Create app.py

app.py

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+import streamlit as st
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+import torch
+
+# Load pre-trained DistilBERT model and tokenizer
+model_name = "distilbert-base-uncased"
+tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=2)
+
+# Function to predict if news is real or fake
+def predict_news(news_text):
+    inputs = tokenizer(news_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predictions = torch.argmax(logits, dim=-1).item()
+    return "Real" if predictions == 1 else "Fake"
+
+# Streamlit App
+st.title("Fake News Detector")
+
+st.write("Enter a news article below to check if it's real or fake:")
+
+news_text = st.text_area("News Article", height=300)
+
+if st.button("Evaluate"):
+    if news_text:
+        prediction = predict_news(news_text)
+        st.write(f"The news article is predicted to be: **{prediction}**")
+    else:
+        st.write("Please enter some news text to evaluate.")
+