Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import torch
+
+MODEL = "AnasAlokla/multilingual_go_emotions"
+
+classifier = pipeline(
+    "text-classification",
+    model=MODEL,
+    tokenizer=MODEL,
+    return_all_scores=True,
+    device=0 if torch.cuda.is_available() else -1
+)
+
+def detect_gratitude(text):
+    if not text or not text.strip():
+        return {"gratitude_detected": False, "gratitude_score": 0.0, "all_emotions": {}}
+
+    out = classifier(text)[0]  # list of dicts: each with label & score
+
+    # Find 'Gratitude'
+    grat = next((e for e in out if e['label'].lower() == "gratitude"), None)
+    grat_score = grat['score'] if grat else 0.0
+
+    # Decide
+    THRESH = 0.6  # you adjust
+    detected = grat_score >= THRESH
+
+    return {
+        "gratitude_detected": detected,
+        "gratitude_score": round(grat_score, 3),
+        "all_emotions": {e['label']: round(e['score'],3) for e in out}
+    }
+
+demo = gr.Interface(
+    fn=detect_gratitude,
+    inputs=gr.Textbox(lines=2, placeholder="Введите English или Russian..."),
+    outputs="json",
+    title="Gratitude Detector",
+    description="Detects gratitude using multilingual_go_emotions"
+)
+
+if __name__ == "__main__":
+    demo.launch()