Update app.py

app.py

@@ -1,16 +1,26 @@
 import gradio as gr
-import h2o
 import numpy as np
 import pandas as pd
 import cv2
+import pickle
 from skimage.color import rgb2hsv
 from skimage.measure import shannon_entropy
 from scipy.ndimage import generic_filter
 
-# Initialize H2O and load the saved model
-h2o.init()
-model_path = "GBM_grid_1_AutoML_2_20241228_54907_model_10"  # Replace with your H2O model path
-h2o_model = h2o.load_model(model_path)
+# Load the pre-trained SVR model, MinMaxScaler, and LabelEncoder from pickle files
+model_path = "svr_model.pkl"  # Replace with the path to your pickle file
+scaler_path = "minmax_scaler.pkl"  # Replace with the path to your MinMaxScaler pickle file
+encoder_path = "label_encoder.pkl"  # Replace with the path to your LabelEncoder pickle file
+
+# Load the pickle files
+with open(model_path, 'rb') as f:
+    svr_model = pickle.load(f)
+
+with open(scaler_path, 'rb') as f:
+    scaler = pickle.load(f)
+
+with open(encoder_path, 'rb') as f:
+    label_encoder = pickle.load(f)
 
 # Feature extraction function
 def extract_features(image):
@@ -76,16 +86,25 @@ def extract_features(image):
 def predict(image, gender, age):
     # Extract image features
     features = extract_features(image)
-    features["gender"] = gender
+
+    # Encode gender using LabelEncoder
+    gender_encoded = label_encoder.transform([gender])[0]  # Transform the gender to the correct encoded value
+
+    # Add gender and age to the feature dictionary
+    features["gender"] = gender_encoded
     features["age"] = age
 
     # Convert features to DataFrame
     features_df = pd.DataFrame([features])
-    features_h2o = h2o.H2OFrame(features_df)
 
-    # Predict using the model
-    prediction = h2o_model.predict(features_h2o)
-    return prediction.as_data_frame().iloc[0, 0]
+    # Scale the features using MinMaxScaler
+    features_scaled = scaler.transform(features_df)
+
+    # Predict using the SVR model
+    prediction = svr_model.predict(features_scaled)
+
+    # Return the prediction (you can format this depending on the model output)
+    return prediction[0]
 
 # Gradio Interface
 interface = gr.Interface(
@@ -95,9 +114,9 @@ interface = gr.Interface(
         gr.Dropdown(choices=["Male", "Female"], label="Gender"),
         gr.Slider(0, 100, step=1, label="Age"),
     ],
-    outputs="label",
+    outputs="number",
     title="Image-based Prediction App",
-    description="Upload an image, enter your gender and age, and get predictions using the pre-trained model."
+    description="Upload an image, enter your gender and age, and get predictions using the pre-trained SVR model."
 )
 
 # Launch the app