Delete app.py

app.py

@@ -1,145 +0,0 @@
-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()