Update app.py

app.py

@@ -1,6 +1,7 @@
 import torch
 import torch.nn as nn
 import numpy as np
+import gradio as gr
 
 SEQUENCE_LENGTH = 10
 INPUT_SIZE = 1
@@ -13,24 +14,55 @@ class Seq2Seq(nn.Module):
         super(Seq2Seq, self).__init__()
         self.seq_len = seq_len
         self.hidden_size = hidden_size
-
         self.encoder_lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
         self.decoder_lstm = nn.LSTM(hidden_size, hidden_size, batch_first=True)
         self.decoder_linear = nn.Linear(hidden_size, output_size)
 
     def forward(self, x):
         _, (hidden, cell) = self.encoder_lstm(x)
-        
         context_vector = hidden.permute(1, 0, 2)
         decoder_input = context_vector.repeat(1, self.seq_len, 1)
-        
         decoder_output, _ = self.decoder_lstm(decoder_input, (hidden, cell))
-        
         prediction = self.decoder_linear(decoder_output)
-        
         return prediction
 
-model_path = 'seq2seq_model_weights.pth' 
+model_path = 'seq2seq_model_weights.pth'
 model = Seq2Seq(INPUT_SIZE, HIDDEN_UNITS, OUTPUT_SIZE, SEQUENCE_LENGTH).to(device)
 model.load_state_dict(torch.load(model_path, map_location=device))
-model.eval()
+model.eval()
+
+def predict_sequence(input_text):
+    try:
+        numbers = [float(n.strip()) for n in input_text.split(',')]
+        
+        if len(numbers) != SEQUENCE_LENGTH:
+            return f"Error: Please enter exactly {SEQUENCE_LENGTH} numbers, separated by commas."
+        
+        input_array = np.array(numbers).reshape(1, SEQUENCE_LENGTH, 1)
+        input_tensor = torch.from_numpy(input_array).float().to(device)
+        
+        with torch.no_grad():
+            prediction_tensor = model(input_tensor)
+            
+        output_array = prediction_tensor.cpu().numpy().flatten()
+        
+        output_text = ", ".join([f"{n:.1f}" for n in output_array])
+        
+        return output_text
+        
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+demo = gr.Interface(
+    fn=predict_sequence,
+    inputs=gr.Textbox(
+        label="Input Sequence", 
+        placeholder=f"Enter {SEQUENCE_LENGTH} numbers, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10"
+    ),
+    outputs=gr.Textbox(label="Predicted Sequence"),
+    title="Q11: Seq2Seq Model (n -> n+1)",
+    description="Enter a sequence of 10 numbers to predict the next sequence.",
+    allow_flagging="never"
+)
+
+demo.launch()