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8566151 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import gradio as gr
from transformers import pipeline
# Load your model
classifier = pipeline("text-classification", model="vnarasiman/unwrap-final")
def classify_post(text):
if not text.strip():
return "Please enter some text."
results = classifier(text, top_k=None) # get all label scores
# Format output nicely
output = ""
for r in sorted(results, key=lambda x: x["score"], reverse=True):
bar = "█" * int(r["score"] * 20)
output += f"{r['label']:<20} {bar} {r['score']*100:.1f}%\n"
return output
demo = gr.Interface(
fn=classify_post,
inputs=gr.Textbox(
lines=6,
placeholder="Paste a Reddit post here...",
label="Reddit Post"
),
outputs=gr.Textbox(
label="Classification Results",
lines=8
),
title="🔍 Unwrap — Reddit Post Classifier",
description="Paste any Reddit post to see how it gets classified.",
examples=[
["I've been feeling really overwhelmed at work lately and I don't know what to do anymore."],
["Just hit 1000 karma on my main account, feels good!"],
["Does anyone know a good recipe for homemade pasta?"],
],
theme=gr.themes.Soft()
)
if __name__ == "__main__":
demo.launch() |