Update app.py

app.py

@@ -2,155 +2,82 @@ import os
 import sys
 import torch
 import gradio as gr
-import importlib
 from huggingface_hub import hf_hub_download, snapshot_download
-from typing import List, Union
-from pathlib import Path
 
-class CognitiveNetApp:
-    def __init__(self):
-        self.model = None
-        self.model_class = None
+def predict(input_text: str) -> str:
+    """
+    Memproses input dan menghasilkan 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."
+
+        # Download dan load kode model
+        repo_path = snapshot_download(
+            repo_id="VLabTech/cognitive_net",
+            local_dir="./model_repo"
+        )
         
-    def setup_environment(self) -> None:
-        """
-        Menyiapkan environment untuk cognitive_net.
-        Mendownload repository dan mengatur Python path.
-        """
-        repo_path = snapshot_download(repo_id="VLabTech/cognitive_net")
-        repo_path = Path(repo_path)
+        # Import files secara langsung
+        import sys
+        sys.path.append("./model_repo")
         
-        if str(repo_path) not in sys.path:
-            sys.path.insert(0, str(repo_path))
-            
-        # Tambahkan parent directory juga ke Python path
-        parent_path = str(repo_path.parent)
-        if parent_path not in sys.path:
-            sys.path.insert(0, parent_path)
-            
-    def load_model(self) -> None:
-        """
-        Memuat model cognitive_net dan checkpoint-nya.
-        Raises:
-            ImportError: Jika modul tidak dapat diimport
-            RuntimeError: Jika terjadi kesalahan saat memuat model
-        """
-        try:
-            self.setup_environment()
-            
-            import cognitive_net
-            importlib.reload(cognitive_net)
-            from cognitive_net.network import DynamicCognitiveNet
-            
-            self.model_class = DynamicCognitiveNet
-            self.model = DynamicCognitiveNet(input_size=5, output_size=2)
-            
-            # Muat weights
-            checkpoint_path = hf_hub_download(
-                repo_id="VLabTech/cognitive_net",
-                filename="model.pt"
-            )
-            self.model.load_state_dict(torch.load(checkpoint_path))
-            self.model.eval()
-            
-        except ImportError as e:
-            raise ImportError(f"Gagal mengimport cognitive_net: {str(e)}")
-        except Exception as e:
-            raise RuntimeError(f"Gagal memuat model: {str(e)}")
-    
-    def parse_input(self, input_text: str) -> List[float]:
-        """
-        Mengurai input text menjadi list of floats.
+        # Import komponen model
+        from memory import CognitiveMemory
+        from node import CognitiveNode
+        from network import DynamicCognitiveNet
         
-        Args:
-            input_text: String berisi angka yang dipisahkan koma
-            
-        Returns:
-            List of float values
-            
-        Raises:
-            ValueError: Jika format input tidak valid
-        """
-        try:
-            values = [float(x.strip()) for x in input_text.split(",")]
-            if len(values) != 5:
-                raise ValueError(
-                    f"Dibutuhkan tepat 5 nilai (dipisahkan koma). "
-                    f"Anda memasukkan {len(values)} nilai."
-                )
-            return values
-        except ValueError as e:
-            raise ValueError(f"Format input tidak valid: {str(e)}")
-    
-    def predict(self, input_text: str) -> str:
-        """
-        Memproses input dan menghasilkan prediksi.
+        # Setup model
+        model = DynamicCognitiveNet(input_size=5, output_size=2)
         
-        Args:
-            input_text: String berisi 5 angka yang dipisahkan koma
-            
-        Returns:
-            String berisi hasil prediksi atau pesan error
-        """
-        try:
-            if self.model is None:
-                self.load_model()
-                
-            # Parse input
-            values = self.parse_input(input_text)
-            
-            # Generate prediction
-            input_tensor = torch.tensor(values, dtype=torch.float32)
-            with torch.no_grad():
-                output = self.model(input_tensor)
-            
-            # Format output
-            result = "Hasil Prediksi:\n"
-            result += f"Output 1: {output[0]:.4f}\n"
-            result += f"Output 2: {output[1]:.4f}"
-            
-            return result
-            
-        except ValueError as e:
-            return f"Error: {str(e)}"
-        except Exception as e:
-            return f"Error: {str(e)}\nTrace: {e.__traceback__}"
-
-def create_demo() -> gr.Interface:
-    """
-    Membuat dan mengkonfigurasi Gradio interface.
-    
-    Returns:
-        Gradio Interface object
-    """
-    app = CognitiveNetApp()
-    
-    demo = gr.Interface(
-        fn=app.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"
-        ),
-        outputs=gr.Textbox(label="Hasil Prediksi"),
-        title="Cognitive Network Demo",
-        description="""
-        ## Cognitive Network Inference Demo
+        # Load weights
+        checkpoint_path = hf_hub_download(
+            repo_id="VLabTech/cognitive_net",
+            filename="model.pt",
+            local_dir="./model_weights"
+        )
+        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)
         
-        Model ini menerima 5 input numerik dan menghasilkan 2 output numerik menggunakan
-        arsitektur Cognitive Network yang terinspirasi dari cara kerja otak biologis.
+        # Format output
+        result = "Hasil Prediksi:\n"
+        result += f"Output 1: {output[0]:.4f}\n"
+        result += f"Output 2: {output[1]:.4f}"
         
-        Model source: https://huggingface.co/VLabTech/cognitive_net
-        """,
-        examples=[
-            ["1.0, 2.0, 3.0, 4.0, 5.0"],
-            ["0.5, -1.0, 2.5, 1.5, -0.5"],
-            ["0.1, 0.2, 0.3, 0.4, 0.5"]
-        ]
-    )
-    
-    return demo
+        return result
+            
+    except ValueError as e:
+        return f"Error dalam format input: {str(e)}"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Setup Gradio Interface
+demo = gr.Interface(
+    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"
+    ),
+    outputs=gr.Textbox(label="Hasil Prediksi"),
+    title="Cognitive Network Demo",
+    description="""
+    ## Cognitive Network Inference Demo
+    Model ini menerima 5 input numerik dan menghasilkan 2 output numerik menggunakan
+    arsitektur Cognitive Network yang terinspirasi dari cara kerja otak biologis.
+    """,
+    examples=[
+        ["1.0, 2.0, 3.0, 4.0, 5.0"],
+        ["0.5, -1.0, 2.5, 1.5, -0.5"],
+        ["0.1, 0.2, 0.3, 0.4, 0.5"]
+    ]
+)
 
 if __name__ == "__main__":
-    demo = create_demo()
     demo.launch()