Create app.py

app.py

@@ -0,0 +1,95 @@
+import torch
+import torch.nn as nn
+import gradio as gr
+
+# ---------------------------------------------------------
+# Industry-style MLP for Customer Churn Risk
+# ---------------------------------------------------------
+class ChurnRiskMLP(nn.Module):
+    def __init__(self, input_size=20, hidden_size=64, output_size=1, dropout_p=0.5):
+        super().__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(p=dropout_p)
+        self.fc2 = nn.Linear(hidden_size, output_size)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        x1 = self.fc1(x)
+        x2 = self.relu(x1)
+        x3 = self.dropout(x2)
+        x4 = self.fc2(x3)
+        out = self.sigmoid(x4)
+        return x1, x2, x3, out
+
+
+# ---------------------------------------------------------
+# Inference function for Gradio
+# ---------------------------------------------------------
+def run_inference(batch_size, hidden_size, dropout_p, mode):
+    # Create dummy customer data
+    dummy = torch.randn(batch_size, 20)
+
+    # Build model
+    model = ChurnRiskMLP(
+        input_size=20,
+        hidden_size=hidden_size,
+        output_size=1,
+        dropout_p=dropout_p
+    )
+
+    # Set mode
+    if mode == "train (dropout ON)":
+        model.train()
+    else:
+        model.eval()
+
+    # Forward pass
+    with torch.no_grad():
+        fc1, relu, drop, out = model(dummy)
+
+    # Convert tensors to readable text
+    result = (
+        f"=== Dummy Customer Input (batch={batch_size}) ===\n{dummy}\n\n"
+        f"=== After fc1 ===\n{fc1}\n\n"
+        f"=== After ReLU ===\n{relu}\n\n"
+        f"=== After Dropout ({mode}) ===\n{drop}\n\n"
+        f"=== Final Churn Risk Predictions ===\n{out}\n"
+    )
+
+    return result
+
+
+# ---------------------------------------------------------
+# Gradio Interface
+# ---------------------------------------------------------
+with gr.Blocks(title="Customer Churn Risk Explorer") as demo:
+    gr.Markdown(
+        """
+        # 📊 Customer Churn Risk Explorer  
+        Investigate how a real industry-style MLP behaves with **dropout**,  
+        adjustable **hidden size**, and **batch size**.
+
+        This tool helps you visualize:
+        - Layer activations  
+        - Dropout effects  
+        - Final churn-risk predictions  
+        """
+    )
+
+    batch = gr.Slider(1, 32, value=8, step=1, label="Batch Size (# of customers)")
+    hidden = gr.Slider(8, 256, value=64, step=8, label="Hidden Layer Size")
+    dropout = gr.Slider(0.0, 0.9, value=0.5, step=0.1, label="Dropout Probability")
+    mode = gr.Radio(["train (dropout ON)", "eval (dropout OFF)"], value="train (dropout ON)", label="Mode")
+
+    output_box = gr.Textbox(label="Model Output", lines=25)
+
+    run_button = gr.Button("Run Model")
+
+    run_button.click(
+        fn=run_inference,
+        inputs=[batch, hidden, dropout, mode],
+        outputs=output_box
+    )
+
+demo.launch()