import gradio as gr
import torch
import numpy as np
import h5py
import faiss
from PIL import Image
import io
import pickle
import random
import click


def getRandID():
    indx = random.randrange(0, len(index_to_id_dict))
    return index_to_id_dict[indx], indx


def get_image_index(indexType):
    if indexType == "FlatIP(default)":
        return image_index_IP
    elif indexType == "FlatL2":
        raise NotImplementedError
        return image_index_L2
    elif indexType == "HNSWFlat":
        raise NotImplementedError
        return image_index_HNSW
    elif indexType == "IVFFlat":
        raise NotImplementedError
        return image_index_IVF
    elif indexType == "LSH":
        raise NotImplementedError
        return image_index_LSH


def get_dna_index(indexType):
    if indexType == "FlatIP(default)":
        return dna_index_IP
    elif indexType == "FlatL2":
        raise NotImplementedError
        return dna_index_L2
    elif indexType == "HNSWFlat":
        raise NotImplementedError
        return dna_index_HNSW
    elif indexType == "IVFFlat":
        raise NotImplementedError
        return dna_index_IVF
    elif indexType == "LSH":
        raise NotImplementedError
        return dna_index_LSH


def searchEmbeddings(id, key_type, query_type, index_type, num_results: int = 10):
    image_index = get_image_index(index_type)
    dna_index = get_dna_index(index_type)

    # get index
    if query_type == "Image":
        query = image_index.reconstruct(id_to_index_dict[id])
    elif query_type == "DNA":
        query = dna_index.reconstruct(id_to_index_dict[id])
    else:
        raise ValueError(f"Invalid query type: {query_type}")
    query = query.astype(np.float32)
    query = np.expand_dims(query, axis=0)

    # search for query
    if key_type == "Image":
        index = image_index
    elif key_type == "DNA":
        index = dna_index
    else:
        raise ValueError(f"Invalid key type: {key_type}")

    _, I = index.search(query, num_results)

    closest_ids = []
    for indx in I[0]:
        id = index_to_id_dict[indx]
        closest_ids.append(id)

    return closest_ids


with gr.Blocks() as demo:

    # for hf: change all file paths, indx_to_id_dict as well

    # load indexes
    image_index_IP = faiss.read_index("bioscan_5m_image_IndexFlatIP.index")
    # image_index_L2 = faiss.read_index("big_image_index_FlatL2.index")
    # image_index_HNSW = faiss.read_index("big_image_index_HNSWFlat.index")
    # image_index_IVF = faiss.read_index("big_image_index_IVFFlat.index")
    # image_index_LSH = faiss.read_index("big_image_index_LSH.index")

    dna_index_IP = faiss.read_index("bioscan_5m_dna_IndexFlatIP.index")
    # dna_index_L2 = faiss.read_index("big_dna_index_FlatL2.index")
    # dna_index_HNSW = faiss.read_index("big_dna_index_HNSWFlat.index")
    # dna_index_IVF = faiss.read_index("big_dna_index_IVFFlat.index")
    # dna_index_LSH = faiss.read_index("big_dna_index_LSH.index")

    # with open("dataset_processid_list.pickle", "rb") as f:
    #     dataset_processid_list = pickle.load(f)
    # with open("processid_to_index.pickle", "rb") as f:
    #     processid_to_index = pickle.load(f)
    with open("big_indx_to_id_dict.pickle", "rb") as f:
        index_to_id_dict = pickle.load(f)
    id_to_index_dict = {v: k for k, v in index_to_id_dict.items()}

    with gr.Column():
        with gr.Row():
            with gr.Column():
                rand_id = gr.Textbox(label="Random ID:")
                rand_id_indx = gr.Textbox(label="Index:")
                id_btn = gr.Button("Get Random ID")
            with gr.Column():
                key_type = gr.Radio(choices=["Image", "DNA"], label="Search From:", value="Image")
                query_type = gr.Radio(choices=["Image", "DNA"], label="Search To:", value="Image")

        index_type = gr.Radio(
            choices=["FlatIP(default)", "FlatL2", "HNSWFlat", "IVFFlat", "LSH"], label="Index:", value="FlatIP(default)"
        )
        num_results = gr.Number(label="Number of Results:", value=10, precision=0)

        process_id = gr.Textbox(label="ID:", info="Enter a sample ID to search for")
        process_id_list = gr.Textbox(label="Closest 10 matches:")
        search_btn = gr.Button("Search")
        id_btn.click(fn=getRandID, inputs=[], outputs=[rand_id, rand_id_indx])

    search_btn.click(
        fn=searchEmbeddings,
        inputs=[process_id, key_type, query_type, index_type, num_results],
        outputs=[process_id_list],
    )


demo.launch()