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| import numpy as np | |
| from sklearn.decomposition import PCA | |
| import gensim.downloader as api | |
| import gradio as gr | |
| import plotly.graph_objects as go | |
| # Load the Word2Vec model | |
| model = api.load("word2vec-google-news-300") | |
| def gensim_analogy(model, word1, word2, word3): | |
| try: | |
| result = model.most_similar(positive=[word2, word3], negative=[word1], topn=1) | |
| return result[0][0] # Return the word | |
| except KeyError as e: | |
| return str(e) | |
| def plot_words_plotly(model, words): | |
| vectors = np.array([model[word] for word in words if word in model.key_to_index]) | |
| # Reduce dimensions to 2D for plotting | |
| pca = PCA(n_components=2) | |
| vectors_2d = pca.fit_transform(vectors) | |
| # Create a scatter plot | |
| fig = go.Figure() | |
| # Add scatter points for each word vector | |
| for word, vec in zip(words, vectors_2d): | |
| fig.add_trace(go.Scatter(x=[vec[0]], y=[vec[1]], | |
| text=[word], mode='markers+text', | |
| textposition="bottom center", | |
| name=word)) | |
| fig.update_layout(title="Visualization of Word Vectors", | |
| xaxis_title="PCA 1", | |
| yaxis_title="PCA 2", | |
| showlegend=True, | |
| width=600, # Adjust width as needed | |
| height=400) # Adjust height as needed | |
| return fig | |
| def gradio_interface(choice, custom_input): | |
| if choice == "Custom": | |
| if not custom_input or len(custom_input.split(", ")) != 3: | |
| return "Invalid input. Please enter exactly three words, separated by commas.", None, { | |
| "error": "Invalid input"} | |
| words = custom_input.split(", ") | |
| else: | |
| if not choice: | |
| return "Invalid input. Please select or enter words.", None, { | |
| "error": "Invalid input"} | |
| words = choice.split(", ") | |
| word1, word2, word3 = words | |
| word4 = gensim_analogy(model, word1, word2, word3) | |
| plot_fig = plot_words_plotly(model, [word1, word2, word3, word4]) | |
| if word4 in model.key_to_index: | |
| vector = model[word4] | |
| vector_display = f"{word4}: {np.round(vector, 2).tolist()}" | |
| else: | |
| vector_display = "Vector not available for the resulting word" | |
| return word4, plot_fig, vector_display | |
| choices = [ | |
| "man, king, woman", | |
| "Paris, France, London", | |
| "strong, stronger, weak", | |
| "pork, pig, beef", | |
| "Custom" | |
| ] | |
| def clear_inputs(): | |
| return "", "", "", "", None | |
| # Define the layout using Rows and Columns | |
| with gr.Blocks() as iface: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("# Word Analogy and Vector Visualization") | |
| gr.Markdown( | |
| "Select a predefined triplet of words or choose 'Custom' and enter your own (comma-separated) to find a fourth word by analogy, and see their vectors plotted with Plotly.") | |
| radio = gr.Radio(choices=choices, label="Choose predefined words or enter custom words") | |
| custom_words = gr.Textbox( | |
| label="Custom words (comma-separated, required for custom choice; use only if 'Custom' is selected)", | |
| placeholder="Enter 3 words separated by commas") | |
| with gr.Row(): | |
| clear_btn = gr.Button("Clear") | |
| submit_btn = gr.Button("Submit") | |
| output_word = gr.Textbox(label="Output Word") | |
| word_plot = gr.Plot(label="Word Vectors Visualization") | |
| with gr.Row(): | |
| word_vectorization = gr.Textbox(label="Vectorization of the Output Word", lines=4, max_lines=4) | |
| clear_btn.click(fn=clear_inputs, inputs=None, | |
| outputs=[radio, custom_words, output_word, word_vectorization, word_plot]) | |
| submit_btn.click(fn=gradio_interface, inputs=[radio, custom_words], | |
| outputs=[output_word, word_plot, word_vectorization]) | |
| iface.launch(share=True) | |