Create app.py

app.py

@@ -0,0 +1,32 @@
+import streamlit as st
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# Load the model and tokenizer from Hugging Face
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model_name = "pritam2014/BERTAIDetector"
+    model = BertForSequenceClassification.from_pretrained(model_name)
+    tokenizer = BertTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+# Load the model and tokenizer
+model, tokenizer = load_model()
+
+# Create a Streamlit app
+st.title("AI Text Detector")
+
+# Get user input
+user_input = st.text_area("Enter your text:", height=200)
+
+# Make predictions
+if st.button("Detect"):
+    inputs = tokenizer(user_input, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    predicted_class = torch.argmax(logits).item()
+    
+    if predicted_class == 0:
+        st.write("This text is likely **human-written**.")
+    else:
+        st.write("This text is likely **AI-generated**.")