Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+
+# ======== Example Dummy Model ========
+# Replace this with your own model loading
+class DummyModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = torch.nn.Linear(2, 3)  # 2 inputs (lat, lon) → 3 outputs
+
+    def forward(self, x):
+        return self.linear(x)
+
+# load your trained model (replace with torch.load if you have .pt file)
+model = DummyModel()
+model.eval()
+
+# ======== Prediction Function ========
+def predict_score(lat, lon):
+    # Convert input to tensor
+    inputs = torch.tensor([[lat, lon]], dtype=torch.float32)
+
+    # Get model output
+    with torch.no_grad():
+        outputs = model(inputs).numpy().flatten()
+
+    # Unpack into respective values
+    score, num_banks, normal_score = outputs
+
+    # You can apply any post-processing here
+    return {
+        "Score": round(float(score), 3),
+        "Num Banks": round(float(num_banks), 3),
+        "Normal Score": round(float(normal_score), 3),
+    }
+
+# ======== Gradio Interface ========
+interface = gr.Interface(
+    fn=predict_score,
+    inputs=[
+        gr.Number(label="Latitude"),
+        gr.Number(label="Longitude"),
+    ],
+    outputs=[
+        gr.Number(label="Score"),
+        gr.Number(label="Num Banks"),
+        gr.Number(label="Normal Score"),
+    ],
+    title="Bank Location Scoring Model",
+    description="Enter latitude and longitude to get the predicted score, number of banks, and normalized score.",
+)
+
+
+interface.launch()