Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py - FINAL PROFESSIONAL version with input validation and guardrails
+# app.py - FINAL POLISHED version with forgiving input clamping
 
 import gradio as gr
 import joblib
@@ -6,7 +6,7 @@ import pandas as pd
 import os
 import subprocess
 
-# --- STEP 1: Download the Model Files from the Hub (same as before) ---
+# --- STEP 1: Download Model Files ---
 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"
 MODEL_LOCAL_PATH = "housing_model.joblib"
@@ -18,11 +18,9 @@ if not os.path.exists(MODEL_LOCAL_PATH):
     subprocess.run(["wget", "-O", COLUMNS_LOCAL_PATH, COLUMNS_URL])
     print("--- Download Complete ---")
 
-# --- STEP 2: Load the Now-Local Model and Columns (same as before) ---
+# --- STEP 2: Load Model and Columns ---
 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
@@ -31,31 +29,44 @@ except Exception as e:
     model_loaded_successfully = False
 
 
-# --- This is the core prediction function with the new validation logic ---
+# --- Core prediction function with the new, forgiving clamping 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 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}.")
+    # --- NEW: Forgiving Input Clamping ---
+    # Instead of raising an error, we gently guide extreme inputs back into a reasonable range.
+    # The min() function takes the smaller of two numbers, the max() function takes the larger.
+    # This "clamps" the value between a minimum and a maximum.
+    
+    # Clamp square footage between 300 and 30,000
+    sqft_clamped = max(300, min(sqft, 30000))
+    
+    # Clamp bedrooms between 1 and 20
+    bedrooms_clamped = max(1, min(bedrooms, 20))
+    
+    # Clamp house age between 0 and 200
+    house_age_clamped = max(0, min(house_age, 200))
+
+    # Clamp year sold between 2000 and 2030
+    year_sold_clamped = max(2000, min(year_sold, 2030))
+
+    # Clamp interest rate between 0 and 25
+    interest_rate_clamped = max(0.0, min(interest_rate, 25.0))
 
-    # If all checks pass, we proceed with the prediction.
+    # We now use these "safe" clamped values for the prediction.
     input_data = {
-        'SquareFootage': sqft, 'Bedrooms': bedrooms, 'HouseAge': house_age,
-        'PropertyCondition': condition, 'HasGarage': has_garage, 'HasPool': has_pool,
-        'YearSold': year_sold, 'InterestRate': interest_rate,
-        'Region': region, 'SubType': sub_type, 'ArchitecturalStyle': style
+        'SquareFootage': sqft_clamped,
+        'Bedrooms': bedrooms_clamped,
+        'HouseAge': house_age_clamped,
+        'PropertyCondition': condition,
+        'HasGarage': has_garage,
+        'HasPool': has_pool,
+        'YearSold': year_sold_clamped,
+        'InterestRate': interest_rate_clamped,
+        'Region': region,
+        'SubType': sub_type,
+        'ArchitecturalStyle': style
     }
     input_df = pd.DataFrame([input_data])
     
@@ -66,11 +77,11 @@ def predict_price(sqft, bedrooms, house_age, condition, year_sold, interest_rate
     
     return f"${predicted_price:,.0f}"
 
-# --- Create the Gradio Interface with better UI limits ---
+# --- Gradio Interface with UI limits ---
+# The UI limits still provide a good first line of guidance for the user.
 demo = gr.Interface(
     fn=predict_price,
     inputs=[
-        # --- 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),
@@ -85,7 +96,7 @@ demo = gr.Interface(
     ],
     outputs=gr.Textbox(label="Predicted Price"),
     title="AI House Price Predictor",
-    description="Describe a property, and our AI will estimate its market value. This AI was trained on a 9.2GB simulated dataset and is hosted on Hugging Face."
+    description="Describe a property, and our AI will estimate its market value. The model is robust and will provide estimates even for extreme values by capping them to its known limits."
 )
 
 # Launch the app