Update app.py

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+iimport gradio as gr
 import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import numpy as np
@@ -45,20 +45,20 @@ class FallacyFinder:
             'no_fallacy': "The statement appears to be logically sound and free of common fallacies."
         }
         
-        # Try to load model, fallback to rule-based if not available
+        # Load your trained model from Hugging Face
         self.model = None
         self.tokenizer = None
         self.use_model = False
         
         try:
-            # This would be your trained model path
-            # For now, we'll use a rule-based approach
-            # self.tokenizer = AutoTokenizer.from_pretrained("your-model-path")
-            # self.model = AutoModelForSequenceClassification.from_pretrained("your-model-path")
-            # self.use_model = True
-            pass
-        except:
-            print("Model not found, using rule-based approach")
+            print("Loading model: SamanthaStorm/fallacyfinder")
+            self.tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/fallacyfinder")
+            self.model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/fallacyfinder")
+            self.use_model = True
+            print("✅ Model loaded successfully!")
+        except Exception as e:
+            print(f"❌ Error loading model: {e}")
+            print("Falling back to rule-based approach")
     
     def predict_with_rules(self, text):
         """Rule-based fallacy detection for when model isn't available"""