Update app.py

app.py

@@ -1,9 +1,18 @@
 # -*- coding: utf-8 -*-
 
 import torch
-import math
+import math, sys
+from flask import *
 
 
+app = Flask(__name__)
+app.config['data'] = ""
+
+def print(data):
+    app.config['data'] += data + "<br>"
+    sys.stdout.write(data + "\n")
+    sys.stdout.flush()
+
 dtype = torch.float
 device = torch.device("cpu")
 # device = torch.device("cuda:0") # Uncomment this to run on GPU
@@ -17,7 +26,7 @@ a = torch.randn((), device=device, dtype=dtype)
 b = torch.randn((), device=device, dtype=dtype)
 c = torch.randn((), device=device, dtype=dtype)
 d = torch.randn((), device=device, dtype=dtype)
-print(a, b, c, d)
+print(f"{a} {b} {c} {d}")
 learning_rate = 1e-6
 for t in range(2000):
     # Forward pass: compute predicted y
@@ -26,7 +35,7 @@ for t in range(2000):
     # Compute and print loss
     loss = (y_pred - y).pow(2).sum().item()
     if t % 100 == 99:
-        print(t, loss)
+        print(str(t) + " " + str(loss))
 
     # Backprop to compute gradients of a, b, c, d with respect to loss
     grad_y_pred = 2.0 * (y_pred - y)
@@ -42,4 +51,10 @@ for t in range(2000):
     d -= learning_rate * grad_d
 
 
-print(f'Result: y = {a.item()} + {b.item()} x + {c.item()} x^2 + {d.item()} x^3')
+print(f'Result: y = {a.item()} + {b.item()} x + {c.item()} x^2 + {d.item()} x^3')
+
+@app.route('/')
+def index():
+    return app.config["data"]
+
+app.run(host= "0.0.0.0", port=7860)