Update app.py

app.py

@@ -1,39 +1,29 @@
-# app.py - FINAL, GUARANTEED VERSION
-# This script explicitly downloads the model files from the Hub before loading them.
-# This removes all guesswork about file paths.
+# app.py - FINAL PROFESSIONAL version with input validation and guardrails
 
 import gradio as gr
 import joblib
 import pandas as pd
 import os
-import subprocess # We need this to run system commands like 'wget'
+import subprocess
 
-# --- STEP 1: Download the Model Files from the Hub ---
-# This happens only ONCE when the Space boots up.
-
-# The direct download URLs for your files on the Hub
+# --- STEP 1: Download the Model Files from the Hub (same as before) ---
 MODEL_URL = "https://huggingface.co/szili2011/ai-house-price-predictor/resolve/main/housing_model.joblib"
 COLUMNS_URL = "https://huggingface.co/szili2011/ai-house-price-predictor/resolve/main/model_columns.joblib"
-
-# The names we will save the files as locally in our Space
 MODEL_LOCAL_PATH = "housing_model.joblib"
 COLUMNS_LOCAL_PATH = "model_columns.joblib"
 
-# Use the 'wget' command to download the files. This is a robust method.
-print("--- Downloading Model Files ---")
-subprocess.run(["wget", "-O", MODEL_LOCAL_PATH, MODEL_URL])
-subprocess.run(["wget", "-O", COLUMNS_LOCAL_PATH, COLUMNS_URL])
-print("--- Download Complete ---")
-
+if not os.path.exists(MODEL_LOCAL_PATH):
+    print("--- Downloading Model Files ---")
+    subprocess.run(["wget", "-O", MODEL_LOCAL_PATH, MODEL_URL])
+    subprocess.run(["wget", "-O", COLUMNS_LOCAL_PATH, COLUMNS_URL])
+    print("--- Download Complete ---")
 
-# --- STEP 2: Load the Now-Local Model and Columns ---
+# --- STEP 2: Load the Now-Local Model and Columns (same as before) ---
 try:
     print(f"Loading model from local path: '{MODEL_LOCAL_PATH}'")
     model = joblib.load(MODEL_LOCAL_PATH)
-    
     print(f"Loading columns from local path: '{COLUMNS_LOCAL_PATH}'")
     model_columns = joblib.load(COLUMNS_LOCAL_PATH)
-    
     print("✅ Model and columns loaded successfully.")
     model_loaded_successfully = True
 except Exception as e:
@@ -41,11 +31,26 @@ except Exception as e:
     model_loaded_successfully = False
 
 
-# --- Core prediction function ---
+# --- This is the core prediction function with the new validation logic ---
 def predict_price(sqft, bedrooms, house_age, condition, year_sold, interest_rate, region, sub_type, style, has_garage, has_pool):
     if not model_loaded_successfully:
-        raise gr.Error("Model failed to load. Please check the Space logs for errors.")
+        raise gr.Error("Model is not loaded. Please check the Space logs for errors.")
+
+    # --- NEW: Input Validation Guardrails ---
+    # Before we do anything, we check if the user's inputs are reasonable.
+    # If not, we stop and send back a helpful error message.
+    if not (300 <= sqft <= 30000):
+        raise gr.Error(f"Input Error: Square Footage must be between 300 and 30,000. You entered {sqft}.")
+    if not (1 <= bedrooms <= 20):
+        raise gr.Error(f"Input Error: Number of Bedrooms must be between 1 and 20. You entered {bedrooms}.")
+    if not (0 <= house_age <= 200):
+        raise gr.Error(f"Input Error: House Age must be between 0 and 200. You entered {house_age}.")
+    if not (2000 <= year_sold <= 2030):
+        raise gr.Error(f"Input Error: Year Sold must be between 2000 and 2030. You entered {year_sold}.")
+    if not (0 <= interest_rate <= 25):
+        raise gr.Error(f"Input Error: Interest Rate must be between 0 and 25. You entered {interest_rate}.")
 
+    # If all checks pass, we proceed with the prediction.
     input_data = {
         'SquareFootage': sqft, 'Bedrooms': bedrooms, 'HouseAge': house_age,
         'PropertyCondition': condition, 'HasGarage': has_garage, 'HasPool': has_pool,
@@ -61,16 +66,17 @@ def predict_price(sqft, bedrooms, house_age, condition, year_sold, interest_rate
     
     return f"${predicted_price:,.0f}"
 
-# --- Create the Gradio Interface ---
+# --- Create the Gradio Interface with better UI limits ---
 demo = gr.Interface(
     fn=predict_price,
     inputs=[
-        gr.Number(label="Square Footage", value=2500),
-        gr.Number(label="Bedrooms", value=4),
-        gr.Number(label="House Age (years)", value=15),
+        # --- NEW: Added minimum and maximum values to the UI components ---
+        gr.Number(label="Square Footage", value=2500, minimum=300, maximum=30000),
+        gr.Number(label="Bedrooms", value=4, minimum=1, maximum=20),
+        gr.Number(label="House Age (years)", value=15, minimum=0, maximum=200),
         gr.Slider(label="Property Condition", minimum=1, maximum=10, step=1, value=8),
-        gr.Number(label="Year Sold", value=2024),
-        gr.Number(label="Interest Rate (%)", value=5.5),
+        gr.Number(label="Year Sold", value=2024, minimum=2000, maximum=2030),
+        gr.Number(label="Interest Rate (%)", value=5.5, minimum=0, maximum=25),
         gr.Radio(['Sunbelt', 'Pacific Northwest', 'Rust Belt', 'New England', 'Mountain West'], label="Region", value="Sunbelt"),
         gr.Radio(['Urban', 'Suburban', 'Rural', 'Historic District'], label="Sub-Type", value="Suburban"),
         gr.Radio(['Modern', 'Ranch', 'Colonial', 'Craftsman', 'Victorian'], label="Architectural Style", value="Colonial"),