Create app.py

app.py

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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):
+    # 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({
+        'Text': texts,
+        'PC1': pca_result[:, 0],
+        'PC2': pca_result[:, 1]
+    })
+    
+    # Plot the PCA result
+    fig = px.scatter(df, x='PC1', y='PC2', text='Text', title='PCA of Text Embeddings')
+    return fig
+
+# Define Gradio app layout and interactions
+def text_editor_app():
+    with gr.Blocks() as demo:
+        # Text box to input texts
+        text_input = gr.Textbox(lines=10, placeholder="Enter or paste your texts here, one per line...", label="Text Inputs")
+        
+        # Display the list of texts
+        texts = gr.Dataframe(headers=["Texts"], label="Text List", interactive=True)
+        
+        # Button to process texts
+        submit_button = gr.Button("Compute Embeddings and PCA")
+
+        # Output plot
+        output_plot = gr.Plot(label="PCA Visualization")
+        
+        # Define button click interaction
+        def process_texts(text_input):
+            # Split input texts by newline
+            text_list = text_input.strip().split('\n')
+            return gr.DataFrame.update(value=[[t] for t in text_list], row_count=len(text_list))
+        
+        submit_button.click(fn=lambda x: compute_pca([t[0] for t in x]), inputs=texts, outputs=output_plot)
+        text_input.change(fn=process_texts, inputs=text_input, outputs=texts)
+
+    return demo
+
+# Launch the app
+text_editor_app().launch()