Update app.py

app.py

@@ -34,17 +34,77 @@ def predict_news(news_text):
     predictions = torch.argmax(logits, dim=-1).item()
     return "Real" if predictions == 1 else "Fake"
 
+# Check if a GPU is available and move the model to the appropriate device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Define text classification function
+def classify_text(text):
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    inputs = {key: value.to(device) for key, value in inputs.items()}
+
+    # Ensure the model is in evaluation mode
+    model.eval()
+
+    # Perform the forward pass
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    logits = outputs.logits
+
+    # Convert logits to probabilities
+    probabilities = torch.nn.functional.softmax(logits, dim=-1)
+
+    # Get the predicted class
+    predicted_class = torch.argmax(probabilities, dim=1).item()
+
+    # Define the label mapping
+    labels = ['False', 'True']
+
+    # Return the predicted label and probabilities
+    predicted_label = labels[predicted_class]
+    probabilities = probabilities.cpu().numpy()
+
+    return predicted_label, probabilities
+
+
 # Streamlit App
 st.title("Fake News Detector")
 
+# Add disclaimer
+st.markdown("""
+### Disclaimer
+
+**Important Notice:**
+
+This model was trained exclusively on news articles from Reuters. As a result, the model may be biased towards considering news from Reuters as "True" and may not accurately classify news from other sources. 
+
+**Usage Warning:**
+
+- This model is intended for experimental and educational purposes only.
+- We do not take any responsibility for the outcomes or decisions made based on the results provided by this model.
+- The model should not be used for any critical or real-world applications, especially those that involve significant consequences or decision-making.
+- Users are encouraged to apply their own judgment and consult multiple sources when evaluating the credibility of news.
+
+**By using this model, you acknowledge and accept these terms and disclaimers.**
+""")
+
 st.write("Enter a news article URL below to check if it's real or fake:")
 
 news_url = st.text_area("News URL", height=100)
 
 if st.button("Evaluate"):
     if news_url:
-        news_text = extract_news_text(news_url)
-        prediction = predict_news(news_text)
-        st.write(f"The news article is predicted to be: **{prediction}**")
+        try:
+            news_text = extract_news_text(news_url)
+            predicted_label, probabilities = classify_text(news_text)
+            st.write(f"The news article is predicted to be: **{predicted_label}**")
+        except:
+            st.write("It wasn't possible to fetch the article text. Enter the news article text below to check if it's real or fake:")
+            news_text = st.text_area("News text", height=300)
+            predicted_label, probabilities = classify_text(news_text)
+            st.write(f"The news article is predicted to be: **{predicted_label}**")
+
     else:
         st.write("Please enter some news URL to evaluate.")