initial commit

app.py

@@ -1,151 +1,92 @@
-from pathlib import Path
-from typing import List, Dict, Tuple
-import matplotlib.colors as mpl_colors
-
 import pandas as pd
-import seaborn as sns
-import shinyswatch
-
-from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
-
-sns.set_theme()
-
-www_dir = Path(__file__).parent.resolve() / "www"
-
-df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
-numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()
-species: List[str] = df["Species"].unique().tolist()
-species.sort()
-
-app_ui = ui.page_fillable(
-    shinyswatch.theme.minty(),
-    ui.layout_sidebar(
-        ui.sidebar(
-            # Artwork by @allison_horst
-            ui.input_selectize(
-                "xvar",
-                "X variable",
-                numeric_cols,
-                selected="Bill Length (mm)",
-            ),
-            ui.input_selectize(
-                "yvar",
-                "Y variable",
-                numeric_cols,
-                selected="Bill Depth (mm)",
-            ),
-            ui.input_checkbox_group(
-                "species", "Filter by species", species, selected=species
-            ),
-            ui.hr(),
-            ui.input_switch("by_species", "Show species", value=True),
-            ui.input_switch("show_margins", "Show marginal plots", value=True),
-        ),
-        ui.output_ui("value_boxes"),
-        ui.output_plot("scatter", fill=True),
-        ui.help_text(
-            "Artwork by ",
-            ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
-            class_="text-end",
-        ),
-    ),
+import tempfile
+from PIL import Image
+from pathlib import Path
+from shiny import App, Inputs, Outputs, Session, reactive, render, ui
+from shiny.types import FileInfo
+import json
+import torch
+import numpy as np
+import os
+from transformers import SamModel
+import torchvision.transforms as transforms
+import matplotlib.pyplot as plt
+
+image_resize_transform = transforms.Compose([
+    transforms.Resize((1024, 1024)), 
+    transforms.ToTensor()  
+])
+
+app_ui = ui.page_fluid(
+    ui.input_file("file2", "Choose Image", accept=".jpg, .jpeg, .png, .tiff, .tif", multiple=False),
+    ui.output_image("original_image"),  
+    ui.output_image("image_display")    
 )
 
-
 def server(input: Inputs, output: Outputs, session: Session):
-    @reactive.Calc
-    def filtered_df() -> pd.DataFrame:
-        """Returns a Pandas data frame that includes only the desired rows"""
-
-        # This calculation "req"uires that at least one species is selected
-        req(len(input.species()) > 0)
-
-        # Filter the rows so we only include the desired species
-        return df[df["Species"].isin(input.species())]
-
-    @output
-    @render.plot
-    def scatter():
-        """Generates a plot for Shiny to display to the user"""
-
-        # The plotting function to use depends on whether margins are desired
-        plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot
-
-        plotfunc(
-            data=filtered_df(),
-            x=input.xvar(),
-            y=input.yvar(),
-            palette=palette,
-            hue="Species" if input.by_species() else None,
-            hue_order=species,
-            legend=False,
-        )
-
-    @output
-    @render.ui
-    def value_boxes():
-        df = filtered_df()
-
-        def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
-            return ui.value_box(
-                title,
-                count,
-                {"class_": "pt-1 pb-0"},
-                showcase=ui.fill.as_fill_item(
-                    ui.tags.img(
-                        {"style": "object-fit:contain;"},
-                        src=showcase_img,
-                    )
-                ),
-                theme_color=None,
-                style=f"background-color: {bgcol};",
-            )
-
-        if not input.by_species():
-            return penguin_value_box(
-                "Penguins",
-                len(df.index),
-                bg_palette["default"],
-                # Artwork by @allison_horst
-                showcase_img="penguins.png",
-            )
-
-        value_boxes = [
-            penguin_value_box(
-                name,
-                len(df[df["Species"] == name]),
-                bg_palette[name],
-                # Artwork by @allison_horst
-                showcase_img=f"{name}.png",
-            )
-            for name in species
-            # Only include boxes for _selected_ species
-            if name in input.species()
-        ]
-
-        return ui.layout_column_wrap(*value_boxes, width = 1 / len(value_boxes))
-
-
-# "darkorange", "purple", "cyan4"
-colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
-colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]
-
-palette: Dict[str, Tuple[float, float, float]] = {
-    "Adelie": colors[0],
-    "Chinstrap": colors[1],
-    "Gentoo": colors[2],
-    "default": sns.color_palette()[0],  # type: ignore
-}
-
-bg_palette = {}
-# Use `sns.set_style("whitegrid")` to help find approx alpha value
-for name, col in palette.items():
-    # Adjusted n_colors until `axe` accessibility did not complain about color contrast
-    bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1])  # type: ignore
-
-
-app = App(
-    app_ui,
-    server,
-    static_assets=str(www_dir),
-)
+    @reactive.calc
+    def loaded_image():
+        file: list[FileInfo] | None = input.file2()
+        if file is None:
+            return None
+        
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        model2 = SamModel.from_pretrained("facebook/sam-vit-base")
+        model2.load_state_dict(torch.load('model.pth', map_location=device))
+        model2.eval()
+        model2.to(device)
+
+        image = Image.open(file[0]["datapath"]).convert('RGB')
+        transform = image_resize_transform
+        image_tensor = transform(image).to(device)
+
+        
+        with torch.no_grad():
+            outputs = model2(pixel_values=image_tensor.unsqueeze(0),multimask_output=False)
+        predicted_masks = outputs.pred_masks.squeeze(1) 
+        predicted_masks = predicted_masks[:, 0, :, :]
+
+        
+        mask_tensor = predicted_masks.cpu().detach().squeeze()
+        mask_array = mask_tensor.numpy()  
+        mask_array = (mask_array * 255).astype(np.uint8)  
+        mask = Image.fromarray(mask_array)
+        mask = mask.resize((1024, 1024), Image.LANCZOS)
+        mask = mask.convert('RGBA')
+
+        
+        alpha = Image.new('L', mask.size, 128)
+        mask.putalpha(alpha)
+
+        
+        image = Image.open(file[0]["datapath"]).convert('RGB')  
+        image = image.resize((1024, 1024), Image.LANCZOS)  
+        image = image.convert('RGBA')
+
+        combined = Image.alpha_composite(image, mask)
+
+
+        combined_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
+        original_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
+        image.save(original_file.name, "PNG", quality=100)
+        mask.save(combined_file.name, "PNG", quality=100)
+        
+        return original_file.name, combined_file.name
+
+    @render.image
+    def original_image():
+        result = loaded_image()
+        if result is None:
+            return None
+        img_path, _ = result
+        return {"src": img_path, "width": "300px"}
+
+    @render.image
+    def image_display():
+        result = loaded_image()
+        if result is None:
+            return None
+        _, img_path = result
+        return {"src": img_path, "width": "300px"}
+
+app = App(app_ui, server)