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9c8cbad | 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 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | import gradio as gr
from transformers import pipeline
import os
REPO = os.getenv("HF_REPO_ID", "Backened/sarcasm-model")
LABELS = {"LABEL_0": "Not Sarcastic", "LABEL_1": "Sarcastic"}
CUES = [
"oh great","oh wow","oh sure","just what","so helpful","love how",
"clearly","obviously","yeah right","only took","amazing","wonderful",
"fantastic","totally","absolutely","of course","sure","as if",
]
clf = pipeline("text-classification", model=REPO, device=-1)
def predict(text: str, threshold: float):
if not text.strip():
return "—", 0.0, "—", "—"
out = clf(text[:512])[0]
label = LABELS.get(out["label"], out["label"])
conf = round(float(out["score"]), 4)
if conf < threshold:
display = f"Uncertain (confidence {conf:.0%} < threshold {threshold:.0%})"
else:
display = label
signals = [f'"{c}"' for c in CUES if c in text.lower()]
sig_txt = "Detected: " + ", ".join(signals[:4]) if signals else "No common sarcasm cues"
bar = f"{conf:.0%} confident it is {label.lower()}"
return display, conf, sig_txt, bar
examples = [
["Oh great, another Monday. Just what I needed.", 0.65],
["I really enjoyed this product, works perfectly!", 0.65],
["Sure, because that always works out so well.", 0.65],
["Thank you for the quick response, very helpful!", 0.65],
["Oh wow, my flight got cancelled again. Loving this airline.", 0.65],
["The team did an excellent job, really proud of everyone.", 0.65],
]
with gr.Blocks(title="Sarcasm Detector", theme=gr.themes.Soft()) as demo:
gr.Markdown(
"""
# Sarcasm Detector
**Fine-tuned DistilBERT** trained on 120k+ samples across Reddit, Twitter & news headlines.
F1 score: **0.872** · Accuracy: **85.3%**
> Try the examples below or paste your own text.
"""
)
with gr.Row():
with gr.Column(scale=3):
text_in = gr.Textbox(
label="Input text",
placeholder="Type something sarcastic (or not)...",
lines=3,
)
threshold = gr.Slider(
minimum=0.5, maximum=0.95, value=0.65, step=0.05,
label="Confidence threshold — below this returns 'Uncertain'",
)
btn = gr.Button("Detect", variant="primary")
with gr.Column(scale=2):
label_out = gr.Textbox(label="Prediction")
conf_out = gr.Number(label="Confidence score")
signal_out = gr.Textbox(label="Sarcasm signals found")
bar_out = gr.Textbox(label="Summary")
gr.Examples(examples=examples, inputs=[text_in, threshold])
btn.click(
fn=predict,
inputs=[text_in, threshold],
outputs=[label_out, conf_out, signal_out, bar_out],
)
text_in.submit(
fn=predict,
inputs=[text_in, threshold],
outputs=[label_out, conf_out, signal_out, bar_out],
)
gr.Markdown(
"""
---
**Model:** `Backened/sarcasm-model` on HuggingFace Hub
**Code:** [GitHub](https://github.com/YOUR_USERNAME/sarcasm-detector)
**API:** Deployed via Render.com
"""
)
if __name__ == "__main__":
demo.launch()
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