Update app.py

app.py

@@ -1,7 +1,143 @@
 from fastapi import FastAPI
+from fastapi import Depends, FastAPI, HTTPException, status
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+
+from pydantic import BaseModel
 
 app = FastAPI()
 
 @app.get("/")
 def greet_json():
     return {"Hello": "World!"}
+
+
+import numpy as np
+import pandas as pd
+
+from utils import compute_features
+
+import json
+
+import joblib
+
+
+# Define the same file path
+filename = 'finalized_linear_model.joblib'
+
+# Load the model from disk
+loaded_model = joblib.load(filename)
+
+
+REQUIRED_COLUMN_ORDER = [
+    'num_hotel', 'num_attraction', 'num_restaurant',
+    'num_convenience_store', 'num_pharmacy', 'num_cafe', 'num_bookstore',
+    'num_school', 'num_co_working', 'num_clinic', 'num_bank',
+    'num_supermarket', 'num_gym', 'num_fast_food', 'num_shopping_mall',
+    'num_bakery', 'num_university', 'num_hospital', 'num_dentist',
+    'num_clothing_store', 'num_department_store', 'num_college',
+    'num_electronics_store', 'num_hostel', 'num_charging_station',
+    'num_viewpoint', 'num_jewelry_store'
+]
+
+# @app.post('/predict')
+# def predict_score(lat, lon):
+    
+class Location(BaseModel):
+    lat: float
+    lon: float
+
+@app.post("/predict")
+async def predict_score(location: Location, request: Request):
+    lat, lon = location.lat, location.lon
+    auth_header = request.headers.get("Authorization")
+    
+    api_key = auth_header.split(" ")[1] if auth_header else None
+   
+    inputs = compute_features((lat,lon), api_key, 500)
+    print("[INPUTS]", inputs)
+    num_banks = inputs.pop("num_banks_in_radius", 0)
+
+    input_dict = inputs.copy()
+
+    input_dict_new = {}
+
+    for col in REQUIRED_COLUMN_ORDER:
+        input_dict_new[col] = input_dict[col]
+
+    mu_pred = loaded_model.predict([list(input_dict_new.values())])[0]
+
+    # 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_pred2 - num_banks
+    # score = 100 / (1 + np.exp(-alpha * diff))
+
+    # score = np.abs(1 + np.tanh(diff)) / 2 * 100
+
+
+    # score = (1 * np.abs(mu_pred2 + 0.1)) * 100
+
+    # score = np.sigmoid(mu_pred2 - num_banks + 0.1) * 100
+
+    score =  100 / (1 + np.exp(num_banks - mu_pred))
+
+    input_dict_new.update({
+        "score":round(float(score), 3),
+        "num_atm":num_banks,
+        "num_prediction_atm":round(float(mu_pred), 3),
+        "lat":lat,
+        "lon":lon
+    })
+
+    return input_dict_new
+
+    # return (
+    #     round(float(score), 3),
+    #     num_banks,
+    #     round(float(mu_pred), 3),
+    #     # round(float(log_score),3)
+    #     # "Normal Score": round(float(normal_score), 3),
+    #     # input_dict["total_amenities"],
+
+    #     *[v for k,v in input_dict_new.items() if k[:3] == "num"]
+
+    # )
+
+# # ======== Gradio Interface ========
+# interface = gr.Interface(
+#     fn=predict_score,
+#     inputs=[
+#         gr.Number(label="Latitude"),
+#         gr.Number(label="Longitude"),
+#         gr.Text(label="Google Api Key")
+#     ],
+#     outputs=[
+#         gr.Number(label="Score (0 - 100)"),
+#         gr.Number(label="Current ATMs"),
+#         gr.Number(label="Ideal ATMs"),
+#         # gr.Number(label="Log Score Probability"),
+
+#         # gr.Number(label="Total Amenities"),
+
+#         *[gr.Number(label=x) for x in REQUIRED_COLUMN_ORDER]
+#     ],
+#     title="Bank Location Scoring Model",
+#     description="Enter latitude and longitude to get the predicted score, number of banks, and normalized score.",
+# )
+
+
+# interface.launch()