Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from transformers import BartForConditionalGeneration, TapexTokenizer, T5ForConditionalGeneration, T5Tokenizer
+import datetime
+
+# Load CSV file
+df = pd.read_csv("anomalies.csv", quotechar='"')
+df.rename(columns={"ds": "Ano e mês", "real": "Valor Monetário", "Group": "Grupo"}, inplace=True)
+df.sort_values(by=['Ano e mês', 'Valor Monetário'], ascending=False, inplace=True)
+df = df[df['Valor Monetário'] >= 1000000.]
+df['Valor Monetário'] = df['Valor Monetário'].apply(lambda x: f"{x:.2f}")
+df = df.fillna('').astype(str)
+
+# Load translation models
+pt_en_translator = T5ForConditionalGeneration.from_pretrained("unicamp-dl/translation-pt-en-t5")
+en_pt_translator = T5ForConditionalGeneration.from_pretrained("unicamp-dl/translation-en-pt-t5")
+tokenizer = T5Tokenizer.from_pretrained("unicamp-dl/translation-pt-en-t5")
+
+# Load TAPEX model
+tapex_model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wtq")
+tapex_tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
+
+def translate(text, model, tokenizer, source_lang="pt", target_lang="en"):
+    input_ids = tokenizer.encode(text, return_tensors="pt", add_special_tokens=True)
+    outputs = model.generate(input_ids)
+    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return translated_text
+
+def response(user_question, table_data):
+    # Translate question to English
+    question_en = translate(user_question, pt_en_translator, tokenizer, source_lang="pt", target_lang="en")
+
+    # Generate response in English
+    encoding = tapex_tokenizer(table=table_data, query=[question_en], padding=True, return_tensors="pt", truncation=True)
+    outputs = tapex_model.generate(**encoding)
+    response_en = tapex_tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+
+    # Translate response to Portuguese
+    response_pt = translate(response_en, en_pt_translator, tokenizer, source_lang="en", target_lang="pt")
+    return response_pt
+
+# Streamlit interface
+
+st.dataframe(table_data.head())
+
+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 questão aqui:", "")
+
+if user_question:
+    # Add human emoji when user asks a question
+    st.session_state['history'].append(('👤', user_question))
+    st.markdown(f"**👤 {user_question}**")
+    
+    # Generate the response
+    bot_response = response(user_question, table_data)["Resposta"]
+    
+    # Add robot emoji when generating response and align to the right
+    st.session_state['history'].append(('🤖', bot_response))
+    st.markdown(f"<div style='text-align: right'>**🤖 {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 == '👤':
+        st.markdown(f"**👤 {message}**")
+    elif sender == '🤖':
+        st.markdown(f"<div style='text-align: right'>**🤖 {message}**</div>", unsafe_allow_html=True)