Spaces:
Sleeping
Sleeping
| import os | |
| import sys | |
| import torch | |
| import gradio as gr | |
| from huggingface_hub import hf_hub_download, snapshot_download | |
| from pathlib import Path | |
| import importlib.util | |
| class CognitiveNetworkDemo: | |
| def __init__(self): | |
| self.model = None | |
| self.repo_path = None | |
| self.setup_environment() | |
| def setup_environment(self): | |
| """Setup the environment and download the model""" | |
| # Download repository content | |
| self.repo_path = Path(snapshot_download( | |
| repo_id="VLabTech/cognitive_net", | |
| repo_type="model", | |
| local_dir="./model_repo" | |
| )) | |
| def import_module_from_file(self, module_name, file_path): | |
| """Import a module from file path""" | |
| spec = importlib.util.spec_from_file_location(module_name, file_path) | |
| module = importlib.util.module_from_spec(spec) | |
| sys.modules[module_name] = module | |
| spec.loader.exec_module(module) | |
| return module | |
| def load_model(self): | |
| """Load the model if not already loaded""" | |
| if self.model is None: | |
| try: | |
| # Find and import required modules | |
| network_path = next(self.repo_path.rglob("network.py")) | |
| node_path = next(self.repo_path.rglob("node.py")) | |
| memory_path = next(self.repo_path.rglob("memory.py")) | |
| # Import modules using absolute paths | |
| node_module = self.import_module_from_file("node", node_path) | |
| memory_module = self.import_module_from_file("memory", memory_path) | |
| network_module = self.import_module_from_file("network", network_path) | |
| # Create model instance | |
| self.model = network_module.DynamicCognitiveNet(input_size=5, output_size=2) | |
| except StopIteration: | |
| raise ImportError("Tidak dapat menemukan file modul yang diperlukan") | |
| except Exception as e: | |
| print("Debug - repo path:", self.repo_path) | |
| print("Debug - sys.path:", sys.path) | |
| raise ImportError(f"Gagal mengimpor model: {str(e)}") | |
| return self.model | |
| def predict(self, input_text): | |
| """Make predictions using the model""" | |
| 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." | |
| # Load model and generate prediction | |
| model = self.load_model() | |
| input_tensor = torch.tensor(values, dtype=torch.float32) | |
| output = 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 dalam format input: {str(e)}" | |
| except Exception as e: | |
| return f"Error: {str(e)}\n\nDebug info:\nRepo path: {self.repo_path}" | |
| def main(): | |
| # Initialize the demo | |
| demo_app = CognitiveNetworkDemo() | |
| # Setup Gradio Interface | |
| demo = gr.Interface( | |
| fn=demo_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 | |
| Model ini menerima 5 input numerik dan menghasilkan 2 output numerik menggunakan | |
| arsitektur Cognitive Network yang terinspirasi dari cara kerja otak biologis. | |
| Model diambil dari 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"] | |
| ] | |
| ) | |
| demo.launch() | |
| if __name__ == "__main__": | |
| main() |