playing around with gradio

app.py

@@ -1,16 +1,48 @@
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import numpy as np
+
 import gradio as gr
 
 
-def update(name):
-    return f"Welcome to Gradio, {name}!"
+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
 
 demo = gr.Blocks()
 
 with demo:
     with gr.Tabs():
-        with gr.TabItem("Lion"):
+        with gr.TabItem("Greedy Search"):
             gr.Button("New Lion")
-        with gr.TabItem("Tiger"):
+        with gr.TabItem("Sample"):
+            gr.Button("New Tiger")
+        with gr.TabItem("Beam Search"):
             gr.Button("New Tiger")
+        with gr.TabItem("Benchmark Information"):
+            gr.Interface(
+                sales_projections,
+                gr.Dataframe(
+                    headers=["Name", "Jan Sales", "Feb Sales", "Mar Sales"],
+                    value=[["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.",
+            )
 
 demo.launch()