push the app

app.py

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+import gradio as gr
+from transformers import pipeline
+
+model_id = "GV05/distilbert-base-uncased-finetuned-emotion"
+classifier = pipeline("text-classification", model=model_id)
+
+label_to_emotion = {
+    'LABEL_0': 'sadness',
+    'LABEL_1': 'joy',
+    'LABEL_2': 'love',
+    'LABEL_3': 'anger',
+    'LABEL_4': 'fear',
+    'LABEL_5': 'surprise',
+}
+
+def classify_emotion(text):
+    preds = classifier(text, return_all_scores=True)
+    res = {}
+    for x in preds[0]:
+        res[label_to_emotion[x['label']]] = x['score']
+    return res
+
+image = gr.Textbox()
+label = gr.Label()
+examples = ["you are not too sensitive. you are not overreacting",
+            "Thinking of you keeps me awake. Dreaming of you keeps me asleep. Being with you keeps me alive."]
+
+title = "Emotion Detector"
+description = "This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset"
+
+intf = gr.Interface(fn=classify_emotion, inputs=image, outputs=label, examples=examples, title=title,
+                    description=description)
+
+intf.launch(inline=False)

requirements.txt

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+transformers
+gradio