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import gradio as gr
import time
from sentence_transformers import CrossEncoder
# Load model
model2 = CrossEncoder('enochlev/coherence-all-mpnet-base-v2')
# Predefined examples
examples = [
["What is your favorite color?", "Blue!"],
["Do you like playing outside?", "I like ice cream."],
["What is your favorite animal?", "I like dogs!"],
["Do you want to go to the park?", "Yes, I want to go on the swings!"],
["What is your favorite food?", "I like playing with blocks."],
["Do you have a pet?", "Yes, I have a cat named Whiskers."],
["What is your favorite thing to do on a sunny day?", "I like playing soccer with my friends."]
]
# Global index for cycling through examples
current_index = 0
def check_coherence(sentence1: str, sentence2: str) -> float:
"""
Predicts the coherence score for a pair of sentences.
"""
score = model2.predict([[sentence1, sentence2]])[0]
return score
def get_next_example():
"""
Returns the next example pair and updates the current index.
"""
global current_index
pair = examples[current_index]
current_index = (current_index + 1) % len(examples)
return pair[0], pair[1]
with gr.Blocks() as demo:
gr.Markdown("## Coherence Checker Demo")
with gr.Row():
with gr.Column():
inp1 = gr.Textbox(label="Sentence 1", placeholder="Enter first sentence...")
inp2 = gr.Textbox(label="Sentence 2", placeholder="Enter second sentence...")
check_button = gr.Button("Check Coherence")
next_example_button = gr.Button("Next Example")
with gr.Column():
output_score = gr.Textbox(label="Coherence Score", interactive=False)
check_button.click(fn=check_coherence, inputs=[inp1, inp2], outputs=output_score)
next_example_button.click(fn=get_next_example, inputs=[], outputs=[inp1, inp2])
demo.launch() |