Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# Toy dataset
+classes = ['tech', 'health']
+keywords = {
+    'tech': ['neural', 'AI', 'system', 'inference', 'hyperparameter', 'network'],
+    'health': ['wellness', 'asbestos', 'hazmat', 'patient', 'health', 'therapy']
+}
+
+# Define a simple neural network
+class SimpleNet(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(SimpleNet, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        return self.fc2(x)
+
+# Tokenizer and encoder (basic word count)
+def encode(text):
+    vector = np.zeros(len(set(sum(keywords.values(), []))))
+    vocab = list(set(sum(keywords.values(), [])))
+    for word in text.lower().split():
+        if word in vocab:
+            vector[vocab.index(word)] += 1
+    return torch.tensor(vector, dtype=torch.float32)
+
+# Initialize model
+model = SimpleNet(input_size=12, hidden_size=6, output_size=2)
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+
+def zero_shot_infer(text):
+    x = encode(text)
+    with torch.no_grad():
+        output = model(x)
+        predicted = torch.argmax(output).item()
+    return f"Predicted Category: {classes[predicted]}"
+
+def train_step(text, label, lr, momentum):
+    optimizer.param_groups[0]['lr'] = lr
+    for g in optimizer.param_groups:
+        g['momentum'] = momentum
+    x = encode(text)
+    y = torch.tensor([classes.index(label)], dtype=torch.long)
+    output = model(x)
+    loss = F.cross_entropy(output.unsqueeze(0), y)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    return f"Loss after training step: {loss.item():.4f}"
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# LARS Playground: Simulating 1989 AI Innovation")
+
+    with gr.Row():
+        input_text = gr.Textbox(label="Input Text")
+        infer_btn = gr.Button("Zero-Shot Inference")
+        infer_out = gr.Textbox(label="Inference Result")
+
+    infer_btn.click(fn=zero_shot_infer, inputs=input_text, outputs=infer_out)
+
+    gr.Markdown("### Train the Model Manually")
+
+    with gr.Row():
+        train_text = gr.Textbox(label="Training Text")
+        train_label = gr.Radio(choices=classes, label="Label")
+        lr = gr.Slider(0.001, 0.1, value=0.01, label="Learning Rate")
+        momentum = gr.Slider(0.0, 0.9, value=0.0, label="Momentum")
+        train_btn = gr.Button("Run Training Step")
+        train_out = gr.Textbox(label="Training Output")
+
+    train_btn.click(fn=train_step, inputs=[train_text, train_label, lr, momentum], outputs=train_out)
+
+demo.launch()