added training Scenerio

app.py

@@ -1,8 +1,55 @@
 import gradio as gr
+import torch
+import torch.nn as nn
+import time
+import math
 
-def greet(name):
-    return "Hello " + name + "!!"
+class Lreg(nn.Module):
+    def __init__(self):
+        super(Lreg,self).__init__()
+        self.l = nn.Linear(1,1)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+    def forward(self,x):
+        y = self.l(x)
+        return y
 
+model = Lreg()
+crit = nn.MSELoss()
+optim = torch.optim.SGD(model.parameters(),lr=0.01)
+
+def weightsbias(weight,bias,predicted,progress=gr.Progress()):
+    weight = float(weight)
+    bias = float(bias)
+    predicted = float(predicted)
+    x = torch.randn(1000)
+    y = (x*weight)+bias
+    progress(0,desc="Starting")
+    model.train()
+    for i in progress.tqdm(range(1,10)):
+        for idx,ele in enumerate(x):
+            ele = ele.unsqueeze(0)
+            out = model(ele)
+            optim.zero_grad()
+            loss = crit(out,y[idx].unsqueeze(0))
+            loss.backward()
+            optim.step()
+    model.eval()
+    t = torch.tensor([predicted],dtype=torch.float32)
+    rout = model(t)
+    c = model.state_dict()
+    def ceil(x):
+        return math.ceil(x)
+    return [ceil(rout.item()),ceil(c['l.weight'].item()),ceil(c['l.bias'].item())]
+
+
+with gr.Blocks() as iface:
+    weights = gr.Textbox(label="Weights")
+    bias = gr.Textbox(label="Bias")
+    predicted = gr.Textbox(label="Prediction Number")
+    output = gr.Number(label="Output Predicted")
+    w = gr.Number(label="Calculated Weight ")
+    b = gr.Number(label="Calculated Bias ")
+    train = gr.Button("Train")
+    train.click(fn=weightsbias, inputs=[weights,bias,predicted], outputs=[output,w,b])
+
+iface.queue(concurrency_count=10).launch()

flagged/log.csv

@@ -0,0 +1,2 @@
+name,output,flag,username,timestamp
+HEllo,Hello HEllo!!,,,2023-03-31 10:59:14.701814