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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 compute document embeddings and apply PCA
# Modify the Gradio interface to accept a list of identifiers and texts
def compute_pca(data):
# data is expected to be a list of dictionaries with 'Identifier' and 'Text' keys
df = pd.DataFrame(data, columns=["Identifier", "Text"])
# Remove rows where 'Identifier' or 'Text' is empty or contains only whitespace
valid_entries = df[
(df['Identifier'].str.strip() != '') &
(df['Text'].str.strip() != '')
]
if valid_entries.empty:
return gr.Plot.update(value=None, label="No data to process. Please fill in the boxes.")
# Generate embeddings
embeddings = model.encode(valid_entries['Text'].tolist())
# Perform PCA to reduce to 2 dimensions
pca = PCA(n_components=2)
pca_result = pca.fit_transform(embeddings)
# Add PCA results to the DataFrame
valid_entries = valid_entries.reset_index(drop=True)
valid_entries['PC1'] = pca_result[:, 0]
valid_entries['PC2'] = pca_result[:, 1]
# Plot the PCA result with identifiers as labels
fig = px.scatter(valid_entries, x='PC1', y='PC2', text='Identifier', title='PCA of Text Embeddings')
return fig
def text_editor_app():
with gr.Blocks() as demo:
identifier_inputs = []
text_inputs = []
gr.Markdown("### Enter at least two identifier-text pairs:")
for i in range(4): # Assuming we have 4 entries
with gr.Column():
id_input = gr.Textbox(label=f"Identifier {i+1}")
text_input = gr.Textbox(label=f"Text {i+1}")
identifier_inputs.append(id_input)
text_inputs.append(text_input)
gr.Markdown("---") # Add a horizontal rule to create a break
# Button to run the analysis
analyze_button = gr.Button("Run Analysis")
# Output plot
output_plot = gr.Plot(label="PCA Visualization")
# Function to collect inputs and process them
def collect_inputs(*args):
# args will be identifier1, text1, identifier2, text2, ..., identifier4, text4
# So we need to pair them up
data = []
for i in range(0, len(args), 2):
identifier = args[i]
text = args[i+1]
data.append([identifier, text])
return compute_pca(data)
inputs = []
for id_input, text_input in zip(identifier_inputs, text_inputs):
inputs.extend([id_input, text_input])
analyze_button.click(fn=collect_inputs, inputs=inputs, outputs=output_plot)
return demo
# Launch the app
text_editor_app().launch()
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