app.py

app.py

@@ -1,6 +1,4 @@
-import numpy as np
 import torch
-import gradio as gr
 
 def predict_score(x1, x2):
     Theta0 = torch.tensor(-0.5738734424645411)
@@ -9,72 +7,77 @@ def predict_score(x1, x2):
     y_actual = Theta0 + Theta1 * x1 + Theta2 * 23  # Adjust the constant value here if needed
     return y_actual.item()
 
-input1 = gr.inputs.Number(label='Number of New Students')
-input2 = gr.inputs.Number(label='Number of Temperature')
-
-output = gr.outputs.Textbox(label='Predicted Rooms')
-
-gr.Interface(fn=predict_score, inputs=[input1, input2], outputs=output).launch()
-
-def  pred(y_actual,y_pred, x1, x2):
-# 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))
+def gradient_descent():
+    # 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))
+
+# Launch the prediction interface
+while True:
+    x1 = float(input("Enter the number of new students: "))
+    x2 = float(input("Enter the number of temperature: "))
+    predicted_rooms = predict_score(x1, x2)
+    print("Predicted rooms:", predicted_rooms)
+    print()
+
+    choice = input("Do you want to predict again? (y/n): ")
+    if choice.lower() != 'y':
         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
-
-    gr.Interface(fn=predict_score, inputs=[input1, input2], outputs=output).launch()
-
-# 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))
+gradient_descent()