personalizando interface

.streamlit/config.toml

@@ -0,0 +1,5 @@
+[theme]
+backgroundColor="#e3e3ec"
+secondaryBackgroundColor="#4a4949"
+textColor="#071b52"
+font="serif"

app.py

@@ -2,37 +2,30 @@ import os
 from dotenv import load_dotenv
 from langchain_openai import OpenAIEmbeddings
 from langchain_core.prompts import PromptTemplate
-from langchain_core.messages import SystemMessage
 from langchain_qdrant import QdrantVectorStore
 from langchain_openai import ChatOpenAI
 import streamlit as st
 
 load_dotenv(dotenv_path=".env", override=True)
 
+# Configuração ChatBot
 base_prompt_content = open("./prompts/base.md").read()
-
 base_prompt = PromptTemplate.from_template(base_prompt_content)
-
-if "messages_for_ia" not in st.session_state:
-    st.session_state.messages_for_ia = [
+if "messages_for_model" not in st.session_state:
+    st.session_state.messages_for_model = [
         {
             "role": "system",
             "content": "Você é um assistente de pesquisa que ajuda a encontrar informações sobre proteomica"
         }
     ]
-
 if "messages" not in st.session_state:
     st.session_state.messages = []
-
 for message in st.session_state.messages:
     if message["role"] != "system":
         with st.chat_message(message["role"]):
             st.markdown(message["content"])
-
 model = ChatOpenAI(model="gpt-4o-mini", api_key=os.getenv('OPENAI_KEY'))
-
 embeddings = OpenAIEmbeddings(model="text-embedding-3-large", api_key=os.getenv('OPENAI_KEY'))
-
 vector_store = QdrantVectorStore.from_existing_collection(
     url=os.getenv('QDRANT_URL'),
     api_key=os.getenv('QDRANT_KEY'),
@@ -40,7 +33,10 @@ vector_store = QdrantVectorStore.from_existing_collection(
     collection_name='proteomica',
 )
 
-input = st.chat_input("Digite sua pergunta:")
+# Interface do ChatBot
+st.title('Chat LAMFO x Proteomica')
+st.logo('./assets/logo-lamfo.png', size='large')
+input = st.chat_input("Digite sua pergunta")
 
 if input:
     st.chat_message("user").markdown(input)
@@ -59,9 +55,9 @@ if input:
 
     prompt = base_prompt.format(context=documentos_json, question=input)
 
-    st.session_state.messages_for_ia.append({"role": "assistant", "content": prompt})
+    st.session_state.messages_for_model.append({"role": "assistant", "content": prompt})
 
-    resposta = model.invoke(st.session_state.messages_for_ia)
+    resposta = model.invoke(st.session_state.messages_for_model)
     st.session_state.messages.append({"role": "assistant", "content": resposta.content})
-    st.session_state.messages_for_ia.append({"role": "assistant", "content": resposta.content})
+    st.session_state.messages_for_model.append({"role": "assistant", "content": resposta.content})
     st.chat_message("assistant").markdown(resposta.content)