update_front

app.py

@@ -36,11 +36,14 @@ import gradio as gr
 from functools import partial
 
 import pdb
-
 import matplotlib.pyplot as plt
 
 from skimage import exposure 
 
+import pandas as pd
+from vega_datasets import data
+
+
 cdl_color_map = [{'value': 1, 'label': 'Natural vegetation', 'rgb': (233,255,190)},
                  {'value': 2, 'label': 'Forest', 'rgb': (149,206,147)},
                  {'value': 3, 'label': 'Corn', 'rgb': (255,212,0)},
@@ -242,6 +245,22 @@ custom_test_pipeline=process_test_pipeline(model.cfg.data.test.pipeline, None)
 
 func = partial(inference_on_file, model=model, custom_test_pipeline=custom_test_pipeline)
 
+
+stocks = data.stocks()
+gapminder = data.gapminder()
+gapminder = gapminder.loc[
+    gapminder.country.isin(["Argentina", "Australia", "Afghanistan"])
+]
+climate = data.climate()
+seattle_weather = data.seattle_weather()
+
+simple = pd.DataFrame(
+    {
+        "a": ["A", "B", "C", "D", "E", "F", "G", "H", "I"],
+        "b": [28, 55, 43, 91, 81, 53, 19, 87, 52],
+    }
+)
+
 with gr.Blocks() as demo:
    
     gr.Markdown(value='# Prithvi multi temporal crop classification')
@@ -253,16 +272,29 @@ with gr.Blocks() as demo:
             inp = gr.File()
             btn = gr.Button("Submit")
             
-    with gr.Row():
-        inp1=gr.Image(image_mode='RGB', scale=10, label='T1')
-        inp2=gr.Image(image_mode='RGB', scale=10, label='T2')
-        inp3=gr.Image(image_mode='RGB', scale=10, label='T3')
-        out = gr.Image(image_mode='RGB', scale=10, label='Model prediction')
-        # gr.Image(value='Legend.png', image_mode='RGB', scale=2, show_label=False)
+        with gr.Column():
+            inp1=gr.Image(image_mode='RGB', scale=10, label='T1')
+            inp2=gr.Image(image_mode='RGB', scale=10, label='T2')
+            inp3=gr.Image(image_mode='RGB', scale=10, label='T3')
+            out = gr.Image(image_mode='RGB', scale=10, label='Model prediction')
+            # gr.Image(value='Legend.png', image_mode='RGB', scale=2, show_label=False)
     
     btn.click(fn=func, inputs=inp, outputs=[inp1, inp2, inp3, out])
-    
+
     with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                gr.BarPlot(simple,
+                        x="a",
+                        y="b",
+                        title="Simple Bar Plot with made up data",
+                        tooltip=["a", "b"],
+                        y_lim=[20, 100],)
+            with gr.Row():
+                gr.LinePlot(simple,
+                            x='a',
+                            y='b')
+
         with gr.Column():
             gr.Markdown(value='### Model prediction legend')
             gr.Image(value='Legend.png', image_mode='RGB', show_label=False)