Upload app.py

app.py

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+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()