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| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import plotly.express as px | |
| from sklearn.decomposition import PCA | |
| from sentence_transformers import SentenceTransformer | |
| # Load pre-trained sentence transformer model | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| # Function to calculate embeddings and PCA | |
| def compute_pca(texts, ids): | |
| # Generate embeddings | |
| embeddings = model.encode(texts) | |
| # Compute PCA | |
| pca = PCA(n_components=2) | |
| pca_result = pca.fit_transform(embeddings) | |
| # Create DataFrame for visualization | |
| df = pd.DataFrame({ | |
| 'ID': ids, | |
| 'Text': texts, | |
| 'PC1': pca_result[:, 0], | |
| 'PC2': pca_result[:, 1] | |
| }) | |
| # Plot the PCA result with identifiers as labels | |
| fig = px.scatter(df, x='PC1', y='PC2', text='ID', title='PCA of Text Embeddings') | |
| return fig | |
| # Define Gradio app layout and interactions | |
| def text_editor_app(): | |
| with gr.Blocks() as demo: | |
| # Input fields for text and identifier | |
| text_input = gr.Textbox(lines=5, placeholder="Enter or paste your texts here, one per line...", label="Text Inputs") | |
| id_input = gr.Textbox(lines=5, placeholder="Enter an identifier for each text, one per line...", label="Identifier Inputs") | |
| # Display the list of texts with identifiers | |
| texts_df = gr.Dataframe(headers=["ID", "Text"], label="Text List with Identifiers", interactive=True) | |
| # Button to process texts and identifiers | |
| submit_button = gr.Button("Compute Embeddings and PCA") | |
| # Output plot | |
| output_plot = gr.Plot(label="PCA Visualization") | |
| # Function to process input texts and identifiers | |
| def process_texts_and_ids(text_input, id_input): | |
| # Split input texts and identifiers by newline | |
| text_list = text_input.strip().split('\n') | |
| id_list = id_input.strip().split('\n') | |
| # Ensure both lists are of equal length | |
| if len(text_list) != len(id_list): | |
| return gr.update(value=[], error="Number of texts and identifiers must match.") | |
| # Return a new DataFrame instance with both text and identifiers | |
| return [[id_list[i], text_list[i]] for i in range(len(text_list))] | |
| # Define the button click interaction | |
| submit_button.click(fn=lambda x: compute_pca([t[1] for t in x], [t[0] for t in x]), inputs=texts_df, outputs=output_plot) | |
| # Update DataFrame with texts and identifiers | |
| text_input.change(fn=process_texts_and_ids, inputs=[text_input, id_input], outputs=texts_df) | |
| id_input.change(fn=process_texts_and_ids, inputs=[text_input, id_input], outputs=texts_df) | |
| return demo | |
| # Launch the app | |
| text_editor_app().launch() | |