"""
"""

from collections import defaultdict
import json
import os
import re

from langchain_core.documents import Document
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableParallel
from langchain_core.runnables import RunnablePassthrough
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_community.vectorstores.utils import DistanceStrategy
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_together import ChatTogether
from langchain_pinecone import PineconeVectorStore
import streamlit as st

import usage


st.set_page_config(layout="wide", page_title="LegisQA")
os.environ["LANGCHAIN_API_KEY"] = st.secrets["langchain_api_key"]
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_PROJECT"] = st.secrets["langchain_project"]
os.environ["TOKENIZERS_PARALLELISM"] = "false"


SS = st.session_state
SEED = 292764
CONGRESS_NUMBERS = [113, 114, 115, 116, 117, 118]
SPONSOR_PARTIES = ["D", "R", "L", "I"]
CONGRESS_GOV_TYPE_MAP = {
    "hconres": "house-concurrent-resolution",
    "hjres": "house-joint-resolution",
    "hr": "house-bill",
    "hres": "house-resolution",
    "s": "senate-bill",
    "sconres": "senate-concurrent-resolution",
    "sjres": "senate-joint-resolution",
    "sres": "senate-resolution",
}
OPENAI_CHAT_MODELS = {
    "gpt-4o-mini": {"cost": {"pmi": 0.15, "pmo": 0.60}},
    "gpt-4o": {"cost": {"pmi": 5.00, "pmo": 15.0}},
}
ANTHROPIC_CHAT_MODELS = {
    "claude-3-haiku-20240307": {"cost": {"pmi": 0.25, "pmo": 1.25}},
    "claude-3-5-sonnet-20240620": {"cost": {"pmi": 3.00, "pmo": 15.0}},
    "claude-3-opus-20240229": {"cost": {"pmi": 15.0, "pmo": 75.0}},
}
TOGETHER_CHAT_MODELS = {
    "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo": {"cost": {"pmi": 0.18, "pmo": 0.18}},
    "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": {
        "cost": {"pmi": 0.88, "pmo": 0.88}
    },
    "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo": {
        "cost": {"pmi": 5.00, "pmo": 5.00}
    },
}

PROVIDER_MODELS = {
    "OpenAI": OPENAI_CHAT_MODELS,
    "Anthropic": ANTHROPIC_CHAT_MODELS,
    "Together": TOGETHER_CHAT_MODELS,
}


def get_sponsor_url(bioguide_id: str) -> str:
    return f"https://bioguide.congress.gov/search/bio/{bioguide_id}"


def get_congress_gov_url(congress_num: int, legis_type: str, legis_num: int) -> str:
    lt = CONGRESS_GOV_TYPE_MAP[legis_type]
    return f"https://www.congress.gov/bill/{int(congress_num)}th-congress/{lt}/{int(legis_num)}"


def load_bge_embeddings():
    model_name = "BAAI/bge-small-en-v1.5"
    model_kwargs = {"device": "cpu"}
    encode_kwargs = {"normalize_embeddings": True}
    emb_fn = HuggingFaceBgeEmbeddings(
        model_name=model_name,
        model_kwargs=model_kwargs,
        encode_kwargs=encode_kwargs,
        query_instruction="Represent this question for searching relevant passages: ",
    )
    return emb_fn


def load_pinecone_vectorstore():
    emb_fn = load_bge_embeddings()
    vectorstore = PineconeVectorStore(
        embedding=emb_fn,
        text_key="text",
        distance_strategy=DistanceStrategy.COSINE,
        pinecone_api_key=st.secrets["pinecone_api_key"],
        index_name=st.secrets["pinecone_index_name"],
    )
    return vectorstore


def render_outreach_links():
    nomic_base_url = "https://atlas.nomic.ai/data/gabrielhyperdemocracy"
    nomic_map_name = "us-congressional-legislation-s1024o256nomic-1"
    nomic_url = f"{nomic_base_url}/{nomic_map_name}/map"
    hf_url = "https://huggingface.co/hyperdemocracy"
    pc_url = "https://www.pinecone.io/blog/serverless"
    together_url = "https://www.together.ai/"
    st.subheader(":brain: About [hyperdemocracy](https://hyperdemocracy.us)")
    st.subheader(f":world_map: Visualize [nomic atlas]({nomic_url})")
    st.subheader(f":hugging_face: Raw [huggingface datasets]({hf_url})")
    st.subheader(f":evergreen_tree: Index [pinecone serverless]({pc_url})")
    st.subheader(f":pancakes: Inference [together.ai]({together_url})")


def render_sidebar():

    with st.container(border=True):
        render_outreach_links()


def group_docs(docs) -> list[tuple[str, list[Document]]]:
    doc_grps = defaultdict(list)

    # create legis_id groups
    for doc in docs:
        doc_grps[doc.metadata["legis_id"]].append(doc)

    # sort docs in each group by start index
    for legis_id in doc_grps.keys():
        doc_grps[legis_id] = sorted(
            doc_grps[legis_id],
            key=lambda x: x.metadata["start_index"],
        )

    # sort groups by number of docs
    doc_grps = sorted(
        tuple(doc_grps.items()),
        key=lambda x: -len(x[1]),
    )

    return doc_grps


def format_docs(docs: list[Document]) -> str:
    """JSON grouped"""

    doc_grps = group_docs(docs)
    out = []
    for legis_id, doc_grp in doc_grps:
        dd = {
            "legis_id": doc_grp[0].metadata["legis_id"],
            "title": doc_grp[0].metadata["title"],
            "introduced_date": doc_grp[0].metadata["introduced_date"],
            "sponsor": doc_grp[0].metadata["sponsor_full_name"],
            "snippets": [doc.page_content for doc in doc_grp],
        }
        out.append(dd)
    return json.dumps(out, indent=4)


def escape_markdown(text: str) -> str:
    MD_SPECIAL_CHARS = r"\`*_{}[]()#+-.!$"
    for char in MD_SPECIAL_CHARS:
        text = text.replace(char, "\\" + char)
    return text


def get_vectorstore_filter(ret_config: dict) -> dict:
    vs_filter = {}
    if ret_config["filter_legis_id"] != "":
        vs_filter["legis_id"] = ret_config["filter_legis_id"]
    if ret_config["filter_bioguide_id"] != "":
        vs_filter["sponsor_bioguide_id"] = ret_config["filter_bioguide_id"]
    vs_filter = {
        **vs_filter,
        "congress_num": {"$in": ret_config["filter_congress_nums"]},
    }
    vs_filter = {
        **vs_filter,
        "sponsor_party": {"$in": ret_config["filter_sponsor_parties"]},
    }
    return vs_filter


def render_doc_grp(legis_id: str, doc_grp: list[Document]):
    first_doc = doc_grp[0]

    congress_gov_url = get_congress_gov_url(
        first_doc.metadata["congress_num"],
        first_doc.metadata["legis_type"],
        first_doc.metadata["legis_num"],
    )
    congress_gov_link = f"[congress.gov]({congress_gov_url})"

    ref = "{} chunks from {}\n\n{}\n\n{}\n\n[{} ({}) ]({})".format(
        len(doc_grp),
        first_doc.metadata["legis_id"],
        first_doc.metadata["title"],
        congress_gov_link,
        first_doc.metadata["sponsor_full_name"],
        first_doc.metadata["sponsor_bioguide_id"],
        get_sponsor_url(first_doc.metadata["sponsor_bioguide_id"]),
    )
    doc_contents = [
        "[start_index={}] ".format(int(doc.metadata["start_index"])) + doc.page_content
        for doc in doc_grp
    ]
    with st.expander(ref):
        st.write(escape_markdown("\n\n...\n\n".join(doc_contents)))


def legis_id_to_link(legis_id: str) -> str:
    congress_num, legis_type, legis_num = legis_id.split("-")
    return get_congress_gov_url(congress_num, legis_type, legis_num)


def legis_id_match_to_link(matchobj):
    mstring = matchobj.string[matchobj.start() : matchobj.end()]
    url = legis_id_to_link(mstring)
    link = f"[{mstring}]({url})"
    return link


def replace_legis_ids_with_urls(text):
    pattern = "11[345678]-[a-z]+-\d{1,5}"
    rtext = re.sub(pattern, legis_id_match_to_link, text)
    return rtext


def render_guide():

    st.write(
        """
When you send a query to LegisQA, it will attempt to retrieve relevant content from the past six congresses ([113th-118th](https://en.wikipedia.org/wiki/List_of_United_States_Congresses)) covering 2013 to the present, pass it to a [large language model (LLM)](https://en.wikipedia.org/wiki/Large_language_model), and generate a response. This technique is known as Retrieval Augmented Generation (RAG). You can read [an academic paper](https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html) or [a high level summary](https://research.ibm.com/blog/retrieval-augmented-generation-RAG) to get more details. Once the response is generated, the retrieved content will be available for inspection with links to the bills and sponsors.


## Disclaimer

This is a research project. The RAG technique helps to ground the LLM response by providing context from a trusted source, but it does not guarantee a high quality response. We encourage you to play around, find questions that work and find questions that fail. There is a small monthly budget dedicated to the OpenAI endpoints. Once that is used up each month, queries will no longer work.


## Config

Use the `Generative Config` to change LLM parameters.
Use the `Retrieval Config` to change the number of chunks retrieved from our congress corpus and to apply various filters to the content before it is retrieved (e.g. filter to a specific set of congresses). Use the `Prompt Config` to try out different document formatting and prompting strategies.

    """
    )


def render_example_queries():

    with st.expander("Example Queries"):
        st.write(
            """

```
What are the themes around artificial intelligence?
```

```
Write a well cited 3 paragraph essay on food insecurity.
```

```
Create a table summarizing major climate change ideas with columns legis_id, title, idea.
```

```
Write an action plan to keep social security solvent.
```

```
Suggest reforms that would benefit the Medicaid program.
```

        """
        )


def get_generative_config(key_prefix: str) -> dict:
    output = {}

    key = "provider"
    output[key] = st.selectbox(
        label=key, options=PROVIDER_MODELS.keys(), key=f"{key_prefix}|{key}"
    )

    key = "model_name"
    output[key] = st.selectbox(
        label=key,
        options=PROVIDER_MODELS[output["provider"]],
        key=f"{key_prefix}|{key}",
    )

    key = "temperature"
    output[key] = st.slider(
        key,
        min_value=0.0,
        max_value=2.0,
        value=0.0,
        key=f"{key_prefix}|{key}",
    )

    key = "max_output_tokens"
    output[key] = st.slider(
        key,
        min_value=1024,
        max_value=2048,
        key=f"{key_prefix}|{key}",
    )

    key = "top_p"
    output[key] = st.slider(
        key, min_value=0.0, max_value=1.0, value=0.9, key=f"{key_prefix}|{key}"
    )

    key = "should_escape_markdown"
    output[key] = st.checkbox(
        key,
        value=False,
        key=f"{key_prefix}|{key}",
    )

    key = "should_add_legis_urls"
    output[key] = st.checkbox(
        key,
        value=True,
        key=f"{key_prefix}|{key}",
    )

    return output


def get_retrieval_config(key_prefix: str) -> dict:
    output = {}

    key = "n_ret_docs"
    output[key] = st.slider(
        "Number of chunks to retrieve",
        min_value=1,
        max_value=32,
        value=8,
        key=f"{key_prefix}|{key}",
    )

    key = "filter_legis_id"
    output[key] = st.text_input("Bill ID (e.g. 118-s-2293)", key=f"{key_prefix}|{key}")

    key = "filter_bioguide_id"
    output[key] = st.text_input("Bioguide ID (e.g. R000595)", key=f"{key_prefix}|{key}")

    key = "filter_congress_nums"
    output[key] = st.multiselect(
        "Congress Numbers",
        CONGRESS_NUMBERS,
        default=CONGRESS_NUMBERS,
        key=f"{key_prefix}|{key}",
    )

    key = "filter_sponsor_parties"
    output[key] = st.multiselect(
        "Sponsor Party",
        SPONSOR_PARTIES,
        default=SPONSOR_PARTIES,
        key=f"{key_prefix}|{key}",
    )

    return output


def get_llm(gen_config: dict):

    match gen_config["provider"]:

        case "OpenAI":
            llm = ChatOpenAI(
                model=gen_config["model_name"],
                temperature=gen_config["temperature"],
                api_key=st.secrets["openai_api_key"],
                top_p=gen_config["top_p"],
                seed=SEED,
                max_tokens=gen_config["max_output_tokens"],
            )

        case "Anthropic":
            llm = ChatAnthropic(
                model_name=gen_config["model_name"],
                temperature=gen_config["temperature"],
                api_key=st.secrets["anthropic_api_key"],
                top_p=gen_config["top_p"],
                max_tokens_to_sample=gen_config["max_output_tokens"],
            )

        case "Together":
            llm = ChatTogether(
                model=gen_config["model_name"],
                temperature=gen_config["temperature"],
                max_tokens=gen_config["max_output_tokens"],
                top_p=gen_config["top_p"],
                seed=SEED,
                api_key=st.secrets["together_api_key"],
            )

        case _:
            raise ValueError()

    return llm


def create_rag_chain(llm, retriever):
    QUERY_RAG_TEMPLATE = """You are an expert legislative analyst. Use the following excerpts from US congressional legislation to respond to the user's query. The excerpts are formatted as a JSON list. Each JSON object has "legis_id", "title", "introduced_date", "sponsor", and "snippets" keys. If a snippet is useful in writing part of your response, then cite the "legis_id", "title", "introduced_date", and "sponsor" in the response. If you don't know how to respond, just tell the user.

---

Congressional Legislation Excerpts:

{context}

---

Query: {query}"""

    prompt = ChatPromptTemplate.from_messages(
        [
            ("human", QUERY_RAG_TEMPLATE),
        ]
    )

    rag_chain = (
        RunnableParallel(
            {
                "docs": retriever,
                "query": RunnablePassthrough(),
            }
        )
        .assign(context=lambda x: format_docs(x["docs"]))
        .assign(aimessage=prompt | llm)
    )

    return rag_chain


def process_query(gen_config: dict, ret_config: dict, query: str):
    vectorstore = load_pinecone_vectorstore()
    llm = get_llm(gen_config)
    vs_filter = get_vectorstore_filter(ret_config)
    retriever = vectorstore.as_retriever(
        search_kwargs={"k": ret_config["n_ret_docs"], "filter": vs_filter},
    )
    rag_chain = create_rag_chain(llm, retriever)
    response = rag_chain.invoke(query)
    return response


def display_retrieved_chunks(response):
    with st.container(border=True):
        doc_grps = group_docs(response["docs"])
        st.write(
            "Retrieved Chunks (note that you may need to 'right click' on links in the expanders to follow them)"
        )
        for legis_id, doc_grp in doc_grps:
            render_doc_grp(legis_id, doc_grp)


def display_response(
    response, model_info, provider, should_escape_markdown, should_add_legis_urls
):
    out_display = response["aimessage"].content
    if should_escape_markdown:
        out_display = escape_markdown(out_display)
    if should_add_legis_urls:
        out_display = replace_legis_ids_with_urls(out_display)

    with st.container(border=True):
        st.write("Response")
        st.info(out_display)

    usage.display_api_usage(response, model_info, provider)
    display_retrieved_chunks(response)


def render_query_rag_tab():
    key_prefix = "query_rag"
    render_example_queries()

    with st.form(f"{key_prefix}|query_form"):
        query = st.text_area(
            "Enter a query that can be answered with congressional legislation:"
        )
        cols = st.columns(2)
        with cols[0]:
            query_submitted = st.form_submit_button("Submit")
        with cols[1]:
            status_placeholder = st.empty()

    col1, col2 = st.columns(2)
    with col1:
        with st.expander("Generative Config"):
            gen_config = get_generative_config(key_prefix)
    with col2:
        with st.expander("Retrieval Config"):
            ret_config = get_retrieval_config(key_prefix)

    rkey = f"{key_prefix}|response"
    if query_submitted:
        with status_placeholder:
            with st.spinner("generating response"):
                SS[rkey] = process_query(gen_config, ret_config, query)

    if response := SS.get(rkey):
        model_info = PROVIDER_MODELS[gen_config["provider"]][gen_config["model_name"]]
        display_response(
            response,
            model_info,
            gen_config["provider"],
            gen_config["should_escape_markdown"],
            gen_config["should_add_legis_urls"],
        )

        with st.expander("Debug"):
            st.write(response)


def render_query_rag_sbs_tab():
    base_key_prefix = "query_rag_sbs"

    with st.form(f"{base_key_prefix}|query_form"):
        query = st.text_area(
            "Enter a query that can be answered with congressional legislation:"
        )
        cols = st.columns(2)
        with cols[0]:
            query_submitted = st.form_submit_button("Submit")
        with cols[1]:
            status_placeholder = st.empty()

    grp1a, grp2a = st.columns(2)

    gen_configs = {}
    ret_configs = {}
    with grp1a:
        st.header("Group 1")
        key_prefix = f"{base_key_prefix}|grp1"
        with st.expander("Generative Config"):
            gen_configs["grp1"] = get_generative_config(key_prefix)
        with st.expander("Retrieval Config"):
            ret_configs["grp1"] = get_retrieval_config(key_prefix)

    with grp2a:
        st.header("Group 2")
        key_prefix = f"{base_key_prefix}|grp2"
        with st.expander("Generative Config"):
            gen_configs["grp2"] = get_generative_config(key_prefix)
        with st.expander("Retrieval Config"):
            ret_configs["grp2"] = get_retrieval_config(key_prefix)

    grp1b, grp2b = st.columns(2)
    sbs_cols = {"grp1": grp1b, "grp2": grp2b}
    grp_names = {"grp1": "Group 1", "grp2": "Group 2"}

    for post_key_prefix in ["grp1", "grp2"]:
        with sbs_cols[post_key_prefix]:
            key_prefix = f"{base_key_prefix}|{post_key_prefix}"
            rkey = f"{key_prefix}|response"
            if query_submitted:
                with status_placeholder:
                    with st.spinner(
                        "generating response for {}".format(grp_names[post_key_prefix])
                    ):
                        SS[rkey] = process_query(
                            gen_configs[post_key_prefix],
                            ret_configs[post_key_prefix],
                            query,
                        )

            if response := SS.get(rkey):
                model_info = PROVIDER_MODELS[gen_configs[post_key_prefix]["provider"]][
                    gen_configs[post_key_prefix]["model_name"]
                ]
                display_response(
                    response,
                    model_info,
                    gen_configs[post_key_prefix]["provider"],
                    gen_configs[post_key_prefix]["should_escape_markdown"],
                    gen_configs[post_key_prefix]["should_add_legis_urls"],
                )


def main():

    st.title(":classical_building: LegisQA :classical_building:")
    st.header("Query Congressional Bills")

    with st.sidebar:
        render_sidebar()

    query_rag_tab, query_rag_sbs_tab, guide_tab = st.tabs(
        [
            "RAG",
            "RAG (side-by-side)",
            "Guide",
        ]
    )

    with query_rag_tab:
        render_query_rag_tab()

    with query_rag_sbs_tab:
        render_query_rag_sbs_tab()

    with guide_tab:
        render_guide()


if __name__ == "__main__":
    main()