app.py

import gradio as gr
from ultralytics import YOLO
import pandas as pd
import datetime
import os
import numpy as np
import matplotlib.pyplot as plt

# 1. KONFIGURASI
MODEL_PATH = 'yolov8n.pt'
DB_FILE = 'inventory_log.csv'
model = YOLO(MODEL_PATH)

# 2. FUNGSI FORECASTING (PREDIKSI)
def predict_demand():
    if not os.path.isfile(DB_FILE):
        return "⚠️ Data histori belum cukup untuk prediksi.", None
    
    df = pd.read_csv(DB_FILE)
    if len(df) < 3: # Butuh minimal 3 data point untuk melihat tren
        return "⚠️ Butuh minimal 3 kali scan untuk menghitung tren prediksi.", None
    
    # Kelompokkan data berdasarkan barang dan hitung rata-rata jumlah per hari
    df['Timestamp'] = pd.to_datetime(df['Timestamp'])
    df['Date_Ordinal'] = df['Timestamp'].apply(lambda x: x.toordinal())
    
    forecast_results = "### 🔮 Prediksi Ketersediaan Stok:\n"
    items = df['Barang'].unique()
    
    fig, ax = plt.subplots(figsize=(8, 4))
    
    for item in items:
        item_df = df[df['Barang'] == item].sort_values('Timestamp')
        X = item_df['Date_Ordinal'].values.reshape(-1, 1)
        y = item_df['Jumlah'].values
        
        # Linear Regression Sederhana (Slope & Intercept)
        if len(y) > 1:
            slope, intercept = np.polyfit(X.flatten(), y, 1)
            
            # Jika tren menurun (slope negatif)
            if slope < 0:
                days_left = int(-intercept / slope) - datetime.date.today().toordinal()
                days_left = max(0, days_left)
                forecast_results += f"- **{item}**: Diperkirakan habis dalam **{days_left} hari**.\n"
            else:
                forecast_results += f"- **{item}**: Stok cenderung stabil/meningkat.\n"
            
            # Plotting histori
            ax.plot(item_df['Timestamp'], item_df['Jumlah'], marker='o', label=f"Tren {item}")

    ax.set_title("Grafik Perubahan Stok Barang")
    ax.set_ylabel("Jumlah Unit")
    ax.legend()
    plt.xticks(rotation=45)
    plt.tight_layout()
    
    return forecast_results, fig

# 3. FUNGSI UTAMA (INTEGRASI SCAN)
def process_inventory(img):
    if img is None:
        return None, None, "⚠️ Unggah foto.", None, "Silakan scan dulu.", None

    results = model(img)
    res_plotted = results[0].plot()
    
    detections = results[0].boxes.cls.tolist()
    names = model.names
    counts = {}
    for class_id in detections:
        name = names[int(class_id)]
        counts[name] = counts.get(name, 0) + 1
    
    inventory_list = []
    for item, count in counts.items():
        status = "✅ AMAN" if count >= 5 else "🚨 LOW STOCK"
        inventory_list.append({"Barang": item.upper(), "Jumlah": count, "Status": status, "Timestamp": datetime.datetime.now()})
    
    df_inventory = pd.DataFrame(inventory_list)
    
    # Save to Database
    if not df_inventory.empty:
        file_exists = os.path.isfile(DB_FILE)
        df_inventory.to_csv(DB_FILE, mode='a', index=False, header=not file_exists)
    
    # Jalankan Forecasting setelah scan
    forecast_text, forecast_plot = predict_demand()
    
    return res_plotted, df_inventory, "✅ Database Updated!", DB_FILE, forecast_text, forecast_plot

# 4. UI GRADIO (TABBED INTERFACE)
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🏙️ IntelliStock AI: End-to-End Warehouse Intelligence")
    
    with gr.Tab("📸 Scan & Monitoring"):
        with gr.Row():
            with gr.Column():
                input_img = gr.Image(type="numpy", label="Input Camera/Upload")
                scan_btn = gr.Button("🔍 Run AI Scan & Update", variant="primary")
            with gr.Column():
                output_img = gr.Image(label="Visual Detection")
                output_table = gr.Dataframe(label="Current Inventory")
        
        with gr.Row():
            output_file = gr.File(label="📥 Download Database (CSV)")
            db_status = gr.Markdown()

    with gr.Tab("🔮 Demand Forecasting"):
        gr.Markdown("### Analisis Prediksi Stok Berdasarkan Histori")
        with gr.Row():
            with gr.Column():
                forecast_output_text = gr.Markdown("Lakukan minimal 3x scan untuk melihat prediksi.")
            with gr.Column():
                forecast_output_plot = gr.Plot(label="Grafik Tren Stok")
        refresh_btn = gr.Button("🔄 Refresh Analisis Prediksi")

    # Click Events
    scan_btn.click(
        fn=process_inventory,
        inputs=input_img,
        outputs=[output_img, output_table, db_status, output_file, forecast_output_text, forecast_output_plot]
    )
    
    refresh_btn.click(fn=predict_demand, outputs=[forecast_output_text, forecast_output_plot])

if __name__ == "__main__":
    demo.launch()