app.py

import gradio as gr
import torch
import numpy as np
from PIL import Image
import torchvision.transforms as transforms
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torch.optim.lr_scheduler import ReduceLROnPlateau
import numpy as np
import pandas as pd
from PIL import Image
import os
import torchvision.transforms as transforms
from sklearn.model_selection import train_test_split

class ClimateNet(nn.Module):
    def __init__(self, input_size=(256, 256), output_size=(64, 64)):
        super(ClimateNet, self).__init__()
        self.input_size = input_size
        self.output_size = output_size

        # Feature map sizes after two max pooling layers
        self.feature_size = (input_size[0] // 4, input_size[1] // 4)

        # Improved RGB Encoder with residual connections
        self.rgb_encoder = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout2d(0.2),

            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout2d(0.2)
        )

        # Improved NDVI Encoder
        self.ndvi_encoder = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout2d(0.2),

            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout2d(0.2)
        )

        # Improved Terrain Encoder
        self.terrain_encoder = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout2d(0.2),

            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout2d(0.2)
        )

        # Improved Weather Encoder with deeper architecture
        self.weather_encoder = nn.Sequential(
            nn.Linear(4, 64),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(64, 128),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(128, 128)
        )

        # Improved Feature Fusion
        self.fusion = nn.Sequential(
            nn.Conv2d(512, 512, kernel_size=1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Dropout2d(0.2),
            nn.Conv2d(512, 512, kernel_size=1),
            nn.BatchNorm2d(512),
            nn.ReLU()
        )

        # Improved Decoders with skip connections
        self.wind_decoder = nn.Sequential(
            nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Dropout2d(0.2),
            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Dropout2d(0.2),
            nn.Conv2d(128, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, 1, kernel_size=1),
            nn.Sigmoid()
        )

        self.solar_decoder = nn.Sequential(
            nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Dropout2d(0.2),
            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Dropout2d(0.2),
            nn.Conv2d(128, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, 1, kernel_size=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        batch_size = x['rgb'].size(0)

        # Resize all inputs to input_size
        rgb_input = F.interpolate(x['rgb'], size=self.input_size, mode='bilinear', align_corners=False)
        ndvi_input = F.interpolate(x['ndvi'], size=self.input_size, mode='bilinear', align_corners=False)
        terrain_input = F.interpolate(x['terrain'], size=self.input_size, mode='bilinear', align_corners=False)

        # Extract features
        rgb_features = self.rgb_encoder(rgb_input)  # [B, 128, H/4, W/4]
        ndvi_features = self.ndvi_encoder(ndvi_input)  # [B, 128, H/4, W/4]
        terrain_features = self.terrain_encoder(terrain_input)  # [B, 128, H/4, W/4]

        # Process weather features and expand to match feature map size
        weather_features = self.weather_encoder(x['weather_features'])  # [B, 128]
        weather_features = weather_features.view(batch_size, 128, 1, 1)
        weather_features = F.interpolate(
            weather_features,
            size=self.feature_size,
            mode='nearest'
        )

        # Combine features
        combined_features = torch.cat([
            rgb_features,
            ndvi_features,
            terrain_features,
            weather_features
        ], dim=1)

        # Apply fusion
        fused_features = self.fusion(combined_features)

        # Generate predictions and resize to output_size
        wind_heatmap = self.wind_decoder(fused_features)
        solar_heatmap = self.solar_decoder(fused_features)

        wind_heatmap = F.interpolate(wind_heatmap, size=self.output_size, mode='bilinear', align_corners=False)
        solar_heatmap = F.interpolate(solar_heatmap, size=self.output_size, mode='bilinear', align_corners=False)

        return wind_heatmap, solar_heatmap

class ClimatePredictor:
    def __init__(self, model_path, device=None):
        if device is None:
            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        else:
            self.device = device
            
        print(f"Using device: {self.device}")
        
        # Load model
        self.model = ClimateNet(input_size=(256, 256), output_size=(64, 64)).to(self.device)
        checkpoint = torch.load(model_path, map_location=self.device)
        
        if "module" in list(checkpoint['model_state_dict'].keys())[0]:
            self.model = torch.nn.DataParallel(self.model)
        
        self.model.load_state_dict(checkpoint['model_state_dict'])
        self.model.eval()
        
        self.rgb_transform = transforms.Compose([
            transforms.Resize((256, 256)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        ])
        
        self.single_channel_transform = transforms.Compose([
            transforms.Resize((256, 256)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.5], std=[0.5])
        ])

    def predict_from_inputs(self, rgb_image, ndvi_image, terrain_image, 
                          elevation_data, wind_speed, wind_direction, 
                          temperature, humidity):
        """Gradio 인터페이스용 예측 함수"""
        # 이미지 전처리
        rgb_tensor = self.rgb_transform(Image.fromarray(rgb_image)).unsqueeze(0)
        ndvi_tensor = self.single_channel_transform(Image.fromarray(ndvi_image)).unsqueeze(0)
        terrain_tensor = self.single_channel_transform(Image.fromarray(terrain_image)).unsqueeze(0)
        
        # 고도 데이터 처리
        elevation_tensor = torch.from_numpy(elevation_data).float().unsqueeze(0).unsqueeze(0)
        elevation_tensor = (elevation_tensor - elevation_tensor.min()) / (elevation_tensor.max() - elevation_tensor.min())
        
        # 기상 데이터 처리
        weather_features = np.array([wind_speed, wind_direction, temperature, humidity])
        weather_features = (weather_features - weather_features.min()) / (weather_features.max() - weather_features.min())
        weather_features = torch.tensor(weather_features, dtype=torch.float32).unsqueeze(0)
        
        # 디바이스로 이동
        sample = {
            'rgb': rgb_tensor.to(self.device),
            'ndvi': ndvi_tensor.to(self.device),
            'terrain': terrain_tensor.to(self.device),
            'elevation': elevation_tensor.to(self.device),
            'weather_features': weather_features.to(self.device)
        }
        
        # 예측
        with torch.no_grad():
            wind_pred, solar_pred = self.model(sample)
        
        # 결과를 numpy 배열로 변환
        wind_map = wind_pred.cpu().numpy()[0, 0]
        solar_map = solar_pred.cpu().numpy()[0, 0]
        
        # 결과 시각화
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
        
        # 풍력 발전 잠재량 시각화
        sns.heatmap(wind_map, ax=ax1, cmap='YlOrRd', cbar_kws={'label': 'Wind Power Potential'})
        ax1.set_title('Wind Power Potential Map')
        
        # 태양광 발전 잠재량 시각화
        sns.heatmap(solar_map, ax=ax2, cmap='YlOrRd', cbar_kws={'label': 'Solar Power Potential'})
        ax2.set_title('Solar Power Potential Map')
        
        plt.tight_layout()
        
        return fig

# Gradio 인터페이스 생성
def create_gradio_interface():
    predictor = ClimatePredictor('best_model.pth')
    
    def predict_and_visualize(rgb_image, ndvi_image, terrain_image, elevation_file,
                            wind_speed, wind_direction, temperature, humidity):
        # Load elevation data
        elevation_data = np.load(elevation_file.name)
        
        # Generate prediction and visualization
        result = predictor.predict_from_inputs(
            rgb_image, ndvi_image, terrain_image, elevation_data,
            wind_speed, wind_direction, temperature, humidity
        )
        return result
    
    interface = gr.Interface(
        fn=predict_and_visualize,
        inputs=[
            gr.Image(label="RGB Satellite Image", type="numpy"),
            gr.Image(label="NDVI Image", type="numpy"),
            gr.Image(label="Terrain Map", type="numpy"),
            gr.File(label="Elevation Data (NPY file)"),
            gr.Number(label="Wind Speed (m/s)", value=5.0),
            gr.Number(label="Wind Direction (degrees)", value=180.0),
            gr.Number(label="Temperature (°C)", value=25.0),
            gr.Number(label="Humidity (%)", value=60.0)
        ],
        outputs=gr.Plot(label="Prediction Results"),
        title="Renewable Energy Potential Predictor",
        description="Upload satellite imagery and environmental data to predict wind and solar power potential.",
        examples=[
            [
                "examples/rgb_example.png",
                "examples/ndvi_example.png",
                "examples/terrain_example.png",
                "examples/elevation_example.npy",
                5.0, 180.0, 25.0, 60.0
            ]
        ]
    )
    return interface

if __name__ == "__main__":
    interface = create_gradio_interface()
    interface.launch()s