Update app.py

app.py

@@ -6,6 +6,7 @@ import numpy as np
 import pandas as pd
 
 from utils import compute_features
+from scipy.stats import nbinom
 
 
 class NegBinomialModel(nn.Module):
@@ -25,10 +26,15 @@ model = NegBinomialModel(16)
 model.load_state_dict(torch.load("model_weights.pt", map_location='cpu'))
 model.eval()
 
+# MU_BANKS = 2.6035915713614286
+# STD_BANKS = 3.0158890435512125
+
+
 def predict_score(lat, lon):
     # Convert input to tensor
     # inputs = torch.tensor([[lat, lon]], dtype=torch.float32)
     inputs = compute_features((lat,lon))
+    print("[INPUTS]", inputs)
     num_banks = inputs.pop("num_banks_in_radius", 0)
 
     inputs = torch.tensor([lat,lon] + list(inputs.values()), dtype=torch.float32)
@@ -40,13 +46,37 @@ def predict_score(lat, lon):
     # Unpack into respective values
     mu_pred = mu_pred.numpy().flatten()
 
-    score = (1 * np.abs(mu_pred + 0.1)) * 100
+    # r = 1/alpha
+    # p = r / (r + mu_pred)
+
+    # # Compute pmf and mode
+    # k_mode = int((r - 1) * (1 - p) / p)  # mode of NB
+    # p_k = nbinom.pmf(num_banks, r, p)
+    # p_mode = nbinom.pmf(k_mode, r, p)
+
+    # # Score normalized 0–100
+    # score = (p_k / p_mode) * 100
+    # score = np.clip(score, 0, 100)
+
+    # diff = (num_banks - mu_pred) / (mu_pred + 1e-6)
+    # # score = (1 - np.tanh(diff))
+
+    # print("[TANH]", np.tanh(diff))
+
+    diff = mu_pred - num_banks
+    score = 100 / (1 + np.exp(-alpha * diff))
+
+    score = np.abs(1 + np.tanh(diff)) / 2 * 100
+
+
+    # score = (1 * np.abs(mu_pred + 0.1)) * 100
 
     # You can apply any post-processing here
     return (
         round(float(score), 3),
         num_banks,
         round(float(mu_pred), 3),
+        # round(float(log_score),3)
         # "Normal Score": round(float(normal_score), 3),
     )
 
@@ -60,8 +90,8 @@ interface = gr.Interface(
     outputs=[
         gr.Number(label="Score"),
         gr.Number(label="Num Current Banks"),
-        gr.Number(label="Num Ideal Banks")
-        # gr.Number(label="Normal Score"),
+        gr.Number(label="Num Ideal Banks"),
+        # gr.Number(label="Log Score Probability"),
     ],
     title="Bank Location Scoring Model",
     description="Enter latitude and longitude to get the predicted score, number of banks, and normalized score.",