Update app.py

app.py

@@ -1,151 +1,81 @@
-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 asyncio
+import matplotlib.pyplot as plt
+import numpy as np
+from transformers import SamModel, SamConfig, SamProcessor
+import torch
+from shiny import App, Inputs, Outputs, Session, reactive, render, ui
+from PIL import Image
+
+app_ui = ui.page_fluid(
+    ui.input_file("file1", "Upload Tile image for sidewalk segmentation", accept=".tif", multiple=False),
+    ui.output_plot("mask"),  # Changed from ui.output_table to ui.output_plot based on the context of output
 )
 
-
 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())]
-
+    @reactive.calc
+    def parsed_file():
+        file_info = input.file1()
+        if file_info is None or len(file_info) == 0:
+            return None
+        return file_info[0]["datapath"]
+    
     @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),
-)
+    async def mask():
+        filepath = parsed_file()
+        if filepath is None:
+            return
+        print(filepath)
+        # Assuming the model and processor are correctly configured
+        model_config = SamConfig.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+        my_sidewalk_model = SamModel(model_config)
+        my_sidewalk_model.load_state_dict(torch.load("./sidwalk_model_checkpoint.pth", map_location='cpu'))
+        device = torch.device("cpu")
+        my_sidewalk_model.to(device)
+
+        # Load image
+        image = Image.open(filepath)
+        imarray = np.array(image)
+        single_patch = Image.fromarray(imarray)
+
+        inputs = processor(single_patch, return_tensors="pt")
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+        my_sidewalk_model.eval()
+        # Model inference
+        with torch.no_grad():
+            outputs = my_sidewalk_model(**inputs, multimask_output=False)
+        single_patch_prob = torch.sigmoid(outputs.pred_masks.squeeze(1))
+        # convert soft mask to hard mask
+        single_patch_prob = single_patch_prob.cpu().numpy().squeeze()
+        single_patch_prediction = (single_patch_prob > 0).astype(np.uint8)
+
+
+        fig, axes = plt.subplots(1, 3, figsize=(15, 5))
+
+        # Plot the first image on the left
+        axes[0].imshow(np.array(single_patch), cmap='gray')  # Assuming the first image is grayscale
+        axes[0].set_title("Image")
+
+        # Plot the second image on the right
+        axes[1].imshow(single_patch_prob)  # Assuming the second image is grayscale
+        axes[1].set_title("Probability Map")
+
+        # Plot the second image on the right
+        axes[2].imshow(single_patch_prediction, cmap='gray')  # Assuming the second image is grayscale
+        axes[2].set_title("Prediction")
+
+        # Hide axis ticks and labels
+        for ax in axes:
+            ax.set_xticks([])
+            ax.set_yticks([])
+            ax.set_xticklabels([])
+            ax.set_yticklabels([])
+
+        # Display the images side by side
+        return fig
+
+    
+
+app = App(app_ui, server)