Update app.py

app.py

@@ -11,17 +11,19 @@ app = Flask(__name__)
 upload_folder = os.path.join('static', 'uploads')
 os.makedirs(upload_folder, exist_ok=True)
 
-# Load Updated Fake News Detection Pipelines
+# Updated Fake News Detection Models
 news_models = {
-    "fakenewsdetector": pipeline("text-classification", model="fakenewsdetector/bert-fake-news")
+    "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")
 }
 
-# Load Image Models for AI vs. Human Detection
-clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
-clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+# Updated Image Models for AI vs. Human Detection
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
 
 ai_image_models = {
-    "openai": clip_model
+    "clip-vit-base-patch32": clip_model
 }
 
 # Image transformation pipeline
@@ -54,8 +56,9 @@ HTML_TEMPLATE = """
             <textarea name="text" placeholder="Enter news text..." required></textarea>
             <label for="model">Select Fake News Model:</label>
             <select name="model" required>
-                <option value="newsverify">NewsVerify (RoBERTa)</option>
-                <option value="fakenewsdetector">FakeNewsDetector (BERT)</option>
+                <option value="mrm8488">MRM8488 (BERT-Tiny)</option>
+                <option value="liam168">Liam168 (BERT)</option>
+                <option value="distilbert">DistilBERT (SST-2)</option>
             </select>
             <button type="submit">Detect News Authenticity</button>
         </form>
@@ -115,7 +118,7 @@ def detect_image():
     inputs = clip_processor(images=img, return_tensors="pt")
 
     with torch.no_grad():
-        image_features = ai_image_models["openai"].get_image_features(**inputs)
+        image_features = ai_image_models["clip-vit-base-patch32"].get_image_features(**inputs)
 
     prediction = "AI-Generated" if torch.mean(image_features).item() > 0 else "Human-Created"
 
@@ -126,4 +129,3 @@ def detect_image():
 
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860)  # Suitable for Hugging Face Spaces
-