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| import gradio as gr | |
| from wordcloud import WordCloud | |
| import pandas as pd | |
| 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() | |