update

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 import numpy as np
+from scipy.special import softmax
 
 
 # Define the function for sentiment analysis
@@ -30,13 +31,13 @@ image = st.image("images.png", width=200)
 # Get user input
 text = st.text_input("Type here:")
 button = st.button('Analyze')
-d = {
+# d = {
 
-    0:'Negative',
-    1:'Neutral',
-    2: 'Positive'
+#     0:'Negative',
+#     1:'Neutral',
+#     2: 'Positive'
      
-}
+# }
 # Define the CSS style for the app
 st.markdown(
 """
@@ -79,9 +80,14 @@ def get_model():
 tokenizer, model = get_model()
 
 if text and button:
-    text_sample = tokenizer([text], padding = 'max_length')
-    print(text_sample)
-    output = model(text_sample)
+    text_sample = tokenizer(text, padding = 'max_length',return_tensors = 'pt')
+    # print(text_sample)
+    output = model(**text_sample)
+    scores_ = output[0][0].detach().numpy()
+    scores_ = softmax(scores_)
+
+    labels = ['Negative','Neutral','Positive']
+    scores = {l:float(s) for (l,s) in zip(labels,scores_)}
     # st.write("Logits: ",output.logits)
     # y_pred = np.argmax(output.logits.detach().numpy(),axis =1)
-    # st.write("Prediction :",d[y_pred[0]])
+    st.write("Prediction :",labels)