import os
import gradio as gr
from local_graphs import get_graph, recommend_dijkstra, recommend_floyd_warshall, recommend_page_rank, get_page_rank_graph
import pandas as pd


def img_path(img_name):
    return os.path.join("images", img_name)


def greet(name, algorithm, n_recommendations):
    recommended_paths = []

    G = get_graph()

    if algorithm == "Dijkstra":
        recommended_paths = recommend_dijkstra(G, name)
    elif algorithm == "Floyd-Warshall":
        recommended_paths = recommend_floyd_warshall(G, name)

    recommended_paths = recommended_paths[:n_recommendations]

    recommended_paths = [img_path(path) for path, _ in recommended_paths]

    return img_path(name), recommended_paths, pd.DataFrame(recommended_paths, columns=['image_path'])


interface = gr.Interface(
    fn=greet,
    inputs=[
        gr.Textbox(
            label="Nome",
            info="Informe o nome (id) da imagem",
            value="zh/2240px-Brazil_State_RiodeJaneiro.svg.png",
            placeholder="zh/2240px-Brazil_State_RiodeJaneiro.svg.png"),
        gr.Dropdown(
            ["Dijkstra", "Floyd-Warshall"],
            label="Algoritmo",
            info="Selecione o algoritmo de recomendação",
            value="Dijkstra"),
        gr.Slider(1, 100, label="Número de recomendações", value=5, step=1)],
    outputs=[
        gr.Image(label="Imagem base"),
        gr.Gallery(label="Recomendações"),
        gr.Dataframe(label="Paths recomendados"),
    ])

if __name__ == "__main__":
    interface.launch(show_api=False)