import os
import gradio as gr
import chromadb
from sentence_transformers import SentenceTransformer
import spaces

client = chromadb.PersistentClient(path="./chroma")
collection_de = client.get_collection(name="phil_de")
collection_en = client.get_collection(name="phil_en")
authors_list_de = ["Ludwig Wittgenstein", "Sigmund Freud", "Marcus Aurelius", "Friedrich Nietzsche", "Epiktet", "Ernst Jünger", "Georg Christoph Lichtenberg", "Balthasar Gracian", "Hannah Arendt", "Erich Fromm", "Albert Camus"]
authors_list_en = ["Friedrich Nietzsche", "Joscha Bach"]

@spaces.GPU
def get_embeddings(queries, task):
    model = SentenceTransformer("Linq-AI-Research/Linq-Embed-Mistral", use_auth_token=os.getenv("HF_TOKEN"))
    prompts = [f"Instruct: {task}\nQuery: {query}" for query in queries]
    query_embeddings = model.encode(prompts)  
    return query_embeddings

def query_chroma(collection, embedding, authors):
    results = collection.query(
        query_embeddings=[embedding.tolist()],
        n_results=10,
        where={"author": {"$in": authors}} if authors else {},
        include=["documents", "metadatas", "distances"]
    )

    ids = results.get('ids', [[]])[0]
    metadatas = results.get('metadatas', [[]])[0]
    documents = results.get('documents', [[]])[0]
    distances = results.get('distances', [[]])[0]

    formatted_results = []
    for id_, metadata, document_text, distance in zip(ids, metadatas, documents, distances):
        result_dict = {
            "id": id_,
            "author": metadata.get('author', 'Unknown author'),
            "book": metadata.get('book', 'Unknown book'),
            "section": metadata.get('section', 'Unknown section'),
            "title": metadata.get('title', 'Untitled'),
            "text": document_text,
            "distance": distance
        }
        formatted_results.append(result_dict)

    return formatted_results

with gr.Blocks(css=".custom-markdown { border: 1px solid #ccc; padding: 10px; border-radius: 5px; }")  as demo:
    gr.Markdown("Enter your query, filter authors (default is all), click **Search** to search. Delimit multiple queries with semicola; since there is a quota for each user (based on IP) it makes sense to query in batches. The search takes around 50 seconds because the embedding model needs to be loaded to a GPU each time (another reason to do multiple queries at once).")
    database_inp = gr.Dropdown(label="Database", choices=["German", "English"], value="German")
    author_inp = gr.Dropdown(label="Authors", choices=authors_list_de, multiselect=True)
    inp = gr.Textbox(label="Query", placeholder="Wie kann ich gesund leben und bedeutet Gesundheit für jeden das gleiche?; Was ist der Sinn des Lebens?; ...")
    btn = gr.Button("Search")
    results = gr.State()

    def update_authors(database):
        return gr.update(choices=authors_list_de if database == "German" else authors_list_en)

    database_inp.change(
        fn=lambda database: update_authors(database),
        inputs=[database_inp],
        outputs=[author_inp]
    )

    def perform_query(queries, authors, database):
        task = "Given a question, retrieve passages that answer the question"
        queries = queries.split(';')
        embeddings = get_embeddings(queries, task)
        collection = collection_de if database == "German" else collection_en
        results_data = []
        for query, embedding in zip(queries, embeddings):
            res = query_chroma(collection, embedding, authors)
            results_data.append((query, res))
        return results_data

    btn.click(
        perform_query,
        inputs=[inp, author_inp, database_inp],
        outputs=[results]
    )

    @gr.render(inputs=[results])
    def display_accordion(data):
        for query, res in data:
            with gr.Accordion(query, open=False) as acc:
                for result in res:
                    with gr.Column():
                        author = result.get('author', 'Unknown author')
                        book = result.get('book', 'Unknown book')
                        text = result.get('text')
                        markdown_contents = f"**{author}, {book}**\n\n{text}"
                        gr.Markdown(value=markdown_contents, elem_classes="custom-markdown")

demo.launch()