import gradio as gr
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
import torch


def create_miread_embed(sents, bundle):
    tokenizer = bundle[0]
    model = bundle[1]
    model.cpu()
    tokens = tokenizer(sents,
                       max_length=512,
                       padding=True,
                       truncation=True,
                       return_tensors="pt"
                       )
    device = torch.device('cpu')
    tokens = tokens.to(device)
    with torch.no_grad():
        out = model.bert(**tokens)
        feature = out.last_hidden_state[:, 0, :]
    return feature.cpu()


def get_matches(query):
    matches = vecdb.similarity_search_with_score(query, k=60)
    return matches


def inference(query):
    matches = get_matches(query)
    auth_counts = {}
    j_bucket = {}
    n_table = []
    a_table = []
    scores = [round(match[1].item(), 3) for match in matches]
    min_score = min(scores)
    max_score = max(scores)
    def normaliser(x): return round(1 - (x-min_score)/max_score, 3)
    for i, match in enumerate(matches):
        doc = match[0]
        score = round(normaliser(round(match[1].item(), 3)), 3)
        title = doc.metadata['title']
        author = eval(doc.metadata['authors'])[0].title()
        date = doc.metadata.get('date', 'None')
        link = doc.metadata.get('link', 'None')
        submitter = doc.metadata.get('submitter', 'None')
        # journal = doc.metadata.get('journal', 'None').strip()
        journal = doc.metadata['journal']
        if (journal is None or journal.strip() == ''):
            journal = 'None'
        else:
            journal = journal.strip()
        # For journals
        if journal not in j_bucket:
            j_bucket[journal] = score
        else:
            j_bucket[journal] += score

        # For authors
        record = [i+1,
                  score,
                  author,
                  title,
                  link,
                  date]
        if auth_counts.get(author, 0) < 2:
            n_table.append(record)
            if auth_counts.get(author, 0) == 0:
                auth_counts[author] = 1
            else:
                auth_counts[author] += 1

        # For abstracts
        record = [i+1,
                  title,
                  author,
                  submitter,
                  journal,
                  date,
                  link,
                  score
                  ]
        a_table.append(record)

    if j_bucket.get('None',None):
        del j_bucket['None']
    j_table = sorted([[journal, round(score, 3)] for journal,
                     score in j_bucket.items()],
                     key=lambda x: x[1], reverse=True)
    j_table = [[i+1, item[0], item[1]] for i, item in enumerate(j_table)]
    j_output = gr.Dataframe.update(value=j_table, visible=True)
    n_output = gr.Dataframe.update(value=n_table, visible=True)
    a_output = gr.Dataframe.update(value=a_table, visible=True)

    return [a_output, j_output, n_output]


model_name = "biodatlab/MIReAD-Neuro-Contrastive"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
faiss_embedder = HuggingFaceEmbeddings(
    model_name=model_name,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs
)

vecdb = FAISS.load_local("medline_contr", faiss_embedder)


with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# NBDT Recommendation Engine for Editors")
    gr.Markdown("NBDT Recommendation Engine for Editors is a tool for neuroscience authors/abstracts/journalsrecommendation built for NBDT journal editors. \
    It aims to help an editor to find similar reviewers, abstracts, and journals to a given submitted abstract.\
    To find a recommendation, paste a `title[SEP]abstract` or `abstract` in the text box below and click \"Find Matches\".\
    Then, you can hover to authors/abstracts/journals tab to find a suggested list.\
    The data in our current demo includes authors associated with the NBDT Journal. We will update the data monthly for an up-to-date publications.")

    abst = gr.Textbox(label="Abstract", lines=10)

    action_btn = gr.Button(value="Find Matches")

    with gr.Tab("Authors"):
        n_output = gr.Dataframe(
            headers=['No.', 'Score', 'Name', 'Title', 'Link', 'Date'],
            datatype=['number', 'number', 'str', 'str', 'str', 'str'],
            col_count=(6, "fixed"),
            wrap=True,
            visible=False
        )
    with gr.Tab("Abstracts"):
        a_output = gr.Dataframe(
            headers=['No.', 'Title', 'Author', 'Corresponding Author',
                     'Journal', 'Date', 'Link', 'Score'],
            datatype=['number', 'str', 'str', 'str',
                      'str', 'str', 'str', 'number'],
            col_count=(8, "fixed"),
            wrap=True,
            visible=False
        )
    with gr.Tab("Journals"):
        j_output = gr.Dataframe(
            headers=['No.', 'Name', 'Score'],
            datatype=['number', 'str', 'number'],
            col_count=(3, "fixed"),
            wrap=True,
            visible=False
        )

    action_btn.click(fn=inference,
                     inputs=[
                         abst,
                     ],
                     outputs=[a_output, j_output, n_output],
                     api_name="neurojane")

demo.launch(debug=True)