embedchain

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import queue
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import streamlit as st
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from embedchain import App
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from embedchain.config import BaseLlmConfig
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from embedchain.helpers.callbacks import (StreamingStdOutCallbackHandlerYield,
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                                          generate)
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@st.cache_resource
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def unacademy_ai():
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    app = App()
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    return app
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app = unacademy_ai()
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assistant_avatar_url = "https://cdn-images-1.medium.com/v2/resize:fit:1200/1*LdFNhpOe7uIn-bHK9VUinA.jpeg"
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st.markdown(f"# <img src='{assistant_avatar_url}' width={35} /> Unacademy UPSC AI", unsafe_allow_html=True)
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styled_caption = """
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<p style="font-size: 17px; color: #aaa;">
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🚀 An <a href="https://github.com/embedchain/embedchain">Embedchain</a> app powered with Unacademy\'s UPSC data!
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</p>
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"""
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st.markdown(styled_caption, unsafe_allow_html=True)
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with st.expander(":grey[Want to create your own Unacademy UPSC AI?]"):
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    st.write(
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        """
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    ```bash
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    pip install embedchain
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    ```
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    ```python
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    from embedchain import App
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    unacademy_ai_app = App()
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    unacademy_ai_app.add(
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        "https://unacademy.com/content/upsc/study-material/plan-policy/atma-nirbhar-bharat-3-0/",
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        data_type="web_page"
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    )
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    unacademy_ai_app.chat("What is Atma Nirbhar 3.0?")
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    ```
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    For more information, checkout the [Embedchain docs](https://docs.embedchain.ai/get-started/quickstart).
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    """
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    )
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if "messages" not in st.session_state:
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    st.session_state.messages = [
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        {
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            "role": "assistant",
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            "content": """Hi, I'm Unacademy UPSC AI bot, who can answer any questions related to UPSC preparation.
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            Let me help you prepare better for UPSC.\n
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Sample questions:
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- What are the subjects in UPSC CSE?
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- What is the CSE scholarship price amount?
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- What are different indian calendar forms?
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            """,
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        }
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    ]
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for message in st.session_state.messages:
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    role = message["role"]
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    with st.chat_message(role, avatar=assistant_avatar_url if role == "assistant" else None):
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        st.markdown(message["content"])
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if prompt := st.chat_input("Ask me anything!"):
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    with st.chat_message("user"):
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        st.markdown(prompt)
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        st.session_state.messages.append({"role": "user", "content": prompt})
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    with st.chat_message("assistant", avatar=assistant_avatar_url):
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        msg_placeholder = st.empty()
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        msg_placeholder.markdown("Thinking...")
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        full_response = ""
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        q = queue.Queue()
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        def app_response(result):
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            llm_config = app.llm.config.as_dict()
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            llm_config["callbacks"] = [StreamingStdOutCallbackHandlerYield(q=q)]
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            config = BaseLlmConfig(**llm_config)
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            answer, citations = app.chat(prompt, config=config, citations=True)
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            result["answer"] = answer
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            result["citations"] = citations
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        results = {}
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        for answer_chunk in generate(q):
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            full_response += answer_chunk
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            msg_placeholder.markdown(full_response)
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        answer, citations = results["answer"], results["citations"]
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        if citations:
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            full_response += "\n\n**Sources**:\n"
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            sources = list(set(map(lambda x: x[1], citations)))
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            for i, source in enumerate(sources):
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                full_response += f"{i+1}. {source}\n"
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        msg_placeholder.markdown(full_response)
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        st.session_state.messages.append({"role": "assistant", "content": full_response})
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