import gradio as gr
import pandas as pd
import numpy as np
from sklearn.manifold import TSNE
from sentence_transformers import SentenceTransformer, util
import torch
import plotly.express as px


embedder = SentenceTransformer('all-MiniLM-L6-v2')


df = pd.DataFrame(columns=['Idea', 'Author', 'Embeddings'])


def add_idea(user, idea, history, df):
    embedded_idea = embedder.encode(idea, convert_to_tensor=False)
    new_row = {'Idea': idea, 'Author': user, 'Embeddings': embedded_idea}
    df.loc[len(df)] = new_row
    print(len(df))
    print(df)
    history += "\n Idea : {} \n".format(idea)
    return f"{history}\n", df

def map_ideas(df):
    emb_list = np.array(list(df.Embeddings))
    emb2D2 = TSNE(n_components=2, random_state = 42, perplexity=3).fit_transform(emb_list)
    df["x"], df["y"] = [emb2D2[:, 0].tolist(), emb2D2[:, 1].tolist()]
    print(df)
    
    fig = px.scatter(
        df, x='x', y='y',
        title='Description samples map',
        color='Author',
        opacity=0.7,
        text='Idea')
    
    fig.update_traces(marker=dict(symbol='square', size=19), textfont=dict(size=10))
    
    fig.update_layout(hovermode=False)

    return fig



with gr.Blocks() as demo:
    gr.Markdown("Suggest your ideas")
    username = gr.Textbox(label='Name')
    idea = gr.Textbox(label='Idea')
    df_state = gr.State(value=df)
    with gr.Row():
        btn_idea = gr.Button("Send Idea")
        btn_map = gr.Button("Map Ideas")
    history = gr.Markdown(label="Your submitted ideas")
    scattered = gr.Plot().style()


    btn_idea.click(add_idea, inputs=[username, idea, history, df_state], outputs=[history, df_state])
    btn_map.click(map_ideas,inputs=df_state, outputs=scattered)


demo.queue(concurrency_count=10)
demo.launch(debug=False,share=False)