Update app.py

app.py

@@ -11,32 +11,32 @@ from scipy.stats import nbinom
 from xgboost import XGBRegressor
 import json
 
-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
+# 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(12)
-model.load_state_dict(torch.load("model_weights(1).pt", map_location='cpu'))
-model.eval()
+# model = NegBinomialModel(12)
+# model.load_state_dict(torch.load("model_weights(1).pt", map_location='cpu'))
+# model.eval()
 
-MU_BANKS = 2.6035915713614286
-STD_BANKS = 3.0158890435512125
+# MU_BANKS = 2.6035915713614286
+# STD_BANKS = 3.0158890435512125
 
 
 # with open("xgb_model(1).json", "r") as f:
 #     params = json.load(f)
 
 xgb_model = XGBRegressor()
-xgb_model.load_model("xgb_model(1).json")
+xgb_model.load_model("xgb_model(2).json")
 
 def predict_score(lat, lon, api_key):
     # Convert input to tensor
@@ -49,12 +49,10 @@ def predict_score(lat, lon, api_key):
 
     inputs = torch.tensor(list(inputs.values()), dtype=torch.float32)
 
-    # Get model output
-    with torch.no_grad():
-        mu_pred, alpha = model(inputs)
+    # with torch.no_grad():
+    #     mu_pred, alpha = model(inputs)
 
-    # Unpack into respective values
-    mu_pred = mu_pred.numpy().flatten()
+    # mu_pred = mu_pred.numpy().flatten()
 
     mu_pred2 = xgb_model.predict(inputs.unsqueeze(0).numpy())
 
@@ -91,11 +89,11 @@ def predict_score(lat, lon, api_key):
     return (
         round(float(score), 3),
         num_banks,
-        round(float(mu_pred), 3),
+        # round(float(mu_pred), 3),
         round(float(mu_pred2), 3),
         # round(float(log_score),3)
         # "Normal Score": round(float(normal_score), 3),
-        input_dict["total_amenities"],
+        # input_dict["total_amenities"],
 
         *[v for k,v in input_dict.items() if k[:3] == "num"]
 
@@ -112,11 +110,11 @@ interface = gr.Interface(
     outputs=[
         gr.Number(label="Score (0 - 100)"),
         gr.Number(label="Current ATMs"),
-        gr.Number(label="Ideal ATMs (Negative Binomial)"),
+        # gr.Number(label="Ideal ATMs (Negative Binomial)"),
         gr.Number(label="Ideal ATMs (XGBoost)"),
         # gr.Number(label="Log Score Probability"),
 
-        gr.Number(label="Total Amenities"),
+        # gr.Number(label="Total Amenities"),
 
         gr.Number(label="Dining and Drinking"),
         gr.Number(label="Community and Government"),
@@ -133,120 +131,3 @@ interface = gr.Interface(
     description="Enter latitude and longitude to get the predicted score, number of banks, and normalized score.",
 )
 
-
-interface.launch()
-
-
-# import gradio as gr
-# import torch
-# from torch import nn
-
-# import numpy as np
-# import pandas as pd
-
-# from utils import compute_features
-# from scipy.stats import nbinom
-
-# from xgboost import XGBRegressor
-# import json
-
-# 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(12)
-# model.load_state_dict(torch.load("model_weights(1).pt", map_location='cpu'))
-# model.eval()
-
-# # MU_BANKS = 2.6035915713614286
-# # STD_BANKS = 3.0158890435512125
-
-
-# # with open("xgb_model(1).json", "r") as f:
-# #     params = json.load(f)
-
-# xgb_model = XGBRegressor()
-# xgb_model.load_model("xgb_model(1).json")
-
-# 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(list(inputs.values()), dtype=torch.float32)
-
-#     # Get model output
-#     with torch.no_grad():
-#         mu_pred, alpha = model(inputs)
-
-#     # Unpack into respective values
-#     mu_pred = mu_pred.numpy().flatten()
-
-#     mu_pred2 = xgb_model.predict(inputs.unsqueeze(0).numpy())
-
-#     # 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(mu_pred2), 3),
-#         # round(float(log_score),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 (0 - 100)"),
-#         gr.Number(label="Number of Current Banks"),
-#         gr.Number(label="Number of Ideal Banks (Negative Binomial)"),
-#         gr.Number(label="Number of Ideal Banks (XGBoost)"),
-#         # 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.",
-# )
-
-
-# interface.launch()