Add all emotions and their persentage of 1

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 from transformers import pipeline
+import numpy as np
 
 print ("Load model...")
 
@@ -9,8 +10,8 @@ emotion_classifier = pipeline("text-classification", model=model_name)
 
 # Title and Description
 st.title("Emotion Classifier")
-st.write("""write down how your day went or what your mood is.
-On this space used model "bhadresh-savani/distilbert-base-uncased-emotion"
+st.write("""write down how your day went or what your mood is.""")
+st.write("""On this space used model "bhadresh-savani/distilbert-base-uncased-emotion".
 """)
 
 # Input text box
@@ -21,8 +22,13 @@ if st.button("Classify Emotion"):
         st.write("Please enter some text to classify.")
     else:
         # Get classification results
-        results = emotion_classifier(input_text)
-        st.subheader("Predicted Emotions:")
-        for result in results:
-            st.write(f"**{result['label']}**: {result['score']:.4f}")
-            st.write(f"**{result['label']}**: {result['score']:.4f}")
+        results = emotion_classifier(input_text, top_k=None)
+
+        # Extract scores and normalize to sum to 1
+        scores = np.array([result["score"] for result in results])
+        normalized_scores = scores / scores.sum()
+
+        # Display normalized results
+        st.subheader("Emotions:")
+        for i, result in enumerate(results):
+            st.write(f"**{result['label']}**: {normalized_scores[i]:.4f}")

requirements.txt

@@ -1,3 +1,4 @@
 streamlit==1.41.1
 transformers
-torch
+torch
+numpy