Update app.py

app.py

@@ -3,14 +3,20 @@ import torch
 import torch.nn as nn
 import joblib
 
-# ---------- Model Definition (matches saved model) ----------
+# ---------- Model Definition (Fixed to match .pth file) ----------
 class SpamClassifier(nn.Module):
     def __init__(self, input_dim):
         super(SpamClassifier, self).__init__()
+        # Based on the error log, the saved model has:
+        # Layer 0: Linear(1000 -> 64)
+        # Layer 2: Linear(64 -> 32)
+        # Layer 4: Linear(32 -> 2)
         self.model = nn.Sequential(
-            nn.Linear(input_dim, 128),
+            nn.Linear(input_dim, 64),
             nn.ReLU(),
-            nn.Linear(128, 2),
+            nn.Linear(64, 32),
+            nn.ReLU(),
+            nn.Linear(32, 2),
             nn.Softmax(dim=1)
         )
 
@@ -18,6 +24,8 @@ class SpamClassifier(nn.Module):
         return self.model(x)
 
 # ---------- Load Vectorizer ----------
+# Note: Ensure the environment uses scikit-learn version close to 1.2.2 
+# if possible to avoid the warnings in your logs, though it often works anyway.
 vectorizer = joblib.load("model/vectorizer.pkl")
 input_dim = len(vectorizer.get_feature_names_out())
 
@@ -45,4 +53,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()