app.py

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

@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

# Initialize a persistent Chroma client and retrieve collection
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"]

def query_chroma(collection, embedding, authors):
    try:
        where_filter = {"author": {"$in": authors}} if authors else {}
        # Directly use the embedding provided, already in list format suitable for the query
        results = collection.query(
            query_embeddings=[embedding.tolist()],  # Ensure embedding is properly formatted
            n_results=10,
            where=where_filter,
            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
    except Exception as e:
        return [{"error": str(e)}]

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



with gr.Blocks() as demo:
    gr.Markdown("Enter your query, filter authors (default is all), click **Search** to search.")
    database_inp = gr.Dropdown(label="Database", choices=["English", "German"], value="German")
    author_inp = gr.Dropdown(label="Authors", choices=authors_list_de, multiselect=True)
    inp = gr.Textbox(label="Query", placeholder="Enter questions separated by semicolons...")
    btn = gr.Button("Search")
    results = gr.State()  # Store results in a State component

    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):
        output_blocks = []
        for query, res in data:
            with gr.Accordion(query) as acc:
                if not res:
                    markdown_contents = "No results found."
                elif "error" in res[0]:
                    markdown_contents = f"Error retrieving data: {res[0]['error']}"
                else:
                    markdown_contents = "\n".join(f"**{r['author']}, {r['book']}**\n\n{r['text']}" for r in res)
                gr.Markdown(markdown_contents)


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

demo.launch()