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

# Step 1: Setup Kaggle API
# Ensure the .kaggle directory exists
kaggle_dir = os.path.expanduser("~/.kaggle")
if not os.path.exists(kaggle_dir):
    os.makedirs(kaggle_dir)

# Step 2: 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.")
    
# Step 3: 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)

# Step 4: 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.")






# Load your 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 without the weights_only argument
    model.load_state_dict(torch.load('resnet50_garbage_classification.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
    'biological': 'Cokelat (Brown)',  # Limbah organik
    'brown-glass': 'Hijau (Green)',   # Gelas berwarna coklat
    'cardboard': 'Kuning (Yellow)',    # Limbah daur ulang
    'clothes': 'Biru (Blue)',         # Pakaian dan tekstil
    'green-glass': 'Hijau (Green)',    # Gelas berwarna hijau
    'metal': 'Kuning (Yellow)',        # Limbah daur ulang
    'paper': 'Kuning (Yellow)',        # Limbah daur ulang
    'plastic': 'Kuning (Yellow)',      # Limbah daur ulang
    'shoes': 'Biru (Blue)',           # Pakaian dan tekstil
    'trash': 'Hitam (Black)',         # Limbah umum
    'white-glass': 'Putih (White)'    # Gelas berwarna putih
}

# 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",
    description="Unggah gambar sampah, dan model akan mengklasifikasikannya ke dalam salah satu dari 12 kategori bersama dengan warna tempat sampah yang sesuai."
)


iface.launch(share=True)