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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
num_rows = 50
df = pd.read_csv('emails_cleaned.csv', on_bad_lines='skip', nrows=num_rows)
def get_message(Series: pd.Series):
result = pd.Series(index=Series.index)
for row, message in enumerate(Series):
message_words = message.split('\n')
del message_words[:15]
result.iloc[row] = ''.join(message_words).strip()
return result
def get_date(Series: pd.Series):
result = pd.Series(index=Series.index)
for row, message in enumerate(Series):
message_words = message.split('\n')
del message_words[0]
del message_words[1:]
result.iloc[row] = ''.join(message_words).strip()
result.iloc[row] = result.iloc[row].replace('Date: ', '')
print('Done parsing, converting to datetime format..')
return pd.to_datetime(result)
def get_sender_and_receiver(Series: pd.Series):
sender = pd.Series(index = Series.index)
recipient1 = pd.Series(index = Series.index)
recipient2 = pd.Series(index = Series.index)
recipient3 = pd.Series(index = Series.index)
for row,message in enumerate(Series):
message_words = message.split('\n')
sender[row] = message_words[2].replace('From: ', '')
recipient1[row] = message_words[3].replace('To: ', '')
recipient2[row] = message_words[10].replace('X-cc: ', '')
recipient3[row] = message_words[11].replace('X-bcc: ', '')
return sender, recipient1, recipient2, recipient3
def get_subject(Series: pd.Series):
result = pd.Series(index = Series.index)
for row, message in enumerate(Series):
message_words = message.split('\n')
message_words = message_words[4]
result[row] = message_words.replace('Subject: ', '')
return result
def get_folder(Series: pd.Series):
result = pd.Series(index = Series.index)
for row, message in enumerate(Series):
message_words = message.split('\n')
message_words = message_words[12]
result[row] = message_words.replace('X-Folder: ', '')
return result
df['text'] = get_message(df.message)
df['sender'], df['recipient1'], df['recipient2'], df['recipient3'] = get_sender_and_receiver(df.message)
df['Subject'] = get_subject(df.message)
df['folder'] = get_folder(df.message)
df['date'] = get_date(df.message)
df = df.drop(['message', 'file'], axis = 1)
import chromadb
chroma_client = chromadb.Client()
from chromadb.utils import embedding_functions
sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="paraphrase-MiniLM-L3-v2")
collection_minilm = chroma_client.create_collection(name="emails_minilm", embedding_function=sentence_transformer_ef)
for i in df.index:
print(i)
collection_minilm.add(
documents = df.loc[i, 'text'],
metadatas = [{"sender": df.loc[i, 'sender'],
"recipient1": df.loc[i, 'recipient1'],
"recipient2": df.loc[i, 'recipient2'],
"recipient3": df.loc[i, 'recipient3'],
"subject": df.loc[i, 'Subject'],
"folder": df.loc[i, 'folder'],
"date": str(df.loc[i, 'date'])
}],
ids = str(i)
)
results = collection_minilm.query(
query_texts = ["this is a document"],
n_results = 2,
include = ['distances', 'metadatas', 'documents']
)
results
import gradio as gr
import ast
def create_output(dictionary, number):
dictionary_ids = str(dictionary['ids'])
dictionary_ids_clean = dictionary_ids.strip("[]")
dictionary_ids_clean = dictionary_ids_clean.replace("'", "")
dictionary_ids_list = dictionary_ids_clean.split(", ")
string_results = "";
for n in range(number):
t = collection_minilm.get(
ids=[dictionary_ids_list[n]]
)
id = str(t["ids"])
doc = str(t["documents"])
metadata = str(t["metadatas"])
dictionary_metadata = ast.literal_eval(metadata.strip("[]"))
string_results_old = string_results
string_temp = """---------------
SUBJECT: """ + dictionary_metadata['subject'] + """"
MESSAGE: """ + "\n" + doc + """
---------------"""
string_results = string_results_old + string_temp
return string_results
def query_chromadb_advanced(question,numberOfResults):
results = collection_minilm.query(
query_texts = question,
n_results = numberOfResults,
)
return create_output(results, numberOfResults)
result_advance = query_chromadb_advanced("bank", 4)
iface = gr.Interface(
fn=query_chromadb_advanced,
inputs=["text","number"],
outputs="text",
title="Email Dataset Interface",
description="Insert the question or the key word to find the topic correlated in the dataset"
)
iface.launch(share=True) |