app.py

import gradio as gr
import torch
import numpy as np
import plotly.graph_objects as go
from huggingface_hub import snapshot_download
from pathlib import Path
import sys

# 1. Setup Model dari Hugging Face Hub ----------------------------
def setup_model():
    REPO_ID = "VLabTech/cognitive_net"
    LOCAL_DIR = "cognitive_net_pkg"
    
    # Download repo
    snapshot_download(
        repo_id=REPO_ID,
        local_dir=LOCAL_DIR,
        allow_patterns=["*.py", "*.txt"],
        repo_type="model",
        local_dir_use_symlinks=False
    )
    
    # Tambahkan ke path Python
    sys.path.insert(0, str(Path(LOCAL_DIR).absolute()))

setup_model()

# 2. Implementasi Model --------------------------------------------
from cognitive_net.network import DynamicCognitiveNet

class CognitiveDemo:
    def __init__(self):
        self.net = DynamicCognitiveNet(input_size=5, output_size=1)
        self.training_loss = []
        self.emotion_states = []
    
    def _parse_input(self, sequence_str):
        """Konversi string input ke tensor"""
        sequence = [float(x.strip()) for x in sequence_str.split(',')]
        if len(sequence) < 6:
            raise ValueError("Input minimal 6 angka")
        return (
            torch.tensor(sequence[:-1]).float(),
            torch.tensor([sequence[-1]]).float()
        )
    
    def train(self, sequence_str, epochs):
        try:
            X, y = self._parse_input(sequence_str)
            
            # Training loop
            self.training_loss = []
            self.emotion_states = []
            
            for _ in range(epochs):
                loss = self.net.train_step(X, y)
                self.training_loss.append(loss)
                self.emotion_states.append(self.net.emotional_state.item())
            
            # Prediksi akhir
            with torch.no_grad():
                pred = self.net(X)
            
            return {
                "prediction": f"{pred.item():.4f}",
                "loss_plot": self._create_loss_plot(),
                "emotion_plot": self._create_emotion_plot()
            }
        except Exception as e:
            return {"error": str(e)}
    
    def _create_loss_plot(self):
        fig = go.Figure()
        fig.add_trace(go.Scatter(
            y=self.training_loss,
            mode='lines+markers',
            name='Loss'
        ))
        fig.update_layout(
            title='Training Loss',
            xaxis_title='Epoch',
            yaxis_title='Loss Value'
        )
        return fig
    
    def _create_emotion_plot(self):
        fig = go.Figure()
        fig.add_trace(go.Scatter(
            y=self.emotion_states,
            mode='lines',
            name='Emotional State',
            line=dict(color='#FF6F61')
        ))
        fig.update_layout(
            title='Emotional State Dynamics',
            xaxis_title='Epoch',
            yaxis_title='State Value'
        )
        return fig

# 3. Antarmuka Gradio ----------------------------------------------
demo = CognitiveDemo()

with gr.Blocks(theme=gr.themes.Soft(), title="Cognitive Network Demo") as app:
    gr.Markdown("# 🧠 Cognitive Network Demo")
    gr.Markdown("""
    **Demonstrasi Jaringan Saraf Kognitif dengan:**
    - Memori Adaptif
    - Plastisitas Struktural
    - Modulasi Emosional
    """)
    
    with gr.Row():
        with gr.Column():
            input_seq = gr.Textbox(
                label="Deret Input (contoh: 0.1, 0.3, 0.5, 0.7, 0.9, 1.1)",
                value="0.1, 0.3, 0.5, 0.7, 0.9, 1.1"
            )
            epochs = gr.Slider(10, 500, value=100, label="Jumlah Epoch")
            train_btn = gr.Button("🚀 Latih Model", variant="primary")
        
        with gr.Column():
            output_pred = gr.Label(label="Prediksi")
            loss_plot = gr.Plot(label="Progress Training")
            emotion_plot = gr.Plot(label="Dinamika Emosional")
    
    # Contoh data preset
    gr.Examples(
        examples=[
            ["1, 2, 3, 4, 5, 6", 100],
            ["0.5, 1.0, 1.5, 2.0, 2.5, 3.0", 150],
            ["10, 8, 6, 4, 2, 0", 200]
        ],
        inputs=[input_seq, epochs],
        label="Contoh Input"
    )
    
    train_btn.click(
        fn=demo.train,
        inputs=[input_seq, epochs],
        outputs=[output_pred, loss_plot, emotion_plot]
    )

# 4. Jalankan Aplikasi ---------------------------------------------
if __name__ == "__main__":
    app.launch(debug=True)