import json  # For stringifying a dict

import gradio  # GUI framework
import datasets  # Used to load publication dataset

import numpy  # For a few simple matrix operations
import pandas  # Needed for operating on dataset
import sentence_transformers  # Needed for query embedding
import faiss  # Needed for fast similarity search

# Load the dataset and convert to pandas
full_data = datasets.load_dataset("ccm/publications")["train"].to_pandas()

# Define the base URL for Google Scholar
SCHOLAR_URL = "https://scholar.google.com"

# Filter out any publications without an abstract
filter = [
    '"abstract": null' in json.dumps(bibdict)
    for bibdict in full_data["bib_dict"].values
]
data = full_data[~pandas.Series(filter)]
data.reset_index(inplace=True)

# Create a FAISS index for fast similarity search
index = faiss.IndexFlatL2(len(data["embedding"][0]))
index.metric_type = faiss.METRIC_INNER_PRODUCT
vectors = numpy.stack(data["embedding"].tolist(), axis=0)
faiss.normalize_L2(vectors)
index.train(vectors)
index.add(vectors)

# Load the model for later use in embeddings
model = sentence_transformers.SentenceTransformer("allenai-specter")


# Define the search function
def search(query: str, k: int):
    query = numpy.expand_dims(model.encode(query), axis=0)
    faiss.normalize_L2(query)
    D, I = index.search(query, k)
    top_five = data.loc[I[0]]
    search_results = ""

    for i in range(k):
        search_results += "### " + top_five["bib_dict"].values[i]["title"] + "\n\n"
        search_results += str("{:d}% relevant  ".format(100*D[0][i]))
        if top_five["pub_url"].values[i] is not None:
            search_results += "[Full Text](" + top_five["pub_url"].values[i] + ")  "
        if top_five["citedby_url"].values[i] is not None:
            search_results += (
                "[Cited By](" + SCHOLAR_URL + top_five["citedby_url"].values[i] + ")  "
            )
        if top_five["url_related_articles"].values[i] is not None:
            search_results += (
                "[Related Articles]("
                + SCHOLAR_URL
                + top_five["url_related_articles"].values[i]
                + ")  "
            )
        search_results += "\n\n```bibtex\n"
        search_results += (
            json.dumps(top_five["bibtex"].values[i], indent=4)
            .replace("\\n", "\n")
            .replace("\\t", "\t")
            .strip('"')
        )
        search_results += "```\n"
    return search_results


with gradio.Blocks() as demo:
    with gradio.Group():
        query = gradio.Textbox(
            placeholder="Enter search terms...", show_label=False, lines=1, max_lines=1
        )
        with gradio.Accordion("Settings", open=False):
            k = gradio.Number(5.0, label="Number of results", precision=0)
    results = gradio.Markdown()
    query.change(fn=search, inputs=[query, k], outputs=results)
    k.change(fn=search, inputs=[query, k], outputs=results)

demo.launch(debug=True)