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| 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") | |