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app.py

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+import os
+import zipfile
+import tempfile
+import uuid
+import rasterio
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+from rasterio.enums import Resampling
+
+def gerar_caminho_temporario(suffix=".tif"):
+    return os.path.join(tempfile.gettempdir(), f"{uuid.uuid4().hex}{suffix}")
+
+def ler_banda(caminho):
+    with rasterio.open(caminho) as src:
+        return src.read(1).astype(np.float32), src.profile
+
+def reamostrar_para_10m(banda_path, ref_shape, ref_transform):
+    with rasterio.open(banda_path) as src:
+        data = src.read(
+            out_shape=(1, ref_shape[0], ref_shape[1]),
+            resampling=Resampling.bilinear
+        )
+        return data[0].astype(np.float32)
+
+def normalizar(arr):
+    arr /= 10000.0
+    return np.clip(arr, 0, 1)
+
+def imagem_para_fig(img, title, cmap=None):
+    fig = plt.figure(figsize=(6, 6))
+    if cmap:
+        plt.imshow(img, cmap=cmap)
+        plt.colorbar()
+    else:
+        plt.imshow(img)
+    plt.title(title)
+    plt.axis('off')
+    return fig
+
+def salvar_tif(path, array, profile, count=3):
+    profile = profile.copy()
+    profile.update({
+        'driver': 'GTiff',
+        'count': count,
+        'dtype': rasterio.float32,
+        'compress': 'deflate',
+        'predictor': 2,
+        'tiled': True,
+        'blockxsize': 512,
+        'blockysize': 512
+    })
+    with rasterio.open(path, 'w', **profile) as dst:
+        if count == 1:
+            dst.write(array, 1)
+        else:
+            for i in range(count):
+                dst.write(array[:, :, i], i + 1)
+
+def processar_visualizacao(zip_file_path, tipo_vis):
+    with tempfile.TemporaryDirectory() as temp_dir:
+        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+            zip_ref.extractall(temp_dir)
+
+        dirs = [d for d in os.listdir(temp_dir) if d.endswith(".SAFE")]
+        if not dirs:
+            raise Exception("Pasta .SAFE não encontrada dentro do zip.")
+        extract_dir = os.path.join(temp_dir, dirs[0])
+
+        granule = os.path.join(extract_dir, "GRANULE")
+        granule_path = [os.path.join(granule, d) for d in os.listdir(granule) if d.startswith("L2A_")][0]
+        img_data_path = os.path.join(granule_path, "IMG_DATA")
+
+        res_paths = {
+            "R10m": os.path.join(img_data_path, "R10m"),
+            "R20m": os.path.join(img_data_path, "R20m")
+        }
+
+        # Referência para geometria
+        b4, prof = ler_banda(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B04" in f][0]))
+
+        if tipo_vis == "RGB Natural (B4, B3, B2)":
+            b2, _ = ler_banda(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B02" in f][0]))
+            b3, _ = ler_banda(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B03" in f][0]))
+            rgb = np.stack([b4, b3, b2], axis=-1)
+            rgb_fig = imagem_para_fig(normalizar(rgb), tipo_vis)
+            tif_path = gerar_caminho_temporario(".tif")
+            salvar_tif(tif_path, rgb, prof, count=3)
+            return rgb_fig, tif_path
+
+        elif tipo_vis == "Falsa Cor Vegetação (B8, B4, B3)":
+            b3, _ = ler_banda(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B03" in f][0]))
+            b8, _ = ler_banda(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B08" in f][0]))
+            fcv = np.stack([b8, b4, b3], axis=-1)
+            fcv_fig = imagem_para_fig(normalizar(fcv), tipo_vis)
+            tif_path = gerar_caminho_temporario(".tif")
+            salvar_tif(tif_path, fcv, prof, count=3)
+            return fcv_fig, tif_path
+
+        elif tipo_vis == "Falso Colorido SWIR (B12, B8, B4)":
+            b8, _ = ler_banda(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B08" in f][0]))
+            b12_path = os.path.join(res_paths["R20m"], [f for f in os.listdir(res_paths["R20m"]) if "_B12" in f][0])
+            b12 = reamostrar_para_10m(b12_path, b4.shape, prof["transform"])
+            fcswir = np.stack([b12, b8, b4], axis=-1)
+            fcswir_fig = imagem_para_fig(normalizar(fcswir), tipo_vis)
+            tif_path = gerar_caminho_temporario(".tif")
+            salvar_tif(tif_path, fcswir, prof, count=3)
+            return fcswir_fig, tif_path
+
+        elif tipo_vis == "NDVI":
+            b8, _ = ler_banda(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B08" in f][0]))
+            ndvi = (b8 - b4) / (b8 + b4 + 1e-6)
+            ndvi_fig = imagem_para_fig(ndvi, "NDVI", cmap='RdYlGn')
+            tif_path = gerar_caminho_temporario(".tif")
+            salvar_tif(tif_path, ndvi, prof, count=1)
+            return ndvi_fig, tif_path
+
+        else:
+            raise ValueError("Tipo de visualização inválido.")
+
+# === Interface Gradio ===
+demo = gr.Interface(
+    fn=processar_visualizacao,
+    inputs=[
+        gr.File(label="Arquivo Sentinel-2 (.zip)", type="filepath"),
+        gr.Dropdown(
+            choices=[
+                "RGB Natural (B4, B3, B2)",
+                "Falsa Cor Vegetação (B8, B4, B3)",
+                "Falso Colorido SWIR (B12, B8, B4)",
+                "NDVI"
+            ],
+            label="Tipo de visualização"
+        )
+    ],
+    outputs=[
+        gr.Plot(label="Visualização"),
+        gr.File(label="Download do GeoTIFF")
+    ],
+    title="Sentinel-2: Visualizar + Exportar GeoTIFF",
+    description="Escolha uma visualização, envie um arquivo .SAFE.zip e baixe o GeoTIFF correspondente."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.0
+rasterio>=1.3
+numpy>=1.21
+matplotlib>=3.5