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 = ["Epikur", "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", "Hannah Arendt", "Albert Camus", "Mark Fisher"]

@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=20,
        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', ''),
            "book": metadata.get('book', ''),
            "section": metadata.get('section', ''),
            "title": metadata.get('title', ''),
            "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 = """
/* Remove outer padding, margins, and borders */
gradio-app,
gradio-app > div,
gradio-app .gradio-container {
    padding: 0 !important;
    margin: 0 !important;
    border: none !important;
}

/* Remove any potential outlines */
gradio-app:focus,
gradio-app > div:focus,
gradio-app .gradio-container:focus {
    outline: none !important;
}

/* Ensure full width */
gradio-app {
    width: 100% !important;
    display: block !important;
}

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

@media (max-width: 768px) {
    gradio-app, 
    gradio-app > div,
    gradio-app .gradio-container {
        padding-left: 1px !important;
        padding-right: 1px !important;
    }
    .custom-markdown {
        padding: 5px;
    }
    .accordion {
        margin-left: -10px;
        margin-right: -10px;
    }
}
"""

with gr.Blocks(theme=theme, css=custom_css) as demo:
    gr.Markdown("Geben Sie ein, wonach Sie suchen möchten (Query), trennen Sie mehrere Suchanfragen durch Semikola; filtern Sie nach Autoren (ohne Auswahl werden alle durchsucht) und klicken Sie auf **Suchen**, um zu suchen.")
    #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", lines=3, 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, elem_id="loading-indicator")
    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 = "Suche den zur Frage passenden Text"
        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: ("", 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, elem_classes="accordion") as acc:
                for result in res:
                    with gr.Column():
                        author = str(result.get('author', ''))
                        book = str(result.get('book', ''))
                        section = str(result.get('section', ''))
                        title = str(result.get('title', ''))
                        text = str(result.get('text', ''))

                        header_parts = []
                        if author and author != "Unknown":
                            header_parts.append(author)
                        if book and book != "Unknown":
                            header_parts.append(book)
                        if section and section != "Unknown":
                            header_parts.append(section)
                        if title and title != "Unknown":
                            header_parts.append(title)
                        
                        header = ", ".join(header_parts)
                        markdown_contents = f"**{header}**\n\n{text}"
                        gr.Markdown(value=markdown_contents, elem_classes="custom-markdown")

demo.launch(inline=False)