Update app.py

app.py

@@ -1,25 +1,18 @@
 import gradio as gr
-import pickle
 import pandas as pd
 import numpy as np
+import joblib
 from huggingface_hub import hf_hub_download
 
-# Download model from MODEL repository (not stored in Space)
-repo_id = "DevNumb/random-forest-model-reduced"  # Your MODEL repo
+# Download model from your repository
+repo_id = "DevNumb/random-forest-model-reduced"
+model_path = hf_hub_download(repo_id=repo_id, filename="model.joblib")
 
-print("Downloading model (1.64 GB)...")
-model_path = hf_hub_download(
-    repo_id=repo_id,
-    filename="random_forest_model_reduced.pkl",
-    local_dir="/tmp/model"  # Downloads to Space's 50 GB disk
-)
-
-print("Loading model...")
-with open(model_path, 'rb') as f:
-    model = pickle.load(f)
-print("✅ Model ready!")
+# Load model
+model = joblib.load(model_path)
+print("✅ Model loaded successfully!")
 
-# Feature names from your JSON
+# Your 18 feature names
 FEATURE_NAMES = [
     'OA_TEMP', 'OA_TEMP_WB', 'Hour', 'Weekday', 'Month',
     'CHL_STA_1', 'CHL_STA_2', 'CHL_STA_3',
@@ -30,26 +23,21 @@ FEATURE_NAMES = [
 ]
 
 def predict(*args):
+    """Make prediction from 18 features"""
     input_data = pd.DataFrame([list(args)], columns=FEATURE_NAMES)
     prediction = model.predict(input_data)[0]
-    
-    try:
-        probabilities = model.predict_proba(input_data)[0]
-        confidence = np.max(probabilities)
-        result = f"**Prediction:** {prediction:.2f}\n\n**Confidence:** {confidence:.2%}"
-    except:
-        result = f"**Prediction:** {prediction}"
-    
-    return result
+    return f"**Prediction:** {prediction:.2f}"
 
-# Create input fields for all 18 features
+# Create input fields
 inputs = [gr.Number(label=name) for name in FEATURE_NAMES]
 
+# Create interface
 demo = gr.Interface(
     fn=predict,
     inputs=inputs,
     outputs=gr.Markdown(label="Result"),
-    title="HVAC Chiller System Prediction API"
+    title="HVAC Chiller Prediction API",
+    description="Predicts chiller system performance"
 )
 
 demo.launch()