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| 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: | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |