Update app.py

app.py

@@ -215,7 +215,6 @@ class ClimatePredictor:
     def convert_to_single_channel(self, image_array):
         """RGB 이미지를 단일 채널로 변환"""
         if len(image_array.shape) == 3:
-            # RGB to grayscale conversion
             return np.dot(image_array[...,:3], [0.2989, 0.5870, 0.1140])
         return image_array
 
@@ -235,11 +234,6 @@ class ClimatePredictor:
             terrain_gray = self.convert_to_single_channel(terrain_image)
             terrain_tensor = self.single_channel_transform(Image.fromarray(terrain_gray.astype(np.uint8))).unsqueeze(0)
             
-            # Print shapes for debugging
-            print(f"RGB tensor shape: {rgb_tensor.shape}")
-            print(f"NDVI tensor shape: {ndvi_tensor.shape}")
-            print(f"Terrain tensor shape: {terrain_tensor.shape}")
-            
             # 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())
@@ -269,11 +263,9 @@ class ClimatePredictor:
             # 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')
             
@@ -284,27 +276,72 @@ class ClimatePredictor:
             print(f"Error in prediction: {str(e)}")
             raise e
 
+def load_examples_from_directory(base_dir):
+    """폴더에서 예제 데이터 로드"""
+    examples = []
+    sample_dirs = sorted(glob.glob(os.path.join(base_dir, "sample_*")))
+    
+    for sample_dir in sample_dirs:
+        try:
+            # 파일 경로 구성
+            rgb_path = os.path.join(sample_dir, "satellite", "sentinel2_rgb_2023-07-15_to_2023-09-01.png")
+            ndvi_path = os.path.join(sample_dir, "satellite", "sentinel2_ndvi_2023-07-15_to_2023-09-01.png")
+            terrain_path = os.path.join(sample_dir, "terrain", "terrain_map.png")
+            elevation_path = os.path.join(sample_dir, "terrain", "elevation_data.npy")
+            weather_path = os.path.join(sample_dir, "weather", "weather_data.csv")
+            
+            # 기상 데이터 읽기
+            weather_data = pd.read_csv(weather_path)
+            wind_speed = weather_data['wind_speed'].mean()
+            wind_direction = weather_data['wind_direction'].mean()
+            temperature = weather_data['temperature'].mean()
+            humidity = weather_data['humidity'].mean()
+            
+            # 예제 리스트에 추가
+            examples.append([
+                rgb_path,
+                ndvi_path,
+                terrain_path,
+                elevation_path,
+                float(wind_speed),
+                float(wind_direction),
+                float(temperature),
+                float(humidity)
+            ])
+        except Exception as e:
+            print(f"Error loading example from {sample_dir}: {str(e)}")
+            continue
+    
+    return examples
+
 def create_gradio_interface():
     predictor = ClimatePredictor('best_model.pth')
     
-    def predict_and_visualize(rgb_image, ndvi_image, terrain_image, elevation_file,
+    def predict_and_visualize(rgb_path, ndvi_path, terrain_path, elevation_path,
                             wind_speed, wind_direction, temperature, humidity):
-        # Load elevation data
-        elevation_data = np.load(elevation_file.name)
+        # 이미지 로드
+        rgb_image = np.array(Image.open(rgb_path))
+        ndvi_image = np.array(Image.open(ndvi_path))
+        terrain_image = np.array(Image.open(terrain_path))
+        elevation_data = np.load(elevation_path)
         
-        # Generate prediction and visualization
+        # 예측 및 시각화
         result = predictor.predict_from_inputs(
             rgb_image, ndvi_image, terrain_image, elevation_data,
             wind_speed, wind_direction, temperature, humidity
         )
         return result
     
+    # 예제 데이터 로드
+    examples = load_examples_from_directory("filtered_climate_data")
+    print(f"Loaded {len(examples)} examples")
+    
     interface = gr.Interface(
         fn=predict_and_visualize,
         inputs=[
-            gr.Image(label="RGB Satellite Image", type="numpy"),
-            gr.Image(label="NDVI Image (will be converted to grayscale)", type="numpy"),
-            gr.Image(label="Terrain Map (will be converted to grayscale)", type="numpy"),
+            gr.Image(label="RGB Satellite Image", type="filepath"),
+            gr.Image(label="NDVI Image", type="filepath"),
+            gr.Image(label="Terrain Map", type="filepath"),
             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),
@@ -314,20 +351,12 @@ def create_gradio_interface():
         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.
-        Note: NDVI and Terrain images will be automatically converted to grayscale.""",
-        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
-            ]
-        ]
+        You can also try various examples from our dataset using the Examples section below.""",
+        examples=examples,
+        cache_examples=True
     )
     return interface
 
-# Hugging Face Spaces에서 앱 실행
 if __name__ == "__main__":
     interface = create_gradio_interface()
     interface.launch()