|
import gradio as gr |
|
from transformers import pipeline |
|
|
|
|
|
zero_shot_classifier = pipeline("zero-shot-classification", model="tasksource/ModernBERT-base-nli") |
|
nli_classifier = pipeline("text-classification", model="tasksource/ModernBERT-base-nli") |
|
|
|
def process_input(text_input, labels_or_premise, mode): |
|
if mode == "Zero-Shot Classification": |
|
labels = [label.strip() for label in labels_or_premise.split(',')] |
|
prediction = zero_shot_classifier(text_input, labels) |
|
results = {label: score for label, score in zip(prediction['labels'], prediction['scores'])} |
|
return results, '' |
|
else: |
|
prediction = nli_classifier([{"text": text_input, "text_pair": labels_or_premise}])[0] |
|
|
|
results = { |
|
"entailment": prediction.get("score", 0) if prediction.get("label") == "entailment" else 0, |
|
"contradiction": prediction.get("score", 0) if prediction.get("label") == "contradiction" else 0, |
|
"neutral": prediction.get("score", 0) if prediction.get("label") == "neutral" else 0 |
|
} |
|
return results, '' |
|
|
|
def update_interface(mode): |
|
if mode == "Zero-Shot Classification": |
|
return gr.update(label="🏷️ Categories", placeholder="Enter comma-separated categories...") |
|
else: |
|
return gr.update(label="🔎 Hypothesis", placeholder="Enter a hypothesis to compare with the premise...") |
|
|
|
with gr.Blocks() as demo: |
|
gr.Markdown("# 🤖 ModernBERT Text Analysis") |
|
|
|
mode = gr.Radio( |
|
["Zero-Shot Classification", "Natural Language Inference"], |
|
label="Select Mode", |
|
value="Zero-Shot Classification" |
|
) |
|
|
|
with gr.Column(): |
|
text_input = gr.Textbox( |
|
label="✍️ Input Text", |
|
placeholder="Enter your text...", |
|
lines=3 |
|
) |
|
|
|
labels_or_premise = gr.Textbox( |
|
label="🏷️ Categories", |
|
placeholder="Enter comma-separated categories...", |
|
lines=2 |
|
) |
|
|
|
submit_btn = gr.Button("Submit") |
|
|
|
outputs = [ |
|
gr.Label(label="📊 Results"), |
|
gr.Markdown(label="📈 Analysis", visible=False) |
|
] |
|
|
|
with gr.Column(variant="panel") as zero_shot_examples_panel: |
|
gr.Examples( |
|
examples=[ |
|
["I need to buy groceries", "shopping, urgent tasks, leisure, philosophy"], |
|
["The sun is very bright today", "weather, astronomy, complaints, poetry"], |
|
["I love playing video games", "entertainment, sports, education, business"], |
|
["The car won't start", "transportation, art, cooking, literature"], |
|
["She wrote a beautiful poem", "creativity, finance, exercise, technology"] |
|
], |
|
inputs=[text_input, labels_or_premise], |
|
label="Zero-Shot Classification Examples" |
|
) |
|
|
|
with gr.Column(variant="panel") as nli_examples_panel: |
|
gr.Examples( |
|
examples=[ |
|
["A man is sleeping on a couch", "The man is awake"], |
|
["The restaurant is full of people", "The place is empty"], |
|
["The child is playing with toys", "The kid is having fun"], |
|
["It's raining outside", "The weather is wet"], |
|
["The dog is barking at the mailman", "There is a cat"] |
|
], |
|
inputs=[text_input, labels_or_premise], |
|
label="Natural Language Inference Examples" |
|
) |
|
|
|
def update_visibility(mode): |
|
return ( |
|
gr.update(visible=(mode == "Zero-Shot Classification")), |
|
gr.update(visible=(mode == "Natural Language Inference")) |
|
) |
|
|
|
mode.change( |
|
fn=update_interface, |
|
inputs=[mode], |
|
outputs=[labels_or_premise] |
|
) |
|
|
|
mode.change( |
|
fn=update_visibility, |
|
inputs=[mode], |
|
outputs=[zero_shot_examples_panel, nli_examples_panel] |
|
) |
|
|
|
submit_btn.click( |
|
fn=process_input, |
|
inputs=[text_input, labels_or_premise, mode], |
|
outputs=outputs |
|
) |
|
|
|
if __name__ == "__main__": |
|
demo.launch() |
