first try

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
+
+MODEL_ID = "cchristinezhou/comment-tone-classifier-demo"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+device = 0 if torch.cuda.is_available() else -1
+pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, device=device, return_all_scores=True)
+
+# Optional warmup to reduce first-call latency
+_ = pipe("warmup")
+
+def predict(text, max_length=256):
+    if not text or not text.strip():
+        return {}
+    out = pipe(text, truncation=True, max_length=max_length)[0]  # list of {label, score}
+    return {d["label"]: float(d["score"]) for d in out}
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Textbox(lines=4, placeholder="Paste a comment…"),
+        gr.Slider(64, 512, value=256, step=32, label="Max tokens"),
+    ],
+    outputs=gr.Label(num_top_classes=7),
+    title="Tone Classifier (BERT)",
+    description="Predicts tone across 7 categories: Supportive, Neutral, Passive-Aggressive, Blunt/Criticism, Emotionally Heavy, Objectifying, Parasocial Overreach.",
+    examples=[
+        ["You’re amazing, thanks for sharing!"],
+        ["lol okay sure 🙄"],
+        ["This wasn’t great—here’s why…"],
+        ["I feel overwhelmed reading this ngl."],
+    ],
+)
+demo.queue()  # handles concurrent users
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.0.0
+transformers
+torch
+accelerate