fixed probability calc

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
-from torch.nn.functional import softmax
+import numpy as np
 
 
 model_name = "Ngit/MiniLMv2-L6-H384-goemotions-v2"
@@ -14,13 +14,13 @@ def evaluate(text):
     if text:
         input_ids = tokenizer(text, return_tensors="pt").input_ids
         output = model(input_ids)
-        proba = softmax(output.logits, dim=1)[0]
+        proba = 1 / (1 + np.exp(-output.logits.detach().numpy()[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)
+    text = gr.Textbox(label="Text to evaluate", lines=6)
     with gr.Row():
         with gr.Group():
             t_adm = gr.Slider(label="admiration", value=0, maximum=100)