Create app.py

app.py

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+import os
+import streamlit as st
+from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
+from langchain_community.llms import HuggingFaceHub
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain.prompts import PromptTemplate
+
+# Environment
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+EMBEDDING_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
+
+@st.cache_resource
+def get_response(question):
+    result = st.session_state.conversational_chain({"question": question})
+    response_text = result.get("answer", "Maaf, saya tidak mengetahui jawaban itu.")
+    
+    # Membersihkan jawaban dari teks yang tidak diperlukan
+    if "Answer:" in response_text:
+        response_text = response_text.split("Answer:")[1].strip()
+    return response_text
+
+
+def setup_vectorstore():
+    persist_directory = "./data"
+    embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME)
+    return Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+
+def chat_chain(vectorstore):
+    hf_hub_llm = HuggingFaceHub(
+        repo_id="SeaLLMs/SeaLLMs-v3-7B-Chat",
+        model_kwargs={"temperature": 1, "max_new_tokens": 1024},
+    )
+
+    prompt_template = """
+    You are an assistant specialized in women's health. Use the retrieved documents to answer the user's question.
+    If you don't know the answer or the information is not in the documents, reply with: "I'm sorry, I don't know."
+
+    Chat History:
+    {chat_history}
+
+    Question:
+    {question}
+
+    Answer:"""
+    prompt = PromptTemplate(input_variables=["chat_history", "question"], template=prompt_template)
+
+
+    # qa_prompt = ChatPromptTemplate.from_messages(messages)
+
+    retriever = vectorstore.as_retriever(
+        search_type="similarity",
+        search_kwargs={"k": 2}
+    )
+
+    memory = ConversationBufferMemory(
+        llm=hf_hub_llm,
+        output_key="answer",
+        memory_key="chat_history",
+        return_messages=True
+    )
+
+    chain = ConversationalRetrievalChain.from_llm(
+        llm=hf_hub_llm,
+        retriever=retriever,
+        chain_type="stuff",
+        memory=memory,
+        verbose=True,
+        combine_docs_chain_kwargs={"prompt": prompt},
+    )
+    return chain
+
+# Streamlit App
+st.set_page_config(
+    page_title="Asisten Kesehatan Wanita",
+    page_icon="💊",
+    layout="centered"
+)
+
+st.title("💊 Asisten Kesehatan Wanita")
+
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+if "vectorstore" not in st.session_state:
+    st.session_state.vectorstore = setup_vectorstore()
+
+if "conversational_chain" not in st.session_state:
+    st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore)
+
+# Display Chat History
+for message in st.session_state.chat_history:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# User Input
+user_input = st.chat_input("Tanyakan sesuatu...")
+
+if user_input:
+    st.session_state.chat_history.append({"role": "user", "content": user_input})
+
+    with st.chat_message("user"):
+        st.markdown(user_input)
+
+
+    with st.chat_message("assistant"):
+        response = st.session_state.conversational_chain({"question": user_input})
+        assistant_response = response["answer"]
+        st.markdown(assistant_response)
+        st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})