app.py

app.py

@@ -1,60 +1,14 @@
-# Input data
-x1 = torch.tensor([50, 60, 70, 80, 90])
-x2 = torch.tensor([20, 21, 22, 23, 24])
-y_actual = torch.tensor([30, 35, 40, 45, 50])
-
-# Learning rate and maximum number of iterations
-alpha = 0.01
-max_iters = 1000
-
-# Initial values for Theta0, Theta1, and Theta2
-Theta0 = torch.tensor(0.0, requires_grad=True)
-Theta1 = torch.tensor(0.0, requires_grad=True)
-Theta2 = torch.tensor(0.0, requires_grad=True)
-
-# Start the iteration counter
-iter_count = 0
-
-# Loop until convergence or maximum number of iterations
-while iter_count < max_iters:
-    # Compute the predicted output
-    y_pred = Theta0 + Theta1 * x1 + Theta2 * x2
-
-    # Compute the errors
-    errors = y_pred - y_actual
-
-    # Compute the cost function
-    cost = torch.sum(errors ** 2) / (2 * len(x1))
-
-    # Print the cost function every 100 iterations
-    if iter_count % 100 == 0:
-        print("Iteration {}: Cost = {}, Theta0 = {}, Theta1 = {}, Theta2 = {}".format(iter_count, cost, Theta0.item(), Theta1.item(),
-                                                                                      Theta2.item()))
-
-    # Check for convergence (if the cost is decreasing by less than 0.0001)
-    if iter_count > 0 and torch.abs(cost - prev_cost) < 0.0001:
-        print("Converged after {} iterations".format(iter_count))
-        break
-
-    # Perform automatic differentiation to compute gradients
-    cost.backward()
-
-    # Update Theta0, Theta1, and Theta2 using gradient descent
-    with torch.no_grad():
-        Theta0 -= alpha * Theta0.grad
-        Theta1 -= alpha * Theta1.grad
-        Theta2 -= alpha * Theta2.grad
-
-        # Reset gradients for the next iteration
-        Theta0.grad.zero_()
-        Theta1.grad.zero_()
-        Theta2.grad.zero_()
-
-    # Update the iteration counter and previous cost
-    iter_count += 1
-    prev_cost = cost
-
-# 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))
+pip install numpy
+pip install gradio
+import numpy as np
+import gradio as gr
+#function to predict the input hours
+def predict_score(hours):
+    #hours = np.array(hours)
+    pred_score = -0.5738734424645411 + 2.1659122905141825*hours
+    return   pred_score     #np.round(pred_score[0], 2)
+input = gr.inputs.Number(label='Number of Hours studied')
+output = gr.outputs.Textbox(label='Predicted Score')
+gr.Interface( fn=predict_score,
+              inputs=input,
+              outputs=output).launch();