Update app.py

app.py

@@ -1,26 +1,15 @@
 import gradio as gr
+import joblib
 import numpy as np
 import pandas as pd
 import cv2
-import joblib
-from skimage.color import rgb2hsv
-from skimage.measure import shannon_entropy
-from scipy.ndimage import generic_filter
-
-# 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 = joblib.load(f)
+from sklearn.preprocessing import MinMaxScaler, LabelEncoder
+from PIL import Image
 
-with open(scaler_path, 'rb') as f:
-    scaler = joblib.load(f)
-
-with open(encoder_path, 'rb') as f:
-    label_encoder = joblib.load(f)
+# Load SVR model and other pickled files
+svr_model = joblib.load('svr_model.pkl')
+scaler = joblib.load('scaler.pkl')
+label_encoder = joblib.load('label_encoder.pkl')
 
 # Feature extraction function
 def extract_features(image):
@@ -32,79 +21,33 @@ def extract_features(image):
     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()
+    # Additional feature extraction logic here...
 
     return {
         "meanr": meanr,
         "meang": meang,
         "meanb": meanb,
-        "HHR": HHR,
-        "Ent": Ent,
-        "B": B,
-        "g1": g1,
-        "g2": g2,
-        "g3": g3,
-        "g4": g4,
-        "g5": g5,
+        # Add more extracted features...
     }
 
 # Prediction function
 def predict(image, gender, age):
-    # Extract image features
-    features = extract_features(image)
-
-    # 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])
+    try:
+        # Extract image features
+        features = extract_features(image)
+        features["gender"] = label_encoder.transform([gender])[0]  # Transform gender label
+        features["age"] = age
 
-    # Scale the features using MinMaxScaler
-    features_scaled = scaler.transform(features_df)
+        # Normalize features using MinMaxScaler
+        features_df = pd.DataFrame([features])
+        scaled_features = scaler.transform(features_df)
 
-    # Predict using the SVR model
-    prediction = svr_model.predict(features_scaled)
+        # Make prediction using the SVR model
+        prediction = svr_model.predict(scaled_features)
+        return prediction[0]
 
-    # Return the prediction (you can format this depending on the model output)
-    return prediction[0]
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 # Gradio Interface
 interface = gr.Interface(
@@ -114,10 +57,10 @@ interface = gr.Interface(
         gr.Dropdown(choices=["Male", "Female"], label="Gender"),
         gr.Slider(0, 100, step=1, label="Age"),
     ],
-    outputs="number",
+    outputs="label",
     title="Image-based Prediction App",
-    description="Upload an image, enter your gender and age, and get predictions using the pre-trained SVR model."
+    description="Upload an image, enter your gender and age, and get predictions using the pre-trained model."
 )
 
 # Launch the app
-interface.launch()
+interface.launch(share=True)