app.py

"""This is an example of a simple chatbot that uses the RAG model to answer questions
about GIZ with Streamlit."""

import streamlit as st

# Here we import the rag_pipeline function from the rag.py file
from rag import rag_pipeline


# We use the st.cache decorator to load the RAG pipeline only once and cache it
@st.cache_resource
def load_rag_pipeline():
    rag = rag_pipeline()
    return rag


rag = load_rag_pipeline()

st.image("gender_strat_cover_pic.png", use_container_width=True)

st.markdown(
    """
    <div style="display: flex; align-items: center; gap: 10px;">
        <span style="font-size: 30px;">🤖</span>
        <h3 style="margin: 0;">Welcome to the GIZ Gender Strategy Assistant</h3>
    </div>
    <p>The <b> GIZ Gender Strategy Chatbot </b> enables users to explore GIZ’s Gender Strategy through open, context-aware questions. It provides insights into how gender equality is integrated into operations, business development, and corporate values. Aligned with GIZ’s vision, the assistant makes gender-related topics accessible, supporting users in understanding policies, enhancing gender competence, and promoting inclusive practices.</p>
    """,
    unsafe_allow_html=True
)


with st.expander("📖 Background Information & Example Questions"):
    st.markdown(
        """
        <h4>💡 How does the app work?</h4>
        <p>The assistant uses a <b>Retrieval-Augmented Generation (RAG) approach</b> to ensure responses are grounded in the content of the <b>GIZ Gender Strategy (2019)</b>:</p>
        <ul>
            <li>Your question is <b>converted into an embedding</b> (numerical representation).</li>
            <li>The system <b>retrieves the most relevant text sections</b> from the strategy document.</li>
            <li>A <b>language model (LLM)</b> generates a response based on the retrieved content.</li>
        </ul>

        <p><b>⚠️ Important:</b> The assistant is <b>limited to the Gender Strategy (2019)</b>. It <b>does not</b> access external sources, additional policies, or updated guidelines beyond the provided document.</p>

        <h4>🎯 Example questions:</h4>
        <ul>
            <li>What are the key objectives of the Gender Strategy?</li>
            <li>How does GIZ define "gender," and what is the conceptual foundation of the strategy?</li>
            <li>How does the strategy align with the <b>2030 Agenda</b>?</li>
            <li>How is the success of the strategy measured and reviewed?</li>
        </ul>

        <h4>📌 Further resources:</h4>
        <ul>
            <li>📄 <a href="https://www.giz.de/en/downloads/giz-2019-en-gender-strategy.pdf" target="_blank" rel="noopener noreferrer"><b>GIZ Gender Strategy (2019, PDF)</b></a></li>
            <li>🌍 <a href="https://reporting.giz.de/2022/operating-responsibly/our-gender-strategy/index.html" target="_blank" rel="noopener noreferrer"><b>GIZ Gender Strategy Website</b></a></li>
            <li>📊 <a href="https://www.blog-datalab.com/" target="_blank" rel="noopener noreferrer"><b>GIZ Data Lab Blog</b></a></li>
        </ul>
        """,
        unsafe_allow_html=True
    )


st.html(
    """
    
    <p>Feel free to explore and gain deeper insights into GIZ’s commitment to gender equality! 🚀</p>
    <p><b>Now, go ahead and ask a question related to the GIZ Gender Strategy in the text field below!</b> 📝</p>
     
    """
)


if "messages" not in st.session_state:
    st.session_state.messages = []

for message in st.session_state.messages:
    with st.chat_message(name=message["role"], avatar=message["avatar"]):
        st.markdown(message["content"])

prompt = st.chat_input("Say something")
if prompt:
    with st.chat_message(name="user", avatar=":material/person:"):
        st.write(prompt)
        st.session_state.messages.append(
            {"role": "user", "content": prompt, "avatar": ":material/person:"}
        )
    with st.chat_message(name="ai", avatar=":material/smart_toy:"):
        result = rag.run(
            {"prompt_builder": {"query": prompt}, "text_embedder": {"text": prompt}},
        )
        result = result["llm"]["replies"][0]
        result = result.split("Question:")[0]
        st.write(result)
        st.session_state.messages.append(
            {"role": "ai", "content": result, "avatar": ":material/smart_toy:"}
        )