gradio image app

Procfile

@@ -1 +1 @@
-web: source setup.sh && python app_table.py
+web: source setup.sh && python app_savta.py

app_savta.py

@@ -0,0 +1,17 @@
+import numpy as np
+
+import gradio as gr
+
+
+def sepia(input_img):
+    sepia_filter = np.array(
+        [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]
+    )
+    sepia_img = input_img.dot(sepia_filter.T)
+    sepia_img /= sepia_img.max()
+    return sepia_img
+
+
+iface = gr.Interface(sepia, gr.inputs.Image(shape=(200, 200)), "image")
+
+iface.launch(auth=("admin", "pass1234"))

app_table.py

@@ -1,36 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-
-import gradio as gr
-
-
-def sales_projections(employee_data):
-    sales_data = employee_data.iloc[:, 1:4].astype("int").to_numpy()
-    regression_values = np.apply_along_axis(
-        lambda row: np.array(np.poly1d(np.polyfit([0, 1, 2], row, 2))), 0, sales_data
-    )
-    projected_months = np.repeat(
-        np.expand_dims(np.arange(3, 12), 0), len(sales_data), axis=0
-    )
-    projected_values = np.array(
-        [
-            month * month * regression[0] + month * regression[1] + regression[2]
-            for month, regression in zip(projected_months, regression_values)
-        ]
-    )
-    plt.plot(projected_values.T)
-    plt.legend(employee_data["Name"])
-    return employee_data, plt.gcf(), regression_values
-
-
-iface = gr.Interface(
-    sales_projections,
-    gr.inputs.Dataframe(
-        headers=["Name", "Jan Sales", "Feb Sales", "Mar Sales"],
-        default=[["Jon", 12, 14, 18], ["Alice", 14, 17, 2], ["Sana", 8, 9.5, 12]],
-    ),
-    ["dataframe", "plot", "numpy"],
-    description="Enter sales figures for employees to predict sales trajectory over year.",
-)
-iface.launch(auth=("admin", "pass1234"))