Update app.py

app.py

@@ -14,8 +14,8 @@ os.makedirs(upload_folder, exist_ok=True)
 # Updated Fake News Detection Models
 news_models = {
     "mrm8488": pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection"),
-    "liam168": pipeline("text-classification", model="liam168/fake-news-bert-base-uncased"),
-    "distilbert": pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+    "distilbert": pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english"),
+    "cardiffnlp": pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-offensive")
 }
 
 # Updated Image Models for AI vs. Human Detection
@@ -57,8 +57,8 @@ HTML_TEMPLATE = """
             <label for="model">Select Fake News Model:</label>
             <select name="model" required>
                 <option value="mrm8488">MRM8488 (BERT-Tiny)</option>
-                <option value="liam168">Liam168 (BERT)</option>
                 <option value="distilbert">DistilBERT (SST-2)</option>
+                <option value="cardiffnlp">CardiffNLP (RoBERTa-Offensive)</option>
             </select>
             <button type="submit">Detect News Authenticity</button>
         </form>
@@ -100,7 +100,7 @@ def detect():
         return render_template_string(HTML_TEMPLATE, news_prediction="Invalid input or model selection.")
 
     result = news_models[model_key](text)[0]
-    label = "REAL" if result['label'].lower() in ["real", "label_1"] else "FAKE"
+    label = "REAL" if result['label'].lower() in ["real", "label_1", "neutral"] else "FAKE"
     confidence = result['score'] * 100
 
     return render_template_string(