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import gradio as gr
import torch
from torch import nn
import numpy as np
import pandas as pd
from utils import compute_features
class NegBinomialModel(nn.Module):
def __init__(self, in_features):
super().__init__()
self.linear = nn.Linear(in_features, 1)
self.alpha = nn.Parameter(torch.tensor(0.5))
def forward(self, x):
# safer activation than exp()
mu = torch.exp(torch.clamp(self.linear(x), min=-5, max=5))
alpha = torch.clamp(self.alpha, min=1e-3, max=10)
return mu.squeeze(), alpha
model = NegBinomialModel(16)
model.load_state_dict(torch.load("model_weights.pt", map_location='cpu'))
model.eval()
def predict_score(lat, lon):
# Convert input to tensor
# inputs = torch.tensor([[lat, lon]], dtype=torch.float32)
inputs = compute_features((lat,lon))
num_banks = inputs.get("num_banks_in_radius", 0)
inputs = torch.tensor([lat,lon] + list(inputs.values()))
# Get model output
with torch.no_grad():
outputs = model(inputs).numpy().flatten()
# Unpack into respective values
mu_pred, alpha = outputs
score = (1 * np.abs(mu_pred + 0.1)) * 100
# You can apply any post-processing here
return {
"Score": round(float(score), 3),
"Number of current ATMs": round(float(mu_pred), 3),
"Number of ideal ATMs" : num_banks_in_radius
# "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 Current Banks"),
gr.Number(label="Num Ideal 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()
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