Update views/exame.py

views/exame.py

@@ -1,83 +1,6 @@
-import streamlit as st
-from openai import OpenAI
-from langchain.document_loaders import UnstructuredFileLoader
-
-st.title("Extração de resultados")
-
-
-def load_document(file_path, mode="single"):
-    loader = UnstructuredFileLoader(file_path, mode=mode)
-    docs = loader.load()
-
-    if mode == "single":
-        return docs[0].page_content, []
-    elif mode == "elements":
-        return " ".join([str(doc.page_content) for doc in docs]), docs
-
-
-# Sidebar para entrada de chave API e seleção de modelo
-with st.sidebar:
-    openai_api_key = st.text_input(
-        "OpenAI API Key", key="chatbot_api_key", type="password"
-    )
-    st.markdown(
-        "[Pegue aqui sua chave OpenAI API](https://platform.openai.com/account/api-keys)"
-    )
-
-    uploaded_file = st.file_uploader(
-        "Escolha um arquivo PDF ou imagem", type=["pdf", "png", "jpg", "jpeg", "docx"]
-    )
-
-    if st.button("Limpar Conversa"):
-        st.session_state["messages"] = [
-            {"role": "assistant", "content": "How can I help you?"}
-        ]
-        st.experimental_rerun()
-
-st.title("💬 Chatbot")
-st.caption("🚀 A Streamlit chatbot powered by OpenAI")
-
-models = "asst_wXmB8syPqCxPWGtNqiuOXQtn"
-
-if "messages" not in st.session_state:
-    st.session_state["messages"] = [
-        {"role": "assistant", "content": "How can I help you?"}
-    ]
-
-for msg in st.session_state.messages:
-    st.chat_message(msg["role"]).write(msg["content"])
-
-
-if prompt := st.chat_input():
-    if not openai_api_key:
-        st.info("Insira sua chave API OpenAI para continuar.")
-        st.stop()
-
-    client = OpenAI(api_key=openai_api_key)
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    st.chat_message("user").write(prompt)
-    response = client.chat.completions.create(
-        model=models, messages=st.session_state.messages
-    )
-    msg = response.choices[0].message.content
-    st.session_state.messages.append({"role": "assistant", "content": msg})
-    st.chat_message("assistant").write(msg)
-
-
-if uploaded_file is not None:
-    data = load_document(st.file_uploader)
-    if data := st.session_state.get("uploaded_file", None) is not None and st.button(
-        "Enviar", type="primary"
-    ):
-        if data is not None:
-            st.session_state.exames({"role": "uploaded_file", "content": data})
-            resposta = client.chat.completions.create(
-                model=models, messages=st.session_state.exames
-            )
-            msg = resposta.choices[0].message.content
-            st.session_state.exames.append({"role": "assistant", "content": msg})
-            st.chat_message("assistant").write(msg)
-        else:
-            st.error("Não foi possível extrair texto do arquivo carregado.")
-    else:
-        st.warning("Por favor, carregue um arquivo antes de enviar.")
+import streamlit as st
+
+
+st.title("Extração de resultados")
+
+st.write('## Em breve!')