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app.py

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+import gradio as gr
+from transformers import pipeline
+
+# ---------- Load models ----------
+sentiment = pipeline("sentiment-analysis")                # DistilBERT SST-2
+classifier = pipeline("zero-shot-classification",
+                      model="facebook/bart-large-mnli")   # Zero-shot
+
+def analyze_email(subject, body):
+    text = subject + "\n" + (body or "")
+
+    # Sentiment
+    s_res = sentiment(text)[0]
+    s_label = s_res["label"]
+    s_score = s_res["score"]
+
+    # Zero-shot custom labels
+    labels = ["engaging", "spammy", "informative", "boring", "urgent"]
+    z_res = classifier(text, labels)
+    z_scores = {l: f"{s:.2f}" for l, s in zip(z_res["labels"], z_res["scores"])}
+
+    # ---------- format output ----------
+    out  = f"### Sentiment\n**{s_label}**  (confidence {s_score:.2f})\n\n"
+    out += "### Quality scores\n"
+    for l, s in z_scores.items():
+        out += f"- **{l}** : {s}\n"
+    return out
+
+demo = gr.Interface(
+    fn     = analyze_email,
+    inputs = [gr.Textbox(label="Subject line"),
+              gr.Textbox(lines=6, label="Email body (optional)")],
+    outputs = gr.Markdown(),
+    title   = "Email Quality & Sentiment Analyzer",
+    description = "Combines a sentiment pipeline + zero-shot classification"
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+transformers
+torch