app.py

app.py

@@ -1,4 +1,22 @@
 import torch
+import numpy as np
+import gradio as gr
+
+# Function to predict the input hours
+def predict_score(x1, x2):
+    Theta0 = torch.tensor(-0.5738734424645411)
+    Theta1 = torch.tensor(2.1659122905141825)
+    Theta2 = torch.tensor(0.0)
+    pred_score = Theta0 + Theta1 * x1 + Theta2 * x2
+    return pred_score.item()
+
+input1 = gr.inputs.Number(label="Number of new students")
+input2 = gr.inputs.Number(label="Number of temperature")
+
+output = gr.outputs.Textbox(label='Predicted Score')
+
+# Gradio interface for the prediction function
+gr.Interface(fn=predict_score, inputs=[input1, input2], outputs=output).launch()
 
 # Input data
 x1 = torch.tensor([50, 60, 70, 80, 90])
@@ -59,4 +77,4 @@ while iter_count < max_iters:
 # Print the final values of Theta0, Theta1, and Theta2
 print("Final values: Theta0 = {}, Theta1 = {}, Theta2 = {}".format(Theta0.item(), Theta1.item(), Theta2.item()))
 print("Final Cost: Cost = {}".format(cost.item()))
-print("Final values: y_pred = {}, y_actual = {}".format(y_pred, y_actual))
+print("Final values: y_pred = {}, y_actual = {}".format(y_pred, y_actual))