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ba5532a 62c16a7 0bd18cc ba5532a ae86b65 62c16a7 ba5532a ae86b65 ba5532a ae86b65 ba5532a 62c16a7 ae86b65 62c16a7 ba5532a 1ba6387 ba5532a ae86b65 62c16a7 ba5532a 1b72b3a ba5532a 62c16a7 ba5532a 1b72b3a ba5532a ae86b65 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 | import os
import pandas as pd
import streamlit as st
from io import StringIO
# import json
# from json2table import convert
from src.modules.chatbot import Chatbot_txt, Chatbot, Chatbot_ledger
from src.modules.embedder import Embedder_txt, Embedder
def ledger_to_dataframe(df_d):
# st.write(ledger_csv_path)
# df_d = pd.read_csv(ledger_csv_path)
data_string = df_d.iloc[0]['fullLedger'][1:-1]
temp = data_string
temp = temp.replace("{\"date\":{\"$", '\"').replace("}", "")
result = dict((a.strip(), b.strip())
for a, b in (element.split(':', 1)
for element in temp.split(',')))
columns = [i.replace("\"", '') for i in list(result.keys())]
row_count = 0
for idx, element in enumerate(temp.split(',')):
q = element.split(':')
command = q[0]
if command == '"date"':
row_count = row_count + 1
out = pd.DataFrame(columns=columns, index=range(row_count))
row = -1
for idx, element in enumerate(temp.split(',')):
q = element.split(':')
command = q[0].replace("\"", '')
if len(q) > 2:
value = ''.join(q[1:])
else:
value = q[1]
try:
value = float(value)
except:
value = value
if command == 'date':
# print(row, command, value)
row = row + 1
out.iloc[row][command] = value
out.index.name = 'transaction_id'
return out
class Utilities:
@staticmethod
def load_api_key():
"""
Loads the OpenAI API key from the .env file or from the user's input
and returns it
"""
if os.path.exists(".env") and os.environ.get("OPENAI_API_KEY") is not None:
user_api_key = os.environ["OPENAI_API_KEY"]
st.sidebar.success("API key loaded from .env", icon="🚀")
else:
user_api_key = st.sidebar.text_input(
label="#### Your OpenAI API key 👇", placeholder="Paste your openAI API key, sk-", type="password"
)
if user_api_key:
st.sidebar.success("API key loaded", icon="🚀")
return user_api_key
@staticmethod
def handle_upload_txt():
"""
Handles the file upload and displays the uploaded file
"""
uploaded_file = st.sidebar.file_uploader("upload", type="txt", label_visibility="collapsed")
if uploaded_file is not None:
def show_user_file(uploaded_file):
file_container = st.expander("Your TXT file :")
uploaded_file_content = StringIO(uploaded_file.getvalue().decode("utf-8"))
string_data = uploaded_file_content.read()
file_container.write(string_data)
try:
dict1 = {}
dict1 = json.loads(string_data)
st.write(dict1)
# creating dictionary
# st.write(string_data)
# for line in string_data:
# st.write(line)
# with open(uploaded_file) as fh:
#
# a = 1
# for line in fh:
# command, description = line.strip().split(None, 1)
# dict1[command] = description.strip()
# file_container.write(dict1)
# # creating json file
# # the JSON file is named as test1
# out_file = open("test1.json", "w")
# json.dump(dict1, out_file, indent=4, sort_keys=False)
# out_file.close()
# #
# # # first load the json file
# file_path = 'test1.json'
# with open(file_path, 'r') as f:
# data = json.load(f)
# df = pd.DataFrame(dict1)
df = pd.json_normalize(dict1, record_path=['date'])
st.DataFrame(df)
# build_direction = "TOP_TO_BOTTOM"
# table_attributes = {"style": "width:100%", "class": "table table-striped"}
# html = convert(dict1, build_direction=build_direction, table_attributes=table_attributes)
# st.markdown(html)
except:
print('not json')
st.error('not a json')
show_user_file(uploaded_file)
else:
st.sidebar.info(
"👆 Upload your TXT file to get started, "
# "sample for try : [fishfry-locations.csv](https://drive.google.com/file/d/1TpP3thVnTcDO1_lGSh99EKH2iF3GDE7_/view?usp=sharing)"
)
st.session_state["reset_chat"] = True
return uploaded_file
@staticmethod
def handle_upload():
"""
Handles the file upload and displays the uploaded file
"""
uploaded_file = st.sidebar.file_uploader("upload", type="csv", label_visibility="collapsed")
if uploaded_file is not None:
def show_user_file(uploaded_file):
file_container = st.expander("Your CSV file :")
shows = pd.read_csv(uploaded_file)
uploaded_file.seek(0)
file_container.write(shows)
show_user_file(uploaded_file)
else:
st.sidebar.info(
"👆 Upload your CSV file to get started, "
"sample for try : [fishfry-locations.csv](https://drive.google.com/file/d/1TpP3thVnTcDO1_lGSh99EKH2iF3GDE7_/view?usp=sharing)"
)
st.session_state["reset_chat"] = True
return uploaded_file
@staticmethod
def handle_upload_ledger():
"""
Handles the file upload and displays the uploaded file
"""
uploaded_file = st.sidebar.file_uploader("upload", type="csv", label_visibility="collapsed")
if uploaded_file is not None:
def show_user_file(uploaded_file):
file_container = st.expander("Your Ledger :")
shows = pd.read_csv(uploaded_file)
out = ledger_to_dataframe(shows)
out.to_csv('ledger.csv')
uploaded_file.seek(0)
file_container.write(out)
show_user_file(uploaded_file)
else:
st.sidebar.info(
"👆 Upload your CSV file to get started, "
"sample for try : [fishfry-locations.csv](https://drive.google.com/file/d/1TpP3thVnTcDO1_lGSh99EKH2iF3GDE7_/view?usp=sharing)"
)
st.session_state["reset_chat"] = True
return uploaded_file
@staticmethod
def setup_chatbot_txt(uploaded_file, model, temperature):
"""
Sets up the chatbot with the uploaded file, model, and temperature
"""
embeds = Embedder_txt()
with st.spinner("Processing..."):
uploaded_file.seek(0)
file = uploaded_file.read()
vectors = embeds.getDocEmbeds(file, uploaded_file.name)
chatbot = Chatbot(model, temperature, vectors)
st.session_state["ready"] = True
return chatbot
@staticmethod
def setup_chatbot(uploaded_file, model, temperature):
"""
Sets up the chatbot with the uploaded file, model, and temperature
"""
embeds = Embedder_txt()
with st.spinner("Processing..."):
uploaded_file.seek(0)
file = uploaded_file.read()
vectors = embeds.getDocEmbeds(file, uploaded_file.name)
chatbot = Chatbot(model, temperature, vectors)
st.session_state["ready"] = True
return chatbot
@staticmethod
def setup_chatbot_ledger(uploaded_file, model, temperature):
"""
Sets up the chatbot with the uploaded file, model, and temperature
"""
# embeds = Embedder()
with st.spinner("Processing..."):
uploaded_file.seek(0)
shows = pd.read_csv(uploaded_file)
out = ledger_to_dataframe(shows)
out.to_csv('ledger.csv')
# file = uploaded_file.read()
# vectors = embeds.getDocEmbeds(file, uploaded_file.name)
chatbot = Chatbot_ledger(model, temperature, 'ledger.csv')
st.session_state["ready"] = True
return chatbot
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