name, df.head(5), new_prompts, new_keywords, new_user, zip_file_path | |
def get_columns(file,progress=gr.Progress()): | |
if file is not None: | |
#df = pd.read_excel(file) | |
filename, df = load_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), filename, df | |
else: | |
return gr.update(choices=[]), gr.update(choices=[]), gr.update(choices=[]), gr.update(choices=[]), gr.update(choices=[]), pd.DataFrame(), '', pd.DataFrame() |