feat: bump version 0.4.0

.streamlit/config.toml

@@ -1,3 +1,6 @@
 [theme]
+font = "sans serif"
+
+[client]
+toolbarMode = "developer"
 
-base = 'light'

README.md

@@ -1,10 +1,10 @@
 ---
-title: Chatbot Carometro
-emoji: 👁
-colorFrom: blue
+title: Chatbot Carometro Staging
+emoji: 💻
+colorFrom: yellow
 colorTo: pink
 sdk: docker
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,15 +1,61 @@
 import streamlit as st
 import requests
-from PIL import Image
-import base64
+import re
+import streamlit_authenticator as stauth
+import os
+import yaml
+from yaml.loader import SafeLoader
+from pathlib import Path
+from drive_search import search_file_in_drive
+from datetime import datetime
+from feedback_manager import FeedbackManager
 
 
 class ChatbotApp:
     def __init__(self):
-        # URL do backend (Flask)
+        # Configura título, ícone e layout da página
+        st.set_page_config(page_title="Sicoob Chatbot 🤖", page_icon="logos/sicoob-ico.ico", layout="wide")
         self.backend_url = "http://localhost:5001/chat"
-        self.title = "Chatbot Carômetro"
-        self.description = "Este assistente virtual pode te ajudar com informações sobre carômetros da Sicoob."
+        self.title = "Sicoob Chatbot"
+        self.description = "Este assistente virtual pode te ajudar com informações sobre documentos da Sicoob."
+        self.caption = "Confira as atualizações no botão 'Atualizações'."
+        self.style_dir = Path("./logos/styles")
+        self.load_styles()
+        self.feedback_manager = FeedbackManager()
+        st.session_state.first = False
+        if "theme" not in st.session_state:
+            st.session_state.theme = "light"
+        self.configyml = os.path.join(os.getcwd(), "./files/config.yaml")
+        if "feedback_submitted" not in st.session_state:
+            st.session_state.feedback_submitted = set()
+
+    def load_styles(self):
+        try:
+            self.base_style = (self.style_dir / "base.css").read_text()
+            self.light_style = (self.style_dir / "light.css").read_text()
+            self.dark_style = (self.style_dir / "dark.css").read_text()
+        except FileNotFoundError as e:
+            st.error(f"Error loading styles: {e}")
+            self.base_style = ""
+            self.light_style = ""
+            self.dark_style = ""
+
+    def change_style(self):
+        with st.sidebar:
+            if st.session_state.theme == "light":
+                st.logo("./logos/sicoob-logo-horizontal-light.png", icon_image="./logos/sicoob-logo-vertical-sm.png")
+            else:
+                st.logo("./logos/sicoob-logo-horizontal-dark.png", icon_image="./logos/sicoob-logo-vertical-sm.png")
+        st.session_state.theme = "dark" if st.session_state.theme == "light" else "light"
+
+    @st.dialog("📄 Atualizações Sicoob Chatbot", width="small")
+    def changelog_user(self):
+        with open("./logos/ChangelogUser.md", encoding="utf-8") as f:
+            st.markdown(f"{f.read()}", unsafe_allow_html=True)
+
+    def get_current_style(self):
+        theme_style = self.dark_style if st.session_state.theme == "dark" else self.light_style
+        return f"<style>{self.base_style}{theme_style}</style>"
 
     def stream_chat(self, user_input):
         """
@@ -30,73 +76,106 @@ class ChatbotApp:
         except Exception as e:
             yield f"Erro ao conectar ao servidor: {e}"
 
+    def clear_chat_history(self):
+        st.session_state.chat_history = []
+        st.toast("Histórico limpo com sucesso!", icon="✅")
+
     def render_sidebar(self):
+        """Siderbar"""
+        with st.sidebar:
+            # st.button("Light/Dark Mode", on_click=self.change_style)
+            if st.session_state.theme == "light":
+                st.logo("./logos/sicoob-logo-horizontal-light.png", icon_image="./logos/sicoob-logo-vertical-sm.png")
+            else:
+                st.logo("./logos/sicoob-logo-horizontal-dark.png", icon_image="./logos/sicoob-logo-vertical-sm.png")
+            if st.button("Limpar Histórico", icon=":material/delete:"):
+                self.clear_chat_history()
+            st.button("Atualizações", icon=":material/info:", on_click=self.changelog_user)
+            st.divider()
+
+    def add_link_to_text(self, text):
         """
-        Exibe opções na barra lateral e renderiza a logo do Sicoob.
+        Adiciona links ao texto com base no padrão ||texto||.
         """
-        st.sidebar.title("Configuração de LLM")
-        sidebar_option = st.sidebar.radio("Selecione o LLM", ["gpt-3.5-turbo"])
-        if sidebar_option != "gpt-3.5-turbo":
-            raise Exception("Opção de LLM inválida!")
-
-        # Exibe a logo do Sicoob na barra lateral
-        with open("sicoob-logo.png", "rb") as f:
-            data = base64.b64encode(f.read()).decode("utf-8")
-            st.sidebar.markdown(
-                f"""
-                <div style="display:table;margin-top:-80%;margin-left:0%;">
-                    <img src="data:image/png;base64,{data}" width="250" height="70">
-                </div>
-                """,
-                unsafe_allow_html=True,
-            )
+        return re.sub(r'\|\|(.*?)\|\|', lambda match: f' <br>Fonte: <a href="{search_file_in_drive(match.group(1))}" target="_blank">{match.group(1)}</a>', text)
 
     def render(self):
         """
         Renderiza a interface do chatbot.
         """
-        # Configura título, ícone e layout da página
-        im = Image.open("pngegg.png")
-        st.set_page_config(page_title="Chatbot Carômetro", page_icon=im, layout="wide")
-
-        # Renderiza a barra lateral
-        self.render_sidebar()
-
-        # Título e descrição
-        st.title(self.title)
-        st.write(self.description)
-
-        # Inicializa o histórico na sessão
-        if "chat_history" not in st.session_state:
-            st.session_state.chat_history = []
-
-        # Renderiza as mensagens do histórico
-        for message in st.session_state.chat_history:
-            role, text = message.split(":", 1)
-            with st.chat_message(role.strip().lower()):
-                st.write(text.strip())
-
-        # Captura o input do usuário
-        user_input = st.chat_input("Digite sua pergunta")
-        if user_input:
-            # Exibe a mensagem do usuário
-            with st.chat_message("user"):
-                st.write(user_input)
-            st.session_state.chat_history.append(f"user: {user_input}")
-
-            # Placeholder para a resposta do assistente
-            with st.chat_message("assistant"):
-                message_placeholder = st.empty()
-                assistant_message = ""
-
-                # Executa o streaming de tokens enquanto o backend responde
-                for token in self.stream_chat(user_input):
-                    assistant_message += token
-                    message_placeholder.markdown(assistant_message + "▌")
-
-                # Atualiza o placeholder com a mensagem final
-                message_placeholder.markdown(assistant_message)
-                st.session_state.chat_history.append(f"assistant: {assistant_message}")
+        st.markdown(self.get_current_style(), unsafe_allow_html=True)
+        with open(self.configyml) as file:
+            config = yaml.load(file, Loader=SafeLoader)
+
+        authenticator = stauth.Authenticate(
+            config['credentials'],
+            config['cookie']['name'],
+            config['cookie']['key'],
+            config['cookie']['expiry_days']
+        )
+
+        with open('./config.yaml', 'w', encoding='utf-8') as file:
+            yaml.dump(config, file, default_flow_style=False)
+
+        authentication_status = authenticator.login(fields={'Form name': 'Autenticação', 'Username': 'Nome de Usuário', 'Password': 'Senha', 'Login': 'Entrar'})
+
+        if st.session_state["authentication_status"]:
+            # Renderiza a barra lateral
+            self.render_sidebar()
+
+            # Título e descrição
+            st.title(self.title)
+            st.write(self.description)
+            with st.sidebar:
+                st.caption(self.caption)
+                authenticator.logout('Sair', 'main')
+            # Inicializa o histórico na sessão
+            if "chat_history" not in st.session_state:
+                st.session_state.chat_history = []
+
+            # Renderiza as mensagens do histórico
+            for message in st.session_state.chat_history:
+                role, text = message.split(":", 1)
+                with st.chat_message(role.strip().lower()):
+                    st.markdown(text.strip(), unsafe_allow_html=True)
+
+            # Captura o input do usuário
+            user_input = st.chat_input(placeholder="Digite sua pergunta...")
+            if user_input:
+                # Exibe a mensagem do usuário
+                with st.chat_message("user"):
+                    st.write(user_input)
+                st.session_state.chat_history.append(f"user: {user_input}")
+                # Placeholder para a resposta do assistente
+                with st.chat_message("assistant"):
+                    message_placeholder = st.empty()
+                    assistant_message = ""
+                    try:
+                        # Gerando ID único para a mensagem
+                        message_id = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+                        print(f"Message ID gerado: {message_id}")
+                        # Executa o streaming de tokens enquanto o backend responde
+                        for token in self.stream_chat(user_input):
+                            assistant_message += token
+                            message_placeholder.markdown(assistant_message + "▌", unsafe_allow_html=True)
+                    except Exception as e:
+                        st.error(f"Erro durante o chat: {str(e)}")
+                        print(f"Erro durante o chat: {str(e)}")
+                    finally:
+                        assistant_message_with_link = self.add_link_to_text(assistant_message)
+                        message_placeholder.markdown(assistant_message_with_link, unsafe_allow_html=True)
+                        # Feedback
+                        self.feedback_manager.render_feedback_buttons(
+                            message_id=message_id,
+                            user_input=user_input,
+                            assistant_response=assistant_message_with_link
+                        )
+                        # Adicionando hist��rico Streamlit
+                        st.session_state.chat_history.append(f"assistant: {assistant_message_with_link}")
+        elif st.session_state["authentication_status"] == False:
+            st.error('Nome de Usuário/Senha incorreta.')
+        elif st.session_state["authentication_status"] == None:
+            st.warning('Por favor entre com seu nome de usuário e senha.')
 
 
 if __name__ == "__main__":

chatbot_server.py

@@ -1,144 +1,140 @@
-import os
-import logging
-import sys
-
-from flask import Flask, request, jsonify, Response
-# Inicializa o Flask
-app = Flask(__name__)
-
-logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-
-from llama_index.llms.openai import OpenAI
-from llama_index.embeddings.openai import OpenAIEmbedding
-from llama_index.core import (
-    Settings,
-    SimpleDirectoryReader,
-    StorageContext,
-    Document,
-)
-
-Settings.llm = OpenAI(model="gpt-3.5-turbo")
-Settings.embed_model = OpenAIEmbedding(model_name="text-embedding-3-small")
-directory_path = "documentos"
-from llama_index.readers.file import PDFReader #concatenar todo o documento já vem nativo no pdfreader
-file_extractor = {".pdf": PDFReader(return_full_document = True)}
-from drive_downloader import GoogleDriveDownloader
-
-# ID da pasta no Drive e caminho local
-folder_id = "1n34bmh9rlbOtCvE_WPZRukQilKeabWsN"
-local_path = directory_path
-
-GoogleDriveDownloader().download_from_folder(folder_id, local_path)
-
-documents = SimpleDirectoryReader(
-    input_dir=directory_path,
-    file_extractor=file_extractor,
-    filename_as_id=True,
-    recursive=True
-).load_data()
-
-from document_creator import create_single_document_with_filenames
-document = create_single_document_with_filenames(directory_path = directory_path)
-documents.append(document)
-
-from llama_index.core.ingestion import IngestionPipeline
-#ingestion pipeline vai entrar em uso quando adicionar o extrator de metadados
-from llama_index.core.node_parser import SentenceSplitter
-splitter = SentenceSplitter(chunk_size=1024, chunk_overlap=128)
-nodes = splitter.get_nodes_from_documents(documents)
-#from llama_index.core.extractors import (
-#    SummaryExtractor,
-#)
-#summary = SummaryExtractor(llm = OpenAI(model="gpt-3.5-turbo", system_prompt "Sempre escreva e responda em português brasileiro."))
-#pipeline = IngestionPipeline(
-#    transformations=[splitter, summary]
-#)
-
-#nodes = pipeline.run(
- #   documents=documents,
- #   in_place=True,
-#    show_progress=True,
-#)
-
-from llama_index.core.storage.docstore import SimpleDocumentStore
-docstore = SimpleDocumentStore()
-docstore.add_documents(nodes)
-
-from llama_index.core import VectorStoreIndex, StorageContext
-from llama_index.vector_stores.chroma import ChromaVectorStore
-import chromadb
-
-db = chromadb.PersistentClient(path="./storage/chroma_db")
-chroma_collection = db.get_or_create_collection("dense_vectors")
-vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
-storage_context = StorageContext.from_defaults(
-    docstore=docstore, vector_store=vector_store
-    )
-index = VectorStoreIndex(nodes = nodes, storage_context=storage_context, show_progress = True)
-
-storage_context.docstore.persist("./storage/docstore.json")
-
-index_retriever = index.as_retriever(similarity_top_k=2)
-import nest_asyncio
-nest_asyncio.apply()
-from llama_index.retrievers.bm25 import BM25Retriever
-bm25_retriever = BM25Retriever.from_defaults(
-    docstore=index.docstore,
-    similarity_top_k=2,
-    language = "portuguese",
-    verbose=True,
-    )
-
-from llama_index.core.retrievers import QueryFusionRetriever
-
-retriever = QueryFusionRetriever(
-    [index_retriever, bm25_retriever],
-    num_queries=1, #desativado = 1
-    mode="reciprocal_rerank",
-    use_async=True,
-    verbose=True,
-)
-
-from llama_index.core.storage.chat_store import SimpleChatStore
-from llama_index.core.memory import ChatMemoryBuffer
-chat_store = SimpleChatStore()
-chat_memory = ChatMemoryBuffer.from_defaults(
-    token_limit=3000,
-    chat_store=chat_store,
-    chat_store_key="user1",
-)
-from llama_index.core.query_engine import RetrieverQueryEngine
-query_engine = RetrieverQueryEngine.from_args(retriever)
-from llama_index.core.chat_engine import CondensePlusContextChatEngine
-chat_engine = CondensePlusContextChatEngine.from_defaults(
-    query_engine,
-    memory=chat_memory,
-    context_prompt=(
-        "Você é um assistente virtual capaz de interagir normalmente, além de"
-        " fornecer informações sobre organogramas e listar funcionários."
-        " Aqui estão os documentos relevantes para o contexto:\n"
-        "{context_str}"
-        "\nInstrução: Use o histórico da conversa anterior, ou o contexto acima, para responder."
-    ),
-)
-
-
-
-@app.route("/chat", methods=["POST"])
-def chat():
-    user_input = request.json.get("message", "")
-    if not user_input:
-        return jsonify({"error": "Mensagem vazia"}), 400
-
-    def generate_response():
-        try:
-            response = chat_engine.stream_chat(user_input)
-            for token in response.response_gen:
-                yield token  # Envia cada token
-            chat_store.persist(persist_path="./storage/chat_store.json")
-        except Exception as e:
-            yield f"Erro: {str(e)}"
-
-    return Response(generate_response(), content_type="text/plain")
-if __name__ == "__main__":
-    app.run(port=5001, debug=False)
+import os
+import logging
+import sys
+
+from flask import Flask, request, jsonify, Response
+# Inicializa o Flask
+app = Flask(__name__)
+
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+
+from llama_index.llms.openai import OpenAI
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.core import (
+    Settings,
+    SimpleDirectoryReader,
+    StorageContext,
+    Document,
+)
+
+Settings.llm = OpenAI(model="gpt-3.5-turbo")
+Settings.embed_model = OpenAIEmbedding(model_name="text-embedding-3-small")
+directory_path = "documentos"
+from llama_index.readers.file import PDFReader #concatenar todo o documento já vem nativo no pdfreader
+file_extractor = {".pdf": PDFReader(return_full_document = True)}
+from drive_downloader import GoogleDriveDownloader
+
+# ID da pasta no Drive e caminho local
+folder_id = "1n34bmh9rlbOtCvE_WPZRukQilKeabWsN"
+local_path = directory_path
+
+GoogleDriveDownloader().download_from_folder(folder_id, local_path)
+
+documents = SimpleDirectoryReader(
+    input_dir=directory_path,
+    file_extractor=file_extractor,
+    filename_as_id=True,
+    recursive=True
+).load_data()
+
+from document_creator import create_single_document_with_filenames
+document = create_single_document_with_filenames(directory_path = directory_path)
+documents.append(document)
+
+#from llama_index.core.ingestion import IngestionPipeline
+#ingestion pipeline vai entrar em uso quando adicionar o extrator de metadados
+from llama_index.core.node_parser import SentenceSplitter
+splitter = SentenceSplitter(chunk_size=1024, chunk_overlap=128)
+nodes = splitter.get_nodes_from_documents(documents)
+
+from llama_index.core.storage.docstore import SimpleDocumentStore
+docstore = SimpleDocumentStore()
+docstore.add_documents(nodes)
+
+from llama_index.core import VectorStoreIndex, StorageContext
+from llama_index.vector_stores.chroma import ChromaVectorStore
+import chromadb
+
+db = chromadb.PersistentClient(path="chroma_db")
+chroma_collection = db.get_or_create_collection("dense_vectors")
+vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+storage_context = StorageContext.from_defaults(
+    docstore=docstore, vector_store=vector_store
+    )
+index = VectorStoreIndex(nodes = nodes, storage_context=storage_context, show_progress = True)
+
+storage_context.docstore.persist("./docstore.json")
+
+index_retriever = index.as_retriever(similarity_top_k=2)
+import nest_asyncio
+nest_asyncio.apply()
+from llama_index.retrievers.bm25 import BM25Retriever
+bm25_retriever = BM25Retriever.from_defaults(
+    docstore=index.docstore,
+    similarity_top_k=2,
+    language = "portuguese",
+    verbose=True,
+    )
+
+from llama_index.core.retrievers import QueryFusionRetriever
+
+retriever = QueryFusionRetriever(
+    [index_retriever, bm25_retriever],
+    num_queries=1, #desativado = 1
+    mode="reciprocal_rerank",
+    use_async=True,
+    verbose=True,
+)
+
+
+from llama_index.core.memory import ChatMemoryBuffer
+from mysqlchatstore import MySQLChatStore
+chat_store = MySQLChatStore.from_params(
+    host=os.getenv("MYSQL_HOST"),
+    port=os.getenv("MYSQL_PORT"),
+    user=os.getenv("MYSQL_USER"),
+    password=os.getenv("MYSQL_PASSWORD"),
+    database=os.getenv("MYSQL_DATABASE"),
+    table_name=os.getenv("MYSQL_TABLE")
+)
+chat_memory = ChatMemoryBuffer.from_defaults(
+    token_limit=3000,
+    chat_store=chat_store,
+    chat_store_key="Sicoob", #Tendo algumas dificuldades ainda pra passar o user
+)
+from llama_index.core.query_engine import RetrieverQueryEngine
+query_engine = RetrieverQueryEngine.from_args(retriever)
+from llama_index.core.chat_engine import CondensePlusContextChatEngine
+chat_engine = CondensePlusContextChatEngine.from_defaults(
+    query_engine,
+    memory=chat_memory,
+    context_prompt=(
+        "Você é um assistente virtual capaz de interagir normalmente, além de"
+        " fornecer informações sobre organogramas e listar funcionários."
+        " Aqui estão os documentos relevantes para o contexto:\n"
+        "{context_str}"
+        "\nInstrução: Use o histórico da conversa anterior, ou o contexto acima, para responder."
+        "No final da resposta, depois de uma quebra de linha escreva o nome do documento que contém a informação entre dois ||, como ||Documento Nome||"
+
+    ),
+)
+
+
+
+@app.route("/chat", methods=["POST"])
+def chat():
+    user_input = request.json.get("message", "")
+    if not user_input:
+        return jsonify({"error": "Mensagem vazia"}), 400
+
+    def generate_response():
+        try:
+            response = chat_engine.stream_chat(user_input)
+            for token in response.response_gen:
+                yield token  # Envia cada token
+        except Exception as e:
+            yield f"Erro: {str(e)}"
+
+    return Response(generate_response(), content_type="text/plain")
+if __name__ == "__main__":
+    app.run(port=5001, debug=False)

config.yaml

@@ -0,0 +1,38 @@
+cookie:
+  expiry_days: 30
+  key: some_signature_key
+  name: some_cookie_name
+credentials:
+  usernames:
+    akcit_root:
+      email: akcit_root@mail.com
+      failed_login_attempts: 0
+      first_name: akcit
+      last_name: root
+      logged_in: false
+      password: $2b$12$wd1lg1DTs4qEDRmohdptDegeSJhGstxqgCaTitfRQ0IGN.rnr51aG
+      roles:
+      - admin
+      - editor
+      - viewer
+    sicoob_central:
+      email: sicoob_central@mail.com
+      failed_login_attempts: 0
+      first_name: sicoob
+      last_name: central
+      logged_in: false
+      password: $2b$12$Y5tHfGABzVP9dm510HuHHuUbeZvNqUibUgj4TYH40rglhZGLPZ8rK
+      roles:
+      - viewer
+    sicoob_unidade:
+      email: sicoob_unidade@mail.com
+      failed_login_attempts: 0
+      first_name: sicoob
+      last_name: unidade
+      logged_in: false
+      password: $2b$12$h8U7XrVfACkHJaqGqcwR0OzDO.YorKF21lHpG/9MVa4K/98AbXtG.
+      roles:
+      - viewer
+pre-authorized:
+  emails:
+  - akcit_root@mail.com

drive_search.py

@@ -0,0 +1,77 @@
+from googleapiclient.discovery import build
+from google.oauth2.service_account import Credentials
+from fuzzywuzzy import process  # Importando a biblioteca fuzzywuzzy
+import os
+
+SCOPES = ["https://www.googleapis.com/auth/drive.readonly"]
+FOLDER_ID = "1hqfPQnsVL2Ld8hu0GRIqcuOp-eDz-CAX"  # ID da pasta que você quer buscar
+
+SERVICE_ACCOUNT_FILE = os.path.join(os.getcwd(), "./files/credenciais.json")
+def authenticate_drive():
+    """Autentica no Google Drive usando uma conta de serviço."""
+    credentials = Credentials.from_service_account_file(SERVICE_ACCOUNT_FILE, scopes=SCOPES)
+    service = build("drive", "v3", credentials=credentials)
+    return service
+
+def list_files_recursive(service, folder_id, path=""):
+    """Lista todos os arquivos e subpastas no Google Drive de forma recursiva."""
+    query = f"'{folder_id}' in parents and trashed = false"
+    response = service.files().list(
+        q=query,
+        spaces="drive",
+        fields="files(id, name, mimeType, parents)",
+    ).execute()
+
+    files = response.get("files", [])
+    all_files = []
+
+    for file in files:
+        # caminho completo
+        current_path = f"{path}/{file['name']}"
+        all_files.append({"id": file["id"], "name": file["name"], "path": current_path})
+
+        # buscar recursivamente
+        if file["mimeType"] == "application/vnd.google-apps.folder":
+            all_files.extend(list_files_recursive(service, file["id"], current_path))
+
+    return all_files
+
+def find_file_by_name(files, search_name):
+    """Encontra um arquivo com nome aproximado utilizando fuzzy matching."""
+    # Usando fuzzywuzzy para encontrar o arquivo mais próximo
+    file_names = [file["name"] for file in files]
+    best_match, score = process.extractOne(search_name, file_names)
+    
+    if score >= 80:  # Ajuste a pontuação mínima de correspondência (por exemplo, 80%)
+        matching_files = [file for file in files if file["name"] == best_match]
+        return matching_files
+    else:
+        return []
+
+def search_file_in_drive(search_name):
+    """Procura o arquivo mais relevante no Google Drive e retorna o link."""
+    service = authenticate_drive()
+    print("Autenticado com sucesso no Google Drive!")
+
+    # Listar arquivos na pasta e subpastas
+    files = list_files_recursive(service, FOLDER_ID)
+
+    if not files:
+        print("Nenhum arquivo encontrado na pasta!")
+        return None  # Retorna None se nenhum arquivo for encontrado
+
+    print(f"Total de arquivos encontrados: {len(files)}")
+
+    # Encontrar o arquivo com nome aproximado
+    matching_files = find_file_by_name(files, search_name)
+
+    if matching_files:
+        best_file = matching_files[0]  # Pega o arquivo mais relevante (primeiro da lista)
+        link = f"https://drive.google.com/file/d/{best_file['id']}/view"
+        print(f"Arquivo encontrado: {best_file['name']}")
+        print(f"Link: {link}")
+        return link  # Retorna o link do arquivo encontrado
+    else:
+        print(f"Nenhum arquivo encontrado com nome aproximado '{search_name}'.")
+        return None  # Retorna None se nenhum arquivo for encontrado
+

feedback_manager.py

@@ -0,0 +1,67 @@
+import json
+from datetime import datetime
+from pathlib import Path
+import streamlit as st
+
+
+class FeedbackManager:
+    def __init__(self, feedback_dir="./feedback"):
+        self.feedback_dir = Path(feedback_dir)
+        self.feedback_dir.mkdir(exist_ok=True)
+        if "feedback_submitted" not in st.session_state:
+            st.session_state.feedback_submitted = set()
+
+    def save_feedback(self, feedback_type: str, user_input: str, assistant_response: str, message_id: str):
+        if message_id in st.session_state.feedback_submitted:
+            st.toast("Feedback já enviado para está mensagem.", icon="ℹ️")
+            return False
+        feedback_file = self.feedback_dir / f"feedback_{feedback_type}.json"
+        try:
+            data = []
+            if feedback_file.exists():
+                with open(feedback_file, 'r', encoding='utf-8') as f:
+                    data = json.load(f)
+            feedback_entry = {
+                "message_id": message_id,
+                "timestamp": datetime.now().isoformat(),
+                "feedback_type": feedback_type,
+                "user_input": user_input,
+                "assistant_response": assistant_response,
+            }
+            data.append(feedback_entry)
+            with open(feedback_file, 'w', encoding='utf-8') as f:
+                json.dump(data, f, ensure_ascii=False, indent=2)
+            st.session_state.feedback_submitted.add(message_id)
+            emoji = "👍" if feedback_type == "positive" else "👎"
+            st.toast(f"Obrigado por seu Feedback! {emoji}", icon="✅")
+            return True
+        except Exception as e:
+            st.error(f"Error ao salvar feedback: {str(e)}")
+            return False
+
+    def handle_feedback_click(self, feedback_type: str, message_id: str, user_input: str, assistant_response: str):
+        self.save_feedback(
+            feedback_type=feedback_type,
+            user_input=user_input,
+            assistant_response=assistant_response,
+            message_id=message_id
+        )
+
+    def render_feedback_buttons(self, message_id: str, user_input: str, assistant_response: str):
+        col1, col2 = st.columns(2, gap="small")
+
+        with col1:
+            st.button(
+                "👍 Gostei",
+                key=f"positive_{message_id}",
+                on_click=self.handle_feedback_click,
+                args=("positive", message_id, user_input, assistant_response)
+            )
+
+        with col2:
+            st.button(
+                "👎 Não Gostei",
+                key=f"negative_{message_id}",
+                on_click=self.handle_feedback_click,
+                args=("negative", message_id, user_input, assistant_response)
+            )

files/config.yaml

@@ -0,0 +1,38 @@
+cookie:
+  expiry_days: 30
+  key: some_signature_key
+  name: some_cookie_name
+credentials:
+  usernames:
+    akcit_root:
+      email: akcit_root@mail.com
+      failed_login_attempts: 0
+      first_name: akcit
+      last_name: root
+      logged_in: false
+      password: $2b$12$wd1lg1DTs4qEDRmohdptDegeSJhGstxqgCaTitfRQ0IGN.rnr51aG
+      roles:
+      - admin
+      - editor
+      - viewer
+    sicoob_central:
+      email: sicoob_central@mail.com
+      failed_login_attempts: 0
+      first_name: sicoob
+      last_name: central
+      logged_in: false
+      password: $2b$12$Y5tHfGABzVP9dm510HuHHuUbeZvNqUibUgj4TYH40rglhZGLPZ8rK
+      roles:
+      - viewer
+    sicoob_unidade:
+      email: sicoob_unidade@mail.com
+      failed_login_attempts: 0
+      first_name: sicoob
+      last_name: unidade
+      logged_in: false
+      password: $2b$12$h8U7XrVfACkHJaqGqcwR0OzDO.YorKF21lHpG/9MVa4K/98AbXtG.
+      roles:
+      - viewer
+pre-authorized:
+  emails:
+  - akcit_root@mail.com

files/credenciais.json

@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "light-processor-374314",
+  "private_key_id": "a207b3d84c4dfdf97eef75f442a78b79a9af7e4a",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nYglZoVNfDeElMat1FZR9dhXo9w==\n-----END PRIVATE KEY-----\n",
+  "client_email": "mydriveapi@light-processor-374314.iam.gserviceaccount.com",
+  "client_id": "115319015910729374958",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/mydriveapi%40light-processor-374314.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}

logos/ChangelogUser.md

@@ -0,0 +1,31 @@
+# Changelog - Versão 0.4.0
+
+## Novos Recursos
+
+- **Suporte a múltiplos manuais**: O chatbot agora pode acessar e fornecer informações de diversos manuais.
+  - Manuais disponíveis nesta versão:
+  - Manual de Normatização
+  - Manual de Riscos Sociais, Ambientais e Climáticos
+  - Manual de Supervisão Auxiliar
+  - Manual de Controles Internos e Conformidade
+  - Manual de Crédito
+  - Manual de Boas Práticas Legais e Tributárias nas Campanhas e Promoções
+  - Manual de Gerenciamento do Risco de Mercado e do IRRBB
+  - Manual de Seguro Vida Prestamista
+  - Manual do Produto Pix
+  - Manual de Câmbio - Operações de Crédito
+  - ...
+
+## Melhorias
+
+- **Qualidade de resposta aprimorada**: Refinamento nos modelos de resposta para oferecer informações mais precisas e relevantes.
+- **Otimização de consultas**: Melhorias no tempo de resposta ao buscar informações em documentos.
+
+## Alterações e Remoções
+
+- **Remoção temporária de respostas sobre o organograma**: Devido a ajustes internos, informações sobre a estrutura organizacional foram temporariamente desativadas. Previsto para retorno na versão 0.5.0.
+
+## Próximos Passos
+
+- **Expansão do suporte a mais manuais e documentos internos.**
+- **Reintegração das informações sobre o organograma na versão 0.5.0.**