create app.py

app.py

@@ -0,0 +1,49 @@
+import streamlit as st
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+
+def load_model():
+    tokenizer = BertTokenizer.from_pretrained("BERT_GED")
+    model = BertForSequenceClassification.from_pretrained("BERT_GED")
+    return model, tokenizer
+
+def predict(model, tokenizer, sentence):
+    # Tokenize sentence
+    encoded_dict = tokenizer.encode_plus(
+        sentence, 
+        add_special_tokens=True,
+        max_length=64, 
+        padding="max_length",
+        truncation=True,
+        return_attention_mask=True, 
+        return_tensors='pt', 
+    )
+    input_ids = encoded_dict['input_ids']
+    attention_mask = encoded_dict['attention_mask']
+
+    # Model inference
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask=attention_mask)
+    
+    logits = outputs.logits
+    index = torch.argmax(logits, -1).item()  # Get the predicted class (0 or 1)
+
+    if index == 1:
+        return "perfect"
+    else:
+        return "not right!!"
+    
+def main():
+    st.title("Grammatical Correctness Predictor")
+    sentence = st.text_area("Sentence to analyze:")
+
+    if st.button("Analyze"):
+        if sentence:
+            model, tokenizer = load_model()
+            prediction = predict(model, tokenizer, sentence)
+            st.write(f'"{sentence}" is grammatically {prediction}')
+        else:
+            st.warning("Please enter a sentence.")
+
+if __name__ == "__main__":
+    main()