initial

.gitignore

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+.DS_Store
+__pycache__/
+.vscode/
+venv/
+env/
+*.pyc
+.envrc

app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from torch.nn.functional import softmax
+
+
+model_name = "Ngit/MiniLMv2-L6-H384-goemotions-v2"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+
+def evaluate(text):
+    text = text.strip()
+    proba = [0]*28
+    if text:
+        input_ids = tokenizer(text, return_tensors="pt").input_ids
+        output = model(input_ids)
+        proba = softmax(output.logits, dim=1)[0]
+        proba = [int(v*1000)/10 for v in proba]
+    return proba
+
+
+with gr.Blocks() as demo:
+    text = gr.Textbox(label="Text to evaluate", lines=12)
+    with gr.Row():
+        with gr.Group():
+            t_adm = gr.Slider(label="admiration", value=0, maximum=100)
+            t_amu = gr.Slider(label="amusement", value=0, maximum=100)
+            t_ang = gr.Slider(label="anger", value=0, maximum=100)
+            t_ann = gr.Slider(label="annoyance", value=0, maximum=100)
+            t_app = gr.Slider(label="approval", value=0, maximum=100)
+            t_car = gr.Slider(label="caring", value=0, maximum=100)
+            t_con = gr.Slider(label="confusion", value=0, maximum=100)
+        with gr.Group():
+            t_cur = gr.Slider(label="curiosity", value=0, maximum=100)
+            t_des = gr.Slider(label="desire", value=0, maximum=100)
+            t_dis = gr.Slider(label="disappointment", value=0, maximum=100)
+            t_dip = gr.Slider(label="disapproval", value=0, maximum=100)
+            t_dit = gr.Slider(label="disgust", value=0, maximum=100)
+            t_emb = gr.Slider(label="embarrassment", value=0, maximum=100)
+            t_exc = gr.Slider(label="excitement", value=0, maximum=100)
+        with gr.Group():
+            t_fea = gr.Slider(label="fear", value=0, maximum=100)
+            t_gra = gr.Slider(label="gratitude", value=0, maximum=100)
+            t_gri = gr.Slider(label="grief", value=0, maximum=100)
+            t_joy = gr.Slider(label="joy", value=0, maximum=100)
+            t_lov = gr.Slider(label="love", value=0, maximum=100)
+            t_ner = gr.Slider(label="nervousness", value=0, maximum=100)
+            t_opt = gr.Slider(label="optimism", value=0, maximum=100)
+        with gr.Group():
+            t_pri = gr.Slider(label="pride", value=0, maximum=100)
+            t_rea = gr.Slider(label="realization", value=0, maximum=100)
+            t_rel = gr.Slider(label="relief", value=0, maximum=100)
+            t_rem = gr.Slider(label="remorse", value=0, maximum=100)
+            t_sad = gr.Slider(label="sadness", value=0, maximum=100)
+            t_sur = gr.Slider(label="surprise", value=0, maximum=100)
+            t_neu = gr.Slider(label="neutral", value=0, maximum=100)
+
+    btn = gr.Button(value="Evaluate Emotion")
+    btn.click(
+            evaluate,
+            inputs=[text],
+            outputs=[
+                t_adm,
+                t_amu,
+                t_ang,
+                t_ann,
+                t_app,
+                t_car,
+                t_con,
+                t_cur,
+                t_des,
+                t_dis,
+                t_dip,
+                t_dit,
+                t_emb,
+                t_exc,
+                t_fea,
+                t_gra,
+                t_gri,
+                t_joy,
+                t_lov,
+                t_ner,
+                t_opt,
+                t_pri,
+                t_rea,
+                t_rel,
+                t_rem,
+                t_sad,
+                t_sur,
+                t_neu,
+            ],
+        )
+
+if __name__ == "__main__":
+    demo.queue().launch()

requirements.txt

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+transformers
+torch