import gensim.downloader 
import gradio as gr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
model = gensim.downloader.load("word2vec-google-news-300") #glove-wiki-gigaword-50

cache = "/home/user/app/d.tif"

# Function to reduce dimensions
def reduce_dimensions(data, method='PCA'):
    if method == 'PCA':
        model = PCA(n_components=2)
    elif method == 'TSNE':
        model = TSNE(n_components=2, learning_rate='auto', init='random', perplexity=4)
    return model.fit_transform(data)

# Plotting function
def plot_reduced_data(reduced_data, labels, title):
    plt.figure(figsize=(10, 8))
    plt.scatter(reduced_data[:, 0], reduced_data[:, 1], alpha=0.6)
    for i, label in enumerate(labels):
        plt.annotate("  " + label, (reduced_data[i, 0], reduced_data[i, 1]), fontsize=18)
    plt.title(title)
    # Data for the arrow 1
    start_point = (reduced_data[0, 0], reduced_data[0, 1])  # Starting point of the arrow
    end_point = (reduced_data[1, 0], reduced_data[1, 1])  # Ending point of the arrow
    
    # Adding an arrow 1
    plt.annotate('', xy=end_point, xytext=start_point,
                 arrowprops=dict(arrowstyle="->", color='green', lw=3))
    
    # Data for the arrow 2
    end_point = (reduced_data[-1, 0] , reduced_data[-1, 1])  # Starting point of the arrow
    start_point = (reduced_data[2, 0], reduced_data[2, 1])  # Ending point of the arrow
    
    # Adding an arrow 2
    plt.annotate('', xy=end_point, xytext=start_point,
                 arrowprops=dict(arrowstyle="->", color='green', lw=3))  
    
    plt.xlabel('Component 1')
    plt.ylabel('Component 2')
    plt.grid(True)
    plt.savefig(cache, dpi=300)

description = """
### Word Embedding Demo App
Universidade Federal de São Paulo - Escola Paulista de Medicina

The output is Word3 + (Word2 - Word1)

Credits:  
* Gensim
* Word2Vec
"""

Word1 = gr.Textbox()
Word2 = gr.Textbox()
Word3 = gr.Textbox()
label = gr.Label(show_label=True, label="Word4")
sp = gr.Image()


def inference(word1, word2, word3):
    transform = model[word3] + model[word2] - model[word1]
    output = model.similar_by_vector(transform)
    print(output)
    word_list = [word1, word2, word3]
    word_list.extend([x for x,y in [item for item in output[:6]]])
    words = {key: model[key] for key in word_list}
    words[word3 + " + (" + word2 + " - " + word1 + ")"] = transform
    data = np.concatenate([x[np.newaxis, :] for x in words.values()], axis=0)
    print(data.shape)
    labels = words.keys()
    reduced_data_pca = reduce_dimensions(data, method='PCA')
    print(reduced_data_pca.shape)
    plot_reduced_data(reduced_data_pca, labels, 'PCA Results')
    return cache

examples = [
    ["woman", "man", "girl"],  
    ["woman", "man", "granddaughter"],
    ["woman", "man", "aunt"],
]

iface = gr.Interface(
    fn=inference,
    inputs=[Word1, Word2, Word3],
    outputs=sp,
    description=description,
    examples=examples
    )

iface.launch()