app.py

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


theme = gr.themes.Soft(
    primary_hue="indigo",
    secondary_hue="slate",
    neutral_hue="slate",
    spacing_size="lg",
    radius_size="lg",
    text_size="lg",
    font=["Helvetica", "sans-serif"],
    font_mono=["Courier", "monospace"],
).set(
    body_text_color="*neutral_800",
    block_background_fill="*neutral_50",
    block_border_width="0px",
    button_primary_background_fill="*primary_600",
    button_primary_background_fill_hover="*primary_700",
    button_primary_text_color="white",
    input_background_fill="white",
    input_border_color="*neutral_200",
    input_border_width="1px",
    checkbox_background_color_selected="*primary_600",
    checkbox_border_color_selected="*primary_600",
)

custom_css = """
.custom-markdown { 
    border: 1px solid var(--neutral-200); 
    padding: 15px; 
    border-radius: var(--radius-lg);
    background-color: var(--color-background-primary);
    margin-bottom: 15px;
}
.custom-markdown p {
    margin-bottom: 10px;
    line-height: 1.6;
}
"""

with gr.Blocks(theme=theme, css=custom_css) as demo:
    gr.Markdown("Geben Sie ein, wonach Sie suchen möchten (Query), filtern Sie nach Autoren (ohne Auswahl werden alle durchsucht) und klicken Sie auf **Suchen**, um zu suchen. Trennen Sie mehrere Fragen durch Semikola; die Suche dauert, unabhängig von der Anzahl der Abfragen, etwa 40 Sekunden, da das Embeddingmodell jedes Mal auf eine GPU geladen werden muss.")
    #database_inp = gr.Dropdown(label="Database", choices=["German", "English"], value="German")
    author_inp = gr.Dropdown(label="Autoren", choices=authors_list_de, multiselect=True)
    inp = gr.Textbox(label="Query", placeholder="Wie kann ich gesund leben?; Wie kann ich mich besser konzentrieren?; Was ist der Sinn des Lebens?; ...")
    btn = gr.Button("Suchen")
    loading_indicator = gr.Markdown(visible=False)
    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):
        task = "Given a question, retrieve passages that answer the question"
        queries = [query.strip() for query in queries.split(';')]
        embeddings = get_embeddings(queries, task)
        collection = collection_de
        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(
        fn=lambda: ("Searching... This may take up to 40 seconds.", gr.update(visible=True)),
        inputs=None,
        outputs=[loading_indicator, loading_indicator],
        queue=False
    ).then(
        perform_query,
        inputs=[inp, author_inp],
        outputs=[results, loading_indicator]
    )

    @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()