Update app.py

app.py

@@ -2,66 +2,60 @@
 import os
 import torch
 import gradio as gr
-from huggingface_hub import hf_hub_download
-from cognitive_net import DynamicCognitiveNet, CognitiveMemory, CognitiveNode
+from huggingface_hub import hf_hub_download, snapshot_download
+from importlib.util import spec_from_file_location, module_from_spec
+import sys
 
-class ModelInterface:
-    def __init__(self):
-        self.model = self.load_model()
-        
-    def load_model(self):
-        """Load model dari HuggingFace Hub"""
-        try:
-            # Inisialisasi model
-            model = DynamicCognitiveNet(input_size=5, output_size=2)
-            
-            # Download checkpoint
-            checkpoint_path = hf_hub_download(
-                repo_id="VLabTech/cognitive_net",
-                filename="model.pt"
-            )
-            
-            # Load weights
-            model.load_state_dict(torch.load(checkpoint_path))
-            model.eval()
-            return model
-        except Exception as e:
-            print(f"Error loading model: {e}")
-            return None
-
-    def predict(self, input_text):
-        """Proses input dan generate prediksi"""
-        try:
-            # Parse input
-            values = [float(x.strip()) for x in input_text.split(",")]
-            if len(values) != 5:
-                return f"Error: Masukkan tepat 5 nilai (dipisahkan koma). Anda memasukkan {len(values)} nilai."
-            
-            # Convert ke tensor
-            input_tensor = torch.tensor(values, dtype=torch.float32)
-            
-            # Generate prediksi
-            with torch.no_grad():
-                output = self.model(input_tensor)
-            
-            # Format output
-            result = f"Hasil Prediksi:\n"
-            result += f"Output 1: {output[0]:.4f}\n"
-            result += f"Output 2: {output[1]:.4f}"
-            
-            return result
-            
-        except ValueError:
-            return "Error: Pastikan semua input adalah angka valid"
-        except Exception as e:
-            return f"Error: {str(e)}"
+def setup_cognitive_net():
+    """Setup cognitive_net module from HuggingFace"""
+    # Download repository content
+    repo_path = snapshot_download(repo_id="VLabTech/cognitive_net")
+    
+    # Import module from downloaded path
+    sys.path.append(repo_path)
+    from cognitive_net import DynamicCognitiveNet
+    return DynamicCognitiveNet
 
-# Inisialisasi interface
-model_interface = ModelInterface()
+def predict(input_text):
+    """Process input and return prediction"""
+    try:
+        # Parse input
+        values = [float(x.strip()) for x in input_text.split(",")]
+        if len(values) != 5:
+            return f"Error: Masukkan tepat 5 nilai (dipisahkan koma). Anda memasukkan {len(values)} nilai."
+        
+        # Setup model
+        DynamicCognitiveNet = setup_cognitive_net()
+        model = DynamicCognitiveNet(input_size=5, output_size=2)
+        
+        # Load weights
+        checkpoint_path = hf_hub_download(
+            repo_id="VLabTech/cognitive_net",
+            filename="model.pt"
+        )
+        model.load_state_dict(torch.load(checkpoint_path))
+        model.eval()
+        
+        # Generate prediction
+        input_tensor = torch.tensor(values, dtype=torch.float32)
+        with torch.no_grad():
+            output = model(input_tensor)
+        
+        # Format output
+        result = f"Hasil Prediksi:\n"
+        result += f"Output 1: {output[0]:.4f}\n"
+        result += f"Output 2: {output[1]:.4f}"
+        
+        return result
+        
+    except ValueError:
+        return "Error: Pastikan semua input adalah angka valid"
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 # Setup Gradio Interface
 demo = gr.Interface(
-    fn=model_interface.predict,
+    fn=predict,
     inputs=gr.Textbox(
         label="Input Values",
         placeholder="Masukkan 5 nilai numerik (pisahkan dengan koma). Contoh: 1.0, 2.0, 3.0, 4.0, 5.0"
@@ -71,16 +65,10 @@ demo = gr.Interface(
     description="""
     ## Cognitive Network Inference Demo
     
-    Model ini menerima 5 input numerik dan menghasilkan 2 output numerik.
-    
-    ### Cara Penggunaan:
-    1. Masukkan 5 angka yang dipisahkan dengan koma
-    2. Klik Submit untuk melihat hasil prediksi
+    Model ini menerima 5 input numerik dan menghasilkan 2 output numerik menggunakan
+    arsitektur Cognitive Network yang terinspirasi dari cara kerja otak biologis.
     
-    ### Format Input:
-    - Masukkan tepat 5 nilai numerik
-    - Pisahkan nilai dengan koma
-    - Gunakan titik untuk desimal
+    Model source: https://huggingface.co/VLabTech/cognitive_net
     """,
     examples=[
         ["1.0, 2.0, 3.0, 4.0, 5.0"],