Update app.py

app.py

@@ -1,59 +1,59 @@
-import streamlit as st
-from joblib import load
-import unicodedata
-import torch 
-import torch.nn as nn
-
-# Load Encoder and Model
-Encoder = load("Country_Encoder")
-all_classes = Encoder.classes_
-
-st.title("Name Classification Based on Last Name")
-
-# Define RNN Model
-class SimpleRNN(nn.Module):
-    def __init__(self, input_size, hidden_size, output_size, num_layers):
-        super(SimpleRNN, self).__init__()
-        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
-        self.dropout = nn.Dropout(0.3)
-        self.fc = nn.Linear(hidden_size, output_size)
-
-    def forward(self, x):
-        out, _ = self.rnn(x)
-        out = self.dropout(out[:, -1, :])  # Take last time step output
-        out = self.fc(out)
-        return out
-
-# Load Model Weights
-model1 = SimpleRNN(1, 512, len(all_classes), 1)  # input_size should be 1 for ASCII values
-model1.load_state_dict(torch.load("rnn_model.pth", map_location=torch.device('cpu')))
-model1.eval()
-
-# Text Input for Name
-name = st.text_input("Enter Last Name")
-
-# Convert Unicode to ASCII
-def unicode_to_ascii(s):
-    s = s.casefold()
-    return ''.join(
-        c for c in unicodedata.normalize('NFD', s)
-        if unicodedata.category(c) != 'Mn'
-    )
-
-if st.button("Submit"):
-    name = unicode_to_ascii(name)
-    name_ascii = [ord(letter) for letter in name]
-
-    # Padding or Truncation to 20 characters
-    name_ascii = name_ascii[:20] + [0] * (20 - len(name_ascii))
-
-    # Convert to Tensor (reshape for RNN input)
-    X = torch.tensor(name_ascii, dtype=torch.float32).view(1, 20, 1)  # Shape: (batch, sequence, input)
-
-    with torch.no_grad():
-        pred = model1(X)
-
-    # Get Predicted Class
-    idx = torch.argmax(pred).item()
-    class_ = all_classes[idx]
+import streamlit as st
+from joblib import load
+import unicodedata
+import torch 
+import torch.nn as nn
+
+# Load Encoder and Model
+Encoder = load("Country_Encoder")
+all_classes = Encoder.classes_
+
+st.title("Name Classification Based on Last Name")
+
+# Define RNN Model
+class SimpleRNN(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size, num_layers):
+        super(SimpleRNN, self).__init__()
+        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
+        self.dropout = nn.Dropout(0.3)
+        self.fc = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        out, _ = self.rnn(x)
+        out = self.dropout(out[:, -1, :])  # Take last time step output
+        out = self.fc(out)
+        return out
+
+# Load Model Weights
+model1 = SimpleRNN(1, 256, len(all_classes), 1)  # input_size should be 1 for ASCII values
+model1.load_state_dict(torch.load("rnn_model.pth", map_location=torch.device('cpu')))
+model1.eval()
+
+# Text Input for Name
+name = st.text_input("Enter Last Name")
+
+# Convert Unicode to ASCII
+def unicode_to_ascii(s):
+    s = s.casefold()
+    return ''.join(
+        c for c in unicodedata.normalize('NFD', s)
+        if unicodedata.category(c) != 'Mn'
+    )
+
+if st.button("Submit"):
+    name = unicode_to_ascii(name)
+    name_ascii = [ord(letter) for letter in name]
+
+    # Padding or Truncation to 20 characters
+    name_ascii = name_ascii[:20] + [0] * (20 - len(name_ascii))
+
+    # Convert to Tensor (reshape for RNN input)
+    X = torch.tensor(name_ascii, dtype=torch.float32).view(1, 20, 1)  # Shape: (batch, sequence, input)
+
+    with torch.no_grad():
+        pred = model1(X)
+
+    # Get Predicted Class
+    idx = torch.argmax(pred).item()
+    class_ = all_classes[idx]
     st.success(f"Predicted Class: {class_}")