Update app.py

app.py

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+import gradio as gr
+import h2o
+import numpy as np
+import pandas as pd
+import cv2
+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 = ""  # Replace with your H2O model path
+h2o_model = h2o.load_model(model_path)
+
+# Feature extraction function
+def extract_features(image):
+    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Extract RGB means
+    meanr = np.mean(image[:, :, 0])
+    meang = np.mean(image[:, :, 1])
+    meanb = np.mean(image[:, :, 2])
+
+    # Convert to HSI and compute HHR
+    hsv_image = rgb2hsv(image)
+    hue = hsv_image[:, :, 0]
+    high_hue_pixels = np.sum(hue > 0.95)
+    total_pixels = hue.size
+    HHR = high_hue_pixels / total_pixels
+
+    # Convert to Grayscale
+    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+
+    # Compute Entropy and Brightness
+    Ent = shannon_entropy(gray_image)
+    B = np.mean(gray_image)
+
+    # Sliding window filters
+    def g1_filter(window):
+        return window[4] - np.min(window)
+
+    def g2_filter(window):
+        return np.max(window) - window[4]
+
+    def g3_filter(window):
+        return window[4] - np.mean(window)
+
+    def g4_filter(window):
+        return np.std(window)
+
+    def g5_filter(window):
+        return window[4]
+
+    g1 = generic_filter(gray_image, g1_filter, size=3).mean()
+    g2 = generic_filter(gray_image, g2_filter, size=3).mean()
+    g3 = generic_filter(gray_image, g3_filter, size=3).mean()
+    g4 = generic_filter(gray_image, g4_filter, size=3).mean()
+    g5 = generic_filter(gray_image, g5_filter, size=3).mean()
+
+    return {
+        "meanr": meanr,
+        "meang": meang,
+        "meanb": meanb,
+        "HHR": HHR,
+        "Ent": Ent,
+        "B": B,
+        "g1": g1,
+        "g2": g2,
+        "g3": g3,
+        "g4": g4,
+        "g5": g5,
+    }
+
+# Prediction function
+def predict(image, gender, age):
+    # Extract image features
+    features = extract_features(image)
+    features["gender"] = gender
+    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]
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(label="Upload Image"),
+        gr.Dropdown(choices=["Male", "Female"], label="Gender"),
+        gr.Slider(0, 100, step=1, label="Age"),
+    ],
+    outputs="label",
+    title="Image-based Prediction App",
+    description="Upload an image, enter your gender and age, and get predictions using the pre-trained model."
+)
+
+# Launch the app
+interface.launch()