created gradio app

app.py

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+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import time
+from tqdm.notebook import tqdm_notebook
+import plotly.express as px
+import plotly.graph_objects as go
+import gradio as gr
+
+
+
+def get_error(data, a, b):
+    n = data.size()[0]
+    y_pred = a * data[:,0] + b
+    #return (1 / (2 * n)) * ((y_pred - data[:,-1]) ** 2).sum().item()
+    return (1 / 2) * torch.mean((y_pred - data[:,-1]) ** 2).item()
+
+def get_grads(data, a, b, alpha):
+    n = data.size()[0]
+    y_pred = a * data[:,0] + b
+    #grad_a = (alpha / n) * ((y_pred - data[:,-1])* data[:,0]).sum().item()
+    grad_a = alpha * torch.mean((y_pred - data[:,-1])* data[:,0]).item()
+    #grad_b = (alpha / n) * (y_pred - data[:,-1]).sum().item()
+    grad_b = alpha * torch.mean((y_pred - data[:,-1])* data[:,0]).item()
+    return (grad_a, grad_b)
+
+def train(data, alpha=0.0001, epochs=500, test_data=[1,2,3]):
+    tensors = torch.from_numpy(data.astype(np.float64))
+    print(f"dataset:{tensors}, size:{tensors.size()}")
+    a = 1
+    b = 1
+    errors = []
+    updated_a = []
+    updated_b = []
+    for i in tqdm_notebook(range(int(epochs)), desc=f"Epoch: "):
+        errors.append(get_error(tensors, a, b))
+        grad_a_temp, grad_b_temp = get_grads(tensors, a, b, alpha)
+        updated_a.append(a)
+        updated_b.append(b)
+        a -= grad_a_temp
+        b -= grad_b_temp
+        
+        #print(f"Epoch: {i} -> Theta0: {updated_b[i]} Theta1: {updated_a[i]}, Error: {errors[i]} \n")
+    y_pred = a * tensors[:,0] + b 
+    fig1 = px.scatter(x=tensors[:, 0], y=tensors[:, -1])
+    fig2 = px.line(x=tensors[:, 0], y=y_pred)
+    fig3 = go.Figure(data=fig1.data + fig2.data)
+    fig3.update_layout(
+        #title="Best Fit Line",
+        xaxis_title="X",
+        yaxis_title="Y",
+    )
+
+    fig4 = px.line(errors)
+    fig4.update_traces(line_color='orange')    
+    fig4.update_layout(
+        #title="Learning Curve",
+        xaxis_title="Epoch",
+        yaxis_title="Error",
+        showlegend=False
+    )
+    y_pred_test = a * test_data.astype(np.float64) + b 
+    return f"Best Model: {round(updated_a[-1],3)} * X + {round(updated_b[-1],3)}",fig3, fig4, np.concatenate((test_data.astype(np.float64), y_pred_test), axis=1)
+
+# gradio app
+
+input_elements = [
+    gr.Numpy(
+            value=[[40,9],[30,8.5],[25,8],[20,7],[10,6],[5,8]],
+            datatype="number",
+            row_count=(6,"dynamic"),
+            col_count=(2, "fixed"),
+            label="Dataset",
+            interactive=True
+        ),
+    gr.Number(label="Learning Rate", value=0.0001, interactive=True),
+    gr.Number(label="Number of epochs", value=500, interactive=True),
+    gr.Numpy(
+        value=[[10],[20],[30]],
+        datatype="number",
+        row_count=3,
+        col_count=(1,"fixed"),
+        label="Test Dataset",
+        interactive=True
+    )
+]
+
+output_elements = [
+    gr.Textbox(label="Generated Model", placeholder="Model Equation: a * X + b"), 
+    gr.Plot(label="Best Fit Line"), 
+    gr.Plot(label="Learning Curve"),
+    gr.Numpy(label="Model Predictions")
+    
+]
+
+app = gr.Interface(
+    title="Linear Regression using Pytorch",
+    #description="a simple app to demonstrate linear regression",
+    fn=train,
+    inputs=input_elements,
+    outputs=output_elements,
+    allow_flagging="never"
+)
+app.launch(debug=True, share=True)

requirements.txt

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+numpy
+torch
+matplotlib
+plotly
+gradio
+seaborn
+tqdm