File size: 6,415 Bytes
7eb716b 52beb16 7eb716b 52beb16 |
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 |
import streamlit as st
import json
from data_module import faq_data, model_options
import uuid
from chat_handler import ChatHandler
chat = ChatHandler()
def add_custom_css():
st.markdown("""
<style>
.css-1d391kg { width: 35%; }
</style>
""", unsafe_allow_html=True)
def generate_user_id():
new_id = chat.generate_id()
return new_id
def clear_history(user_id):
chat.clear_history(user_id)
return 'response'
if 'user_id' not in st.session_state:
st.session_state['user_id'] = generate_user_id()
with open("embeddings_db_model.json", "r") as file:
embedding_models = json.load(file)
embedding_model_names = [model["model"] for model in embedding_models]
agent_types = [
'JSON_CHAT_MODEL',
'REACT_TEXT'
]
selected_model = st.sidebar.selectbox("Escolha o Modelo LLM", model_options)
selected_embedding_model = st.sidebar.selectbox("Escolha o Modelo de Embedding", embedding_model_names)
selected_embedding_dir = next(item for item in embedding_models if item["model"] == selected_embedding_model)["dir"]
selected_agent_type = st.sidebar.selectbox("Escolha o Tipo de Agent", agent_types)
add_custom_css()
with st.sidebar:
st.write("## Opções de Controle")
if st.button('Limpar Histórico'):
# Fazer a requisição para limpar o histórico
response = clear_history(st.session_state['user_id'])
if response:
st.session_state.messages = [{"role": "assistant", "content": "Histórico limpo. Pode começar uma nova conversa."}]
st.rerun()
else:
st.error("Erro ao limpar o histórico")
with st.container():
col1, col2 = st.columns([1, 1])
with col1:
st.caption("LLM:")
st.write(selected_model)
with col2:
st.caption("Embeddings:")
st.write(selected_embedding_model)
st.title("⚖️ ChatBot Direito Tributário")
st.caption("Direito Tributário da Pessoa Jurídica")
st.caption("Projeto do Workshop de LLM UFG")
if "messages" not in st.session_state:
st.session_state["messages"] = [{"role": "assistant", "content": "Olá como posso ajudar?"}]
if "faq_question" not in st.session_state:
st.session_state["faq_question"] = None
# Input de chat do usuário
for msg in st.session_state.messages:
if msg['role'] == 'assistant':
img = "server_icon.png"
else:
img = 'user_icon.png'
st.chat_message(msg["role"],avatar=img).write(msg["content"])
def process_question(question):
st.session_state.messages.append({"role": "user", "content": question})
st.chat_message("user", avatar="user_icon.png").write(question)
with st.chat_message("assistant", avatar="server_icon.png"):
with st.spinner("Thinking..."):
data = dict(
user_id=st.session_state['user_id'],
text= question,
embedding_model= selected_embedding_model,
embedding_dir= selected_embedding_dir,
model= selected_model,
agent_type=selected_agent_type
)
msg,intermediary_steps = chat.post_message(message=data)
st.write(str(msg))
st.session_state.messages.append({"role": "assistant", "content": msg})
# Adicionando os passos intermediários
#intermediary_steps = response['response']['intermediate_steps']
# intermediary_steps = []
if intermediary_steps:
with st.expander("Ver Passos Intermediários"):
if intermediary_steps[0] == 'erro':
st.markdown("## ERROR...\n")
else:
st.markdown("## > Entering new AgentExecutor chain...\n")
for index, step in enumerate(intermediary_steps, start=1):
# action = step[0].get('tool', 'Unknown')
action = step[0].tool if hasattr(step[0], 'tool') else 'Unknown'
# action_input = step[0].get('tool_input', 'N/A')
# log = step[0].get('log', 'No log available')
action_input = step[0].tool_input if hasattr(step[0], 'tool_input') else 'N/A'
log = step[0].log if hasattr(step[0], 'log') else 'No log available'
st.markdown(f"**Passo {index}:**")
st.markdown(f" **Ação:** `{action}`")
st.markdown(f" **Entrada da Ação:** `{action_input}`")
st.code(log, language='json')
st.markdown("---")
# Adiciona a ação "Final Answer" ao final dos passos
st.markdown("**Ação:** Final Answer")
st.markdown(f"**Entrada da Ação:** `{msg}`")
st.markdown("## > Finished chain.")
else:
with st.expander("Ver Passos Intermediários"):
st.markdown("#### > Entering new AgentExecutor chain...\n")
st.markdown("**Ação:** Final Answer")
st.markdown(f"**Entrada da Ação:** `{msg}`")
st.markdown("#### > Finished chain...")
def add_faq_question_to_chat(question):
st.session_state["faq_question"] = question
# Barra lateral com perguntas frequentes
with st.sidebar:
st.write("## Perguntas Frequentes")
for index, item in enumerate(faq_data, start=1):
question_with_number = f"{index}\. {item['question']}"
expander = st.expander(question_with_number, expanded=False)
with expander:
st.write(item["answer"])
button_key = f"button_{index}"
if st.button("Enviar esta pergunta", key=button_key):
st.session_state['selected_question'] = item["question"]
if 'selected_question' in st.session_state and st.session_state['selected_question']:
add_faq_question_to_chat(st.session_state['selected_question'])
del st.session_state['selected_question']
if st.session_state["faq_question"]:
process_question(st.session_state["faq_question"])
st.session_state["faq_question"] = None
if prompt := st.chat_input():
process_question(prompt)
|