Update app.py

app.py

@@ -1,9 +1,33 @@
 import streamlit as st
-from transformers import pipeline
+import torch
+import numpy as np
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-pipe = pipeline(model = 'kitkeat/distilbert-based-uncased-argumentativewriting')
-text = st.text_area('Yo, type here')
+tokenizer1 = AutoTokenizer.from_pretrained('kitkeat/distilbert-based-uncased-argumentativewriting')
+tokenizer2 = AutoTokenizer.from_pretrained('kitkeat/bert-large-uncased-sparse-90-unstructured-pruneofa-argumentativewriting')
+tokenizer3 = AutoTokenizer.from_pretrained('kitkeat/deberta-v3-base-argumentativewriting')
+model1 = AutoModelForSequenceClassification.from_pretrained('kitkeat/distilbert-based-uncased-argumentativewriting',num_labels=3)
+model2 = AutoModelForSequenceClassification.from_pretrained('kitkeat/bert-large-uncased-sparse-90-unstructured-pruneofa-argumentativewriting',num_labels=3)
+model3 = AutoModelForSequenceClassification.from_pretrained('kitkeat/deberta-v3-base-argumentativewriting',num_labels=3)
+text = st.text_area('Input Here!')
 
 if text:
-  out = pipe(text)
-  print(out)
+  inputs1 = tokenizer1(text, padding=True, truncation=True, return_tensors="pt")
+  inputs2 = tokenizer2(text, padding=True, truncation=True, return_tensors="pt")
+  inputs3 = tokenizer3(text, padding=True, truncation=True, return_tensors="pt")
+  
+  outputs1 = model1(**inputs1)
+  outputs2 = model2(**inputs2)
+  outputs3 = model3(**inputs3)
+    
+  prediction = outputs1.logits.argmax(dim=-1).item()
+  # model.config.id2label
+  
+  if prediction == 0:
+      out = 'Adequate'
+  elif prediction == 1:
+      out = 'Effective'
+  elif prediction == 2:
+      out = 'Ineffective'
+      
+  st.json(out)