Upload stream.py

stream.py

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+import streamlit as st
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# Load the model and tokenizer from Hugging Face
+@st.cache_resource(allow_output_mutation=True)
+def load_model():
+    model = BertForSequenceClassification.from_pretrained("your-huggingface-username/your-model-repo")
+    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+# Define Streamlit app layout
+st.title("AI vs Human Text Classifier")
+
+user_input = st.text_area("Enter the text to classify:")
+
+if st.button("Classify"):
+    # Preprocess the input text
+    inputs = tokenizer(user_input, return_tensors="pt", max_length=256, padding="max_length", truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Get prediction
+    prediction = torch.argmax(outputs.logits, dim=1).item()
+
+    # Convert prediction to human-readable label
+    label_mapping = {0: "Human-written", 1: "AI-generated"}
+    st.write(f"The text is classified as: {label_mapping[prediction]}")