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import gradio as gr
import torch
import torch.nn as nn
import time
import math
class Lreg(nn.Module):
def __init__(self):
super(Lreg,self).__init__()
self.l = nn.Linear(1,1)
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(1,100)):
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()
return [rout.item(),c['l.weight'].item(),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()
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