import gradio as gr
from wordcloud import WordCloud
import pandas as pd
import hopsworks
import matplotlib.pyplot as plt

# Assume you have a DataFrame with columns: Topic, Article, Keywords
# You need to replace this with your actual data loading mechanism.
# For example, you might fetch data from a database or a file.
data = {
    'Topic': ['Topic1', 'Topic2'],
    'keywords': ['key1 key2 key3', ["cream","ice","vanilla","flavor","chocolate","kid","häagen","dazs","you","sprinting"]],
    'scores' :[[], [0.16746170342156613,0.15000902432939608,0.0793086259849024,0.0642684614359449,0.05274725840837433,0.051507427048382876,0.047404471182744455,0.047404471182744455,0.03655408024186657,0.035427310538133555]]
}
data2 = {
    'Topic' : ["Topic2", "Topic2"],
    'Article' : ["1", "2"],
    'Link' : ['https://www.google.fr', 'https://www.google.fr']
}
df = pd.DataFrame(data)
topics = df['Topic'].unique()
df2 = pd.DataFrame(data2)

project = hopsworks.login()
fs = project.get_feature_store()
dataset = fs.get_feature_group(name="daily_topic_info")
print(dataset)
def display_topics(topic):
    # Filter DataFrame based on the selected topic
    selected_data = df[df['Topic'] == topic]
    selected_data2 = df2[df2['Topic'] == topic]
    # Display relevant articles
    articles = selected_data2['Article'] 
    links = selected_data2['Link']
    nb_art = min(4, len(links))
    articles_ret = """## Most relevant articles  
      
    """
    for i in range(nb_art):
        articles_ret += f""" * [{articles[i]}]({links[i]})  
    """
    # Generate word cloud for keywords
    keywords = selected_data['keywords'][1]
    freq = selected_data["scores"][1]
    keywords_wordcloud = dict()
    for i, elem in enumerate(keywords):
        keywords_wordcloud[elem] = freq[i]
    wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(keywords_wordcloud)
    fig, ax = plt.subplots()
    plt.axis("off")
    ax =plt.imshow(wordcloud, interpolation='bilinear')

    return articles_ret ,  fig

# Define Gradio interface
iface = gr.Interface(
    fn=display_topics,
    inputs=gr.Dropdown(["Topic1", "Topic2"], label="Topic"),
    outputs=[gr.Markdown(label="Most relevant articles"),gr.Plot(label="Main Keywords")],
    live=True,
    examples=[]
)

# Launch the app
iface.launch()