Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+MODEL_ID = "j-hartmann/emotion-english-distilroberta-base"
+
+text_emotion = pipeline(
+    task="text-classification",
+    model=MODEL_ID,
+    return_all_scores=True
+)
+
+def analyze_text(text: str):
+    """Return top emotion, its confidence, and all scores."""
+    if not text or not text.strip():
+        return "—", 0.0, {"notice": "Please enter some text."}
+
+       result = text_emotion(text)[0]
+    sorted_pairs = sorted(
+        [(r["label"], float(r["score"])) for r in result],
+        key=lambda x: x[1],
+        reverse=True
+    )
+    top_label, top_score = sorted_pairs[0]
+    all_scores = {label.lower(): round(score, 4) for label, score in sorted_pairs}
+    return top_label, round(top_score, 4), all_scores
+
+with gr.Blocks(title="Empath AI — Text Emotions") as demo:
+    gr.Markdown("# Empath AI — Text Emotion Detection\nPaste text and click **Analyze**.")
+
+    with gr.Row():
+        inp = gr.Textbox(
+            label="Enter text",
+            placeholder="Example: I'm so happy with the result today!",
+            lines=4
+        )
+        btn = gr.Button("Analyze", variant="primary")
+
+    with gr.Row():
+        top = gr.Textbox(label="Top Emotion", interactive=False)
+        conf = gr.Number(label="Confidence (0–1)", interactive=False)
+
+    all_scores = gr.JSON(label="All Emotion Scores")
+
+    gr.Examples(
+        examples=[
+            ["I'm thrilled with how this turned out!"],
+            ["This is taking too long and I'm getting frustrated."],
+            ["I'm worried this might fail."],
+            ["Thanks so much—this really helped."]
+        ],
+        inputs=inp
+    )
+
+    btn.click(analyze_text, inputs=inp, outputs=[top, conf, all_scores])
+
+demo.launch()