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
+import sentencepiece as spm
+
+# File upload interface
+uploaded_file = st.file_uploader("Upload a CSV or XLSX file", type=['csv', 'xlsx'])
+
+if uploaded_file:
+    # Load the file into a DataFrame
+    if uploaded_file.name.endswith('.csv'):
+        df = pd.read_csv(uploaded_file, quotechar='"', encoding='utf-8')
+    elif uploaded_file.name.endswith('.xlsx'):
+        df = pd.read_excel(uploaded_file)
+
+    df.rename(columns={"ds": "datetime", "real": "monetary value", "Explicação": "explanation"}, inplace=True)
+    df.sort_values(by=['datetime', 'monetary value'], ascending=False, inplace=True)
+    df = df[df['monetary value'] >= 10000000.]
+    df['monetary value'] = df['monetary value'].apply(lambda x: f"{x:.2f}")
+    df = df.fillna('').astype(str)
+    table_data = df
+
+    # Display the uploaded table
+    st.dataframe(table_data.head())
+
+    # 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):
+        # Traduz a pergunta para o inglês
+        question_en = translate(user_question, pt_en_translator, tokenizer, source_lang="pt", target_lang="en")
+        print(question_en)
+
+        # Gera a resposta em inglês
+        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]
+        print(response_en)
+
+        # Traduz a resposta para o português
+        response_pt = translate(response_en, en_pt_translator, tokenizer, source_lang="en", target_lang="pt")
+        return response_pt
+
+    # 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 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)
+        
+        # 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)
+else:
+    st.warning("Carregue um arquivo CSV ou XLSX para começar.")