import gradio
import pandas as pd
import concurrent.futures

from App.tfidfrecommender import TfidfRecommender

import gradio as gr

desc = pd.read_csv('App/data/descriptions.csv')

rec = TfidfRecommender(desc, 'id', 'description' , "none")
def initialize_and_tokenize(tokenizer):
    print("tok")
    rec.tokenization_method = tokenizer
    rec.tokenize_text()

names = []
def recommend (movies, tok) :
    rec.tokenization_method = tok
    tf, vecs = rec.tokenize_text()
    rec.fit(tf, vecs)
    print("rec")
    pool = concurrent.futures.ThreadPoolExecutor(max_workers=10)
    futures = [pool.submit(rec.recommend_k_items, movie, 5) for movie in movies]
    idss = []
    print("after submit")
    for i in range(len(futures)):
        print("res")
        idss.append(futures[i].result())
    print("shutdown")
    pool.shutdown(wait=True)
    ids = [id for ids in idss for id in ids]
    ids = list(set(ids))
    names = desc[desc['id'].isin(ids)]['title'].to_list()
    return ', '.join(names)

def recom(movies, tok):
    rec.tokenization_method = tok
    tf, vecs = rec.tokenize_text()
    rec.fit(tf, vecs)
    print(movies[0])
    ids = rec.recommend_k_items(movies[0], 5)
    print("reccc")
    # ids = list(set(ids))
    names = desc[desc['id'].isin(ids)]['title'].to_list()
    return ', '.join(names)

demo = gr.Interface(fn=recom, 
             inputs=[gr.Dropdown(choices = list(desc['title'][:20]), multiselect=True, max_choices=3, label="Movies"), 
                     gr.Radio(["bert", "scibert", "nltk" , "none"], value="none", label="Tokenization and text preprocess")], 
             outputs=gr.Textbox(label="Recommended"))
demo.launch()


# ===========================
# with gr.Blocks() as demo:
#     gr.Markdown("Start typing below and then click **Run** to see the output.")
#     with gr.Row():
#         radio = gr.Radio(["bert", "scibert", "nltk" , "none"], value="none", 
#                          label="Tokenization and text preprocess")
#         btn = gr.Button("Tokenize and Preprocess")
#         btn.click(fn=initialize_and_tokenize, inputs=radio)
# # demo.launch()
# # with gr.Blocks() as demo2:
#     gr.Markdown("Choose 3 movies")
#     with gr.Row():
#         dropdown = gr.Dropdown(choices = list(desc['title']), multiselect=True, max_choices=3,
#                                label="Movies")
#         box = gr.Textbox(lines=3, label="recs")
#         btn2 = gr.Button("Recommend")
#         btn2.click(fn=recommend, inputs=dropdown,outputs=[])
#         gr.Markdown("rec{}".format(len(names)))
# demo.launch()

# ==========================

# with gr.Blocks() as demo :
#     gr.Markdown("Start typing below and then click **Run** to see the output.")
#     with gr.Row():
#         radio = gr.Radio(["bert", "scibert", "nltk" , "none"], value="none", 
#                          label="Tokenization and text preprocess")
#         btn = gr.Button("Tokenize and Preprocess")
#         btn.click(fn=initialize_and_tokenize, inputs=radio, outputs=[])
# demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")