import os
import shutil
import subprocess
import zipfile
import time
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms, models
from torch.optim import lr_scheduler
import subprocess
import zipfile
from PIL import Image
import gradio as gr

# Setup Kaggle API
kaggle_dir = os.path.expanduser("~/.kaggle")
if not os.path.exists(kaggle_dir):
    os.makedirs(kaggle_dir)

# Copy the kaggle.json file to the ~/.kaggle directory
kaggle_json_path = "kaggle.json"
kaggle_dest_path = os.path.join(kaggle_dir, "kaggle.json")

if not os.path.exists(kaggle_dest_path):
    shutil.copy(kaggle_json_path, kaggle_dest_path)
    os.chmod(kaggle_dest_path, 0o600)
    print("Kaggle API key copied and permissions set.")
else:
    print("Kaggle API key already exists.")
    
# Download the dataset from Kaggle using Kaggle CLI
dataset_name = "mostafaabla/garbage-classification"
print(f"Downloading the dataset: {dataset_name}")
download_command = f"kaggle datasets download -d {dataset_name}"

# Run the download command
subprocess.run(download_command, shell=True)
# Unzip the downloaded dataset
dataset_zip = "garbage-classification.zip"
extracted_folder = "./garbage-classification"

# Check if the zip file exists
if os.path.exists(dataset_zip):
    if not os.path.exists(extracted_folder):
        with zipfile.ZipFile(dataset_zip, 'r') as zip_ref:
            zip_ref.extractall(extracted_folder)
            print("Dataset unzipped successfully!")
    else:
        print("Dataset already unzipped.")
else:
    print(f"Dataset zip file '{dataset_zip}' not found.")


# Model training and testing in separate directory at ipynb file (Copy of ai-portfolio Kendrick.ipynb)

from PIL import Image
import gradio as gr

# Load model
def load_model():
    model = models.resnet50(weights='DEFAULT')  # Using default weights for initialization
    num_ftrs = model.fc.in_features
    model.fc = nn.Linear(num_ftrs, 12)  # Adjust to the number of classes you have
    
    # Load the state dict
    model.load_state_dict(torch.load('resnet50_garbage_classificationv1.2.pth', map_location=torch.device('cpu')))
    
    model.eval()  # Set to evaluation mode
    return model

model = load_model()

# Define image transformations
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# Class names
class_names = ['battery', 'biological', 'brown-glass', 'cardboard', 
               'clothes', 'green-glass', 'metal', 'paper', 
               'plastic', 'shoes', 'trash', 'white-glass']

# Define bin colors for each class
bin_colors = {
    'battery': 'Merah (Red)',                # Limbah berbahaya (B3)
    'biological': 'Hijau (Green)',           # Limbah organik
    'brown-glass': 'Kuning (Yellow or trash banks / recycling centers)',  # Gelas berwarna coklat (anorganik/daur ulang)
    'cardboard': 'Biru (Blue)',              # Kertas (daur ulang)
    'clothes': 'Kuning atau Bank Sampah (Yellow or trash banks / recycling centers)',  # Pakaian (dimasukkan sebagai daur ulang)
    'green-glass': 'Kuning (Yellow)',        # Gelas berwarna hijau (anorganik/daur ulang)
    'metal': 'Kuning (Yellow)',              # Logam (anorganik/daur ulang)
    'paper': 'Biru (Blue)',                  # Kertas (daur ulang)
    'plastic': 'Kuning (Yellow)',            # Plastik (anorganik/daur ulang)
    'shoes': 'Kuning atau Bank Sampah (Yellow or trash banks / recycling centers)',  # Sepatu (dimasukkan sebagai daur ulang)
    'trash': 'Abu-abu (Gray)',               # Limbah umum
    'white-glass': 'Kuning (Yellow or trash banks / recycling centers)'         # Gelas berwarna putih (anorganik/daur ulang)
}

# Define the prediction function
def predict(image):
    image = Image.fromarray(image)  # Convert numpy array to PIL Image
    image = transform(image)  # Apply transformations
    image = image.unsqueeze(0)  # Add batch dimension
    
    with torch.no_grad():
        outputs = model(image)
        _, predicted = torch.max(outputs, 1)
    
    class_name = class_names[predicted.item()]  # Return predicted class name
    bin_color = bin_colors[class_name]  # Get the corresponding bin color
    return class_name, bin_color  # Return both class name and bin color

# Make Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="numpy", label="Unggah Gambar"),
    outputs=[
        gr.Textbox(label="Jenis Sampah"), 
        gr.Textbox(label="Tong Sampah yang Sesuai")  # 2 output with label
    ],
    title="Klasifikasi Sampah dengan ResNet50 v1.2",
    description="Unggah gambar sampah, dan model kami akan mengklasifikasikannya ke dalam salah satu dari 12 kategori bersama dengan warna tempat sampah yang sesuai. "
                "<strong>Model ini bisa memprediksi jenis sampah dari ke-12 jenis berikut:</strong> Baterai, Sampah organik, Gelas Kaca Coklat, "
                "Kardus, Pakaian, Gelas Kaca Hijau, Metal, Kertas, Plastik, Sepatu/sandal, Popok/pampers, Gelas Kaca bening. "
                "<strong>Note: Untuk masker dan pampers dikategorikan sebagai trash</strong>"
)

iface.launch(share=True)