Update app.py

app.py

@@ -1,66 +1,98 @@
-import gradio as gr
 import joblib
 import numpy as np
 import pandas as pd
 import cv2
-from sklearn.preprocessing import MinMaxScaler, LabelEncoder
-from PIL import Image
+from skimage.color import rgb2hsv
+from skimage.measure import shannon_entropy
+from scipy.ndimage import generic_filter
 
-# Load SVR model and other pickled files
-svr_model = joblib.load('svr_model.pkl')
-scaler = joblib.load('minmax_scaler.pkl')
-label_encoder = joblib.load('label_encoder.pkl')
-
-# Feature extraction function
-def extract_features(image):
-    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+# Extract features from the image (same as your previous code)
+def extract_features(image_path):
+    image = cv2.imread(image_path)
     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])
+    meanr = np.mean(image[:, :, 0])  # Red channel
+    meang = np.mean(image[:, :, 1])  # Green channel
+    meanb = np.mean(image[:, :, 2])  # Blue channel
+    
+    # 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
+    Ent = shannon_entropy(gray_image)
+    
+    # Compute Brightness
+    B = np.mean(gray_image)
+    
+    # Sliding window for gray-level features
+    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)
 
-    # Additional feature extraction logic here...
+    def g5_filter(window):
+        return window[4]
 
+    # Apply filters with 3x3 window
+    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 features
     return {
         "meanr": meanr,
         "meang": meang,
         "meanb": meanb,
-        # Add more extracted features...
+        "HHR": HHR,
+        "Ent": Ent,
+        "B": B,
+        "g1": g1,
+        "g2": g2,
+        "g3": g3,
+        "g4": g4,
+        "g5": g5,
     }
 
-# Prediction function
-def predict(image, gender, age):
-    try:
-        # Extract image features
-        features = extract_features(image)
-        features["gender"] = label_encoder.transform([gender])[0]  # Transform gender label
-        features["age"] = age
-
-        # Normalize features using MinMaxScaler
-        features_df = pd.DataFrame([features])
-        scaled_features = scaler.transform(features_df)
-
-        # Make prediction using the SVR model
-        prediction = svr_model.predict(scaled_features)
-        return prediction[0]
-
-    except Exception as e:
-        return f"Error: {str(e)}"
+# Function to make predictions
+def predict_hemoglobin(age, gender, image_path):
+    # Extract features from the image
+    features = extract_features(image_path)
+    
+    # Add age and gender to the features
+    features['age'] = age
+    features['gender'] = 1 if gender.lower() == 'male' else 0
+    
+    # Convert features to DataFrame
+    features_df = pd.DataFrame([features])
+    
+    # Load the pre-trained models
+    svr_model = joblib.load('svr_model.pkl')  # SVR model
+    scaler = joblib.load('minmax_scaler.pkl')        # MinMaxScaler
+    label_encoder = joblib.load('label_encoder.pkl')  # LabelEncoder
 
-# 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."
-)
+    # Apply MinMaxScaler and LabelEncoder transformations
+    # For age and gender, you can scale them or leave them as-is, depending on your training procedure
+    features_df_scaled = scaler.transform(features_df)
+    
+    # Make the prediction
+    hemoglobin = svr_model.predict(features_df_scaled)[0]
+    
+    return hemoglobin
 
-# Launch the app
-interface.launch(share=True)