import os
import requests
import gradio as gr
import chromadb
import json
import pandas as pd

from sentence_transformers import SentenceTransformer

import spaces

@spaces.GPU
def get_embeddings(text, task):
    model = SentenceTransformer("Linq-AI-Research/Linq-Embed-Mistral", use_auth_token=os.getenv("HF_TOKEN"))
    task = "Given a question, retrieve passages that answer the question"
    prompt = f"Instruct: {task}\nQuery: {text}"  # Use text here
    query_embeddings = model.encode([prompt], convert_to_tensor=True)  # Ensure it's a list
    return query_embeddings.cpu().numpy()


# Initialize a persistent Chroma client and retrieve collection
client = chromadb.PersistentClient(path="./chroma")
collection = client.get_collection(name="phil_de")

authors_list = ["Ludwig Wittgenstein", "Sigmund Freud", "Marcus Aurelius", "Friedrich Nietzsche", "Epiktet", "Ernst Jünger", "Georg Christoph Lichtenberg", "Balthasar Gracian", "Hannah Arendt", "Erich Fromm", "Albert Camus"]
#authors_list = ["Friedrich Nietzsche", "Joscha Bach"]

def query_chroma(embeddings, authors, num_results=10):
    try:
        where_filter = {"author": {"$in": authors}} if authors else {}

        results = collection.query(
            query_embeddings=[embeddings],
            n_results=num_results,
            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 f"Failed to query the database: {str(e)}"


# Main function
def perform_query(query, task, author, num_results):
    embeddings = get_embeddings(query, task)
    initial_results = query_chroma(embeddings, author, num_results)

    results = [(f"{res['author']}, {res['book']}, Distance: {res['distance']}", res['text'], res['id']) for res in initial_results]
    
    updates = []
    for meta, text, id_ in results:
        markdown_content = f"**{meta}**\n\n{text}"
        updates.append(gr.update(visible=True, value=markdown_content))
        updates.append(gr.update(visible=True, value="Flag", elem_id=f"flag-{len(updates)//2}"))
        updates.append(gr.update(visible=False, value=id_))  # Hide the ID textbox
    
    updates += [gr.update(visible=False)] * (3 * (max_textboxes - len(results) // 3))
    
    return updates


# Initialize the CSVLogger callback for flagging 
callback = gr.CSVLogger()

def flag_output(query, output_text, output_id):
    callback.flag([query, output_text, output_id])
        
# Gradio interface
max_textboxes = 30

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. Click **Flag** if a result is relevant to the query and interesting to you. Try reranking the results.")
    with gr.Row():
        with gr.Column():
            inp = gr.Textbox(label="query", placeholder="Enter thought...")
            author_inp = gr.Dropdown(label="authors", choices=authors_list, multiselect=True)
            num_results_inp = gr.Number(label="number of results", value=10, step=1, minimum=1, maximum=max_textboxes)
            btn = gr.Button("Search")

    components = []
    textboxes = []
    flag_buttons = []
    ids = []

    for _ in range(max_textboxes):
        with gr.Column() as col:
            text_out = gr.Markdown(visible=False, elem_classes="custom-markdown")
            flag_btn = gr.Button(value="Flag", visible=False)
            id_out = gr.Textbox(visible=False)
            components.extend([text_out, flag_btn, id_out])
            textboxes.append(text_out)
            flag_buttons.append(flag_btn)
            ids.append(id_out)

    callback.setup([inp] + textboxes + ids, "flagged_data_points")
        
    btn.click(
        fn=perform_query, 
        inputs=[inp, author_inp, num_results_inp],
        outputs=components
    )
    
    for i in range(0, len(components), 3):
        flag_buttons[i//3].click(
            fn=flag_output, 
            inputs=[inp, textboxes[i//3], ids[i//3]], 
            outputs=[],
            preprocess=False
        )


demo.launch()