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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 requests
from bs4 import BeautifulSoup
def extract_statuses(url):
# Send a GET request to the webpage
response = requests.get(url)
# Parse the webpage content
soup = BeautifulSoup(response.content, 'html.parser')
# Find all links in the webpage
links = soup.find_all('a')
# Identify and download the Excel file
for link in links:
href = link.get('href')
if href and (href.endswith('.xls') or href.endswith('.xlsx')):
excel_url = href if href.startswith('http') else url + href
excel_response = requests.get(excel_url)
file_name = 'guide_status.xlsx' #excel_url.split('/')[-1]
# Save the file
with open(file_name, 'wb') as f:
f.write(excel_response.content)
# Read the Excel file
df = pd.read_excel(file_name)
# Check if 'TDoc Status' column exists and extract unique statuses
if 'TDoc Status' in df.columns:
unique_statuses = df['TDoc Status'].unique().tolist()
print(f'Downloaded {file_name} and extracted statuses: {unique_statuses}')
if 'withdrawn' in unique_statuses:
unique_statuses.remove('withdrawn')
return unique_statuses
else:
print(f"'TDoc Status' column not found in {file_name}")
return []
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}",
}
]
if client_index == 0:
client = Groq(api_key=os.environ["GROQ_API_KEY"])
chat_completion = client.chat.completions.create(
messages=messages,
model='mixtral-8x7b-32768',
)
else:
client = MistralClient(api_key=os.environ['MISTRAL_API_KEY'])
chat_completion = client.chat(
messages=messages,
model='mistral-small-latest',
)
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):
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)
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):
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() |