app.py

import gradio as gr
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt

print("🚀 The script has started running...")  # Debugging print

# Define a simple neural network
class SimpleNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(SimpleNN, self).__init__()
        self.hidden = nn.Linear(input_size, hidden_size)
        self.activation = nn.Tanh()
        self.output = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = self.hidden(x)
        x = self.activation(x)
        x = self.output(x)
        return x

# Generate sample data
def generate_data(noise=0):
    x = np.linspace(-5, 5, 100)
    y = np.sin(x) + noise * np.random.randn(100)
    return x, y

# Train the model
def train_model(epochs, learning_rate):
    print(f"🔄 Training started with {epochs} epochs and learning rate {learning_rate}...")

    model = SimpleNN(1, 4, 1)
    optimizer = optim.SGD(model.parameters(), lr=learning_rate)
    criterion = nn.MSELoss()

    x_train, y_train = generate_data()
    x_train = torch.tensor(x_train, dtype=torch.float32).unsqueeze(1)
    y_train = torch.tensor(y_train, dtype=torch.float32).unsqueeze(1)

    losses = []
    for epoch in range(epochs):
        optimizer.zero_grad()
        outputs = model(x_train)
        loss = criterion(outputs, y_train)
        loss.backward()
        optimizer.step()
        losses.append(loss.item())

    print("✅ Training complete. Plotting loss...")

    # Plot loss graph
    plt.figure(figsize=(5, 3))
    plt.plot(losses, label="Training Loss", color="blue")
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    plt.title("Neural Network Training Loss")
    plt.legend()
    plt.grid(True)

    loss_plot_path = "loss_plot.png"
    plt.savefig(loss_plot_path)
    plt.close()

    return loss_plot_path

# Gradio interface
iface = gr.Interface(
    fn=train_model,
    inputs=[
        gr.Slider(1, 100, value=10, label="Epochs"),
        gr.Slider(0.001, 0.1, value=0.03, label="Learning Rate"),
    ],
    outputs="image",
    title="Neural Network Trainer",
    description="Train a simple neural network and visualize the loss curve.",
)

print("✅ Gradio is about to launch...")
iface.launch()