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| import streamlit as st | |
| import pandas as pd | |
| import torch | |
| from transformers import pipeline | |
| import datetime | |
| #from datasets import load_dataset | |
| #dataset = load_dataset("wikitablequestions", trust_remote_code=True) | |
| #item = dataset["test"][10] # show first test example | |
| #def to_pandas(item): | |
| #return pd.DataFrame(item['table']["rows"], columns=item['table']["header"]) | |
| #df = to_pandas(item) | |
| #print(df.head()) | |
| df = pd.read_csv("anomalies.csv",quotechar='"',dtype={col: str for col in pd.read_csv('anomalies.csv', nrows=1)}) | |
| df = df.fillna('').astype(str) | |
| # Function to generate a response using the TAPEX model | |
| def response(user_question, df): | |
| a = datetime.datetime.now() | |
| tqa = pipeline(task="table-question-answering", model="google/tapas-large-finetuned-wtq") | |
| print("DataFrame shape:", df.shape) | |
| print("DataFrame head:\n", df.head()) | |
| print("User question:", user_question) | |
| answer = tqa(table=df, query=user_question)['answer'] | |
| query_result = { | |
| "Resposta": answer | |
| } | |
| b = datetime.datetime.now() | |
| print(b - a) | |
| return query_result | |
| # Streamlit interface | |
| st.markdown(""" | |
| <div style='display: flex; align-items: center;'> | |
| <div style='width: 40px; height: 40px; background-color: green; border-radius: 50%; margin-right: 5px;'></div> | |
| <div style='width: 40px; height: 40px; background-color: red; border-radius: 50%; margin-right: 5px;'></div> | |
| <div style='width: 40px; height: 40px; background-color: yellow; border-radius: 50%; margin-right: 5px;'></div> | |
| <span style='font-size: 40px; font-weight: bold;'>Chatbot do Tesouro RS</span> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Chat history | |
| if 'history' not in st.session_state: | |
| st.session_state['history'] = [] | |
| # Input box for user question | |
| user_question = st.text_input("Escreva sua questΓ£o aqui:", "") | |
| if user_question: | |
| # Add human emoji when user asks a question | |
| st.session_state['history'].append(('π€', user_question)) | |
| st.markdown(f"**π€ {user_question}**") | |
| # Generate the response | |
| bot_response = response(user_question, df)["Resposta"] | |
| # Add robot emoji when generating response and align to the right | |
| st.session_state['history'].append(('π€', bot_response)) | |
| st.markdown(f"<div style='text-align: right'>**π€ {bot_response}**</div>", unsafe_allow_html=True) | |
| # Clear history button | |
| if st.button("Limpar"): | |
| st.session_state['history'] = [] | |
| # Display chat history | |
| for sender, message in st.session_state['history']: | |
| if sender == 'π€': | |
| st.markdown(f"**π€ {message}**") | |
| elif sender == 'π€': | |
| st.markdown(f"<div style='text-align: right'>**π€ {message}**</div>", unsafe_allow_html=True) |