Update app.py

app.py

@@ -1,147 +1,108 @@
-import io
-import random
-from typing import List, Tuple
-
-import aiohttp
-import panel as pn
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
-
-pn.extension(design="bootstrap", sizing_mode="stretch_width")
-
-ICON_URLS = {
-    "brand-github": "https://github.com/holoviz/panel",
-    "brand-twitter": "https://twitter.com/Panel_Org",
-    "brand-linkedin": "https://www.linkedin.com/company/panel-org",
-    "message-circle": "https://discourse.holoviz.org/",
-    "brand-discord": "https://discord.gg/AXRHnJU6sP",
+import dash
+from dash import dcc, html
+from dash.dependencies import Input, Output
+import plotly.express as px
+import pandas as pd
+
+
+layers = ["conv2d5", "conv2d6", "conv2d7", "conv2d8", "conv2d9", "linear1", "linear2"]
+color_dict = {
+    "ClassifierA_Cr203_C6": "#B3262A",
+    "ClassifierA_test_stim": "#2f559a",
+    "ClassifierA_test_unstim": "#5AADC5",
 }
 
+# Initialize Dash app
+app = dash.Dash(__name__)
+
+# App layout
+app.layout = html.Div(
+    [
+        html.Div(
+            id="plot-container",
+            children=[
+                html.P(
+                    children=[
+                        "The original report can be accessed through this ",
+                        html.A(
+                            "BioRxiv Link",
+                            href="https://www.biorxiv.org/content/10.1101/2023.06.01.542416v1.full.pdf",
+                            target="_blank",
+                        ),
+                        " and the data through this ",
+                        html.A(
+                            "GitHub repository",
+                            href="https://github.com/MannLabs/SPARCS_pub_figures",
+                            target="_blank",
+                        ),
+                    ],
+                    id="initial-announcement",
+                ),
+                html.P(
+                    "Please select a layer to view from the dropdown.",
+                    id="initial-message",
+                ),
+            ],
+        ),
+        dcc.Dropdown(
+            id="layer-dropdown",
+            options=[{"label": layer, "value": layer} for layer in layers],
+            value=layers[0],
+        ),
+        dcc.Graph(id="umap-plot"),
+    ]
+)
 
-async def random_url(_):
-    pet = random.choice(["cat", "dog"])
-    api_url = f"https://api.the{pet}api.com/v1/images/search"
-    async with aiohttp.ClientSession() as session:
-        async with session.get(api_url) as resp:
-            return (await resp.json())[0]["url"]
-
-
-@pn.cache
-def load_processor_model(
-    processor_name: str, model_name: str
-) -> Tuple[CLIPProcessor, CLIPModel]:
-    processor = CLIPProcessor.from_pretrained(processor_name)
-    model = CLIPModel.from_pretrained(model_name)
-    return processor, model
-
-
-async def open_image_url(image_url: str) -> Image:
-    async with aiohttp.ClientSession() as session:
-        async with session.get(image_url) as resp:
-            return Image.open(io.BytesIO(await resp.read()))
 
+# Callback to update the plot based on dropdown selection
+@app.callback(Output("umap-plot", "figure"), [Input("layer-dropdown", "value")])
+def update_plot(selected_layer):
 
-def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:
-    processor, model = load_processor_model(
-        "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"
+    input_path = (
+        f"data/classifier_1_Test_Data/UMAP_data/Raw_data_UMAP_{selected_layer}.csv"
     )
-    inputs = processor(
-        text=class_items,
-        images=[image],
-        return_tensors="pt",  # pytorch tensors
+    df_train_umap = pd.read_csv(input_path)
+
+    fig = px.scatter(
+        df_train_umap.sample(frac=1, random_state=19),
+        x="UMAP_1",
+        y="UMAP_2",
+        color="class_label",
+        color_discrete_map=color_dict,
+        title=f"UMAP Test Data Labels - {selected_layer}",
+    )
+    fig.update_traces(
+        marker=dict(size=4, opacity=1, line=dict(width=0)),
+        selector=dict(mode="markers"),
     )
-    outputs = model(**inputs)
-    logits_per_image = outputs.logits_per_image
-    class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()
-    return class_likelihoods[0]
-
 
-async def process_inputs(class_names: List[str], image_url: str):
-    """
-    High level function that takes in the user inputs and returns the
-    classification results as panel objects.
-    """
-    try:
-        main.disabled = True
-        if not image_url:
-            yield "##### ⚠️ Provide an image URL"
-            return
-    
-        yield "##### ⚙ Fetching image and running model..."
-        try:
-            pil_img = await open_image_url(image_url)
-            img = pn.pane.Image(pil_img, height=400, align="center")
-        except Exception as e:
-            yield f"##### 😔 Something went wrong, please try a different URL!"
-            return
-    
-        class_items = class_names.split(",")
-        class_likelihoods = get_similarity_scores(class_items, pil_img)
-    
-        # build the results column
-        results = pn.Column("##### 🎉 Here are the results!", img)
-    
-        for class_item, class_likelihood in zip(class_items, class_likelihoods):
-            row_label = pn.widgets.StaticText(
-                name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center"
-            )
-            row_bar = pn.indicators.Progress(
-                value=int(class_likelihood * 100),
-                sizing_mode="stretch_width",
-                bar_color="secondary",
-                margin=(0, 10),
-                design=pn.theme.Material,
-            )
-            results.append(pn.Column(row_label, row_bar))
-        yield results
-    finally:
-        main.disabled = False
+    # Update layout to have equal aspect ratio
+    fig.update_layout(
+        xaxis=dict(
+            scaleanchor="y",
+            scaleratio=1,
+            dtick=2,
+        ),
+        yaxis=dict(
+            scaleanchor="x",
+            scaleratio=1,
+            dtick=2,
+        ),
+        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+    )
 
+    # Set a constant range for both axes
+    umap_min = min(df_train_umap["UMAP_1"].min(), df_train_umap["UMAP_2"].min())
+    umap_max = max(df_train_umap["UMAP_1"].max(), df_train_umap["UMAP_2"].max())
 
-# create widgets
-randomize_url = pn.widgets.Button(name="Randomize URL", align="end")
+    x_range = [umap_min - 2, umap_max + 2]
+    # y_range = [umap_min - 2, umap_max + 2]
 
-image_url = pn.widgets.TextInput(
-    name="Image URL to classify",
-    value=pn.bind(random_url, randomize_url),
-)
-class_names = pn.widgets.TextInput(
-    name="Comma separated class names",
-    placeholder="Enter possible class names, e.g. cat, dog",
-    value="cat, dog, parrot",
-)
+    fig.update_xaxes(range=x_range, constrain="domain")
+    # fig.update_yaxes(range=y_range, scaleanchor="x", scaleratio=1)
 
-input_widgets = pn.Column(
-    "##### 😊 Click randomize or paste a URL to start classifying!",
-    pn.Row(image_url, randomize_url),
-    class_names,
-)
+    return fig
 
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
-)
-
-# add footer
-footer_row = pn.Row(pn.Spacer(), align="center")
-for icon, url in ICON_URLS.items():
-    href_button = pn.widgets.Button(icon=icon, width=35, height=35)
-    href_button.js_on_click(code=f"window.open('{url}')")
-    footer_row.append(href_button)
-footer_row.append(pn.Spacer())
-
-# create dashboard
-main = pn.WidgetBox(
-    input_widgets,
-    interactive_result,
-    footer_row,
-)
 
-title = "Panel Demo - Image Classification"
-pn.template.BootstrapTemplate(
-    title=title,
-    main=main,
-    main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)
+if __name__ == "__main__":
+    app.run_server(debug=True)