Update app.py

app.py

@@ -12,28 +12,28 @@ from PIL import Image
 def extract_features(image):
     # Convert PIL image to NumPy array
     image = np.array(image)
-    
+
     # Extract RGB means
     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)
@@ -85,43 +85,45 @@ def check_image_format(image):
 
 # Function to predict hemoglobin value
 def predict_hemoglobin(age, gender, image):
-    print(f"Image path: {image}")  # Debugging line to check the image path
-
-    # Check if the image file is valid
-    if not check_image_format(image):
-        return "Error: The uploaded image file is not recognized or is corrupt."
-    
-    # Extract features from the image
-    features = extract_features(image)
-    
-    # Ensure gender is encoded correctly (0 for female, 1 for male)
-    features['Gender'] = 1 if gender.lower() == 'male' else 0
-    features['Age'] = age
-    
-    # Create a DataFrame for features (do not include Hgb, as it's the predicted value)
-    features_df = pd.DataFrame([features])
-
-    # Load the trained model, scaler, and label encoder
-    svr_model = joblib.load('svr_model(1).pkl')
-    scaler = joblib.load('minmax_scaler.pkl')
-    label_encoder = joblib.load('label_encoder.pkl')
-
-    # Ensure that features_df matches the expected training feature set (without 'Hgb')
-    expected_columns = ['meanr', 'meang', 'meanb', 'HHR', 'Ent', 'B', 'g1', 'g2', 'g3', 'g4', 'g5', 'Age', 'Gender']
-    
-    for col in expected_columns:
-        if col not in features_df:
-            features_df[col] = 0  # Or some default value to match the expected columns.
-    
-    features_df = features_df[expected_columns]  # Ensure the correct order of columns
-    
-    # Apply scaling (do not include 'Hgb' as it is the target)
-    features_df_scaled = scaler.transform(features_df)
-    
-    # Predict hemoglobin using the trained SVR model
-    hemoglobin = svr_model.predict(features_df_scaled)[0]
-    
-    return f"Predicted Hemoglobin Value: {hemoglobin:.2f}"
+    try:
+        # Check if the image file is valid
+        if not check_image_format(image):
+            return "Error: The uploaded image file is not recognized or is corrupt. Please upload an image in JPG, PNG, BMP, or GIF format."
+
+        # Extract features from the image
+        features = extract_features(image)
+
+        # Ensure gender is encoded correctly (0 for female, 1 for male)
+        features['Gender'] = 1 if gender.lower() == 'M' else 0
+        features['Age'] = age
+
+        # Create a DataFrame for features (do not include Hgb, as it's the predicted value)
+        features_df = pd.DataFrame([features])
+
+        # Load the trained model, scaler, and label encoder
+        svr_model = joblib.load('svr_model(1).pkl')  # Replace with the actual path to your model file
+        scaler = joblib.load('minmax_scaler.pkl')     # Replace with the actual path to your scaler file
+
+        # Ensure that features_df matches the expected training feature set (without 'Hgb')
+        expected_columns = ['meanr', 'meang', 'meanb', 'HHR', 'Ent', 'B', 'g1', 'g2', 'g3', 'g4', 'g5', 'Age', 'Gender']
+
+        for col in expected_columns:
+            if col not in features_df:
+                features_df[col] = 0  # Or some default value to match the expected columns.
+
+        features_df = features_df[expected_columns]  # Ensure the correct order of columns
+
+        # Apply scaling (do not include 'Hgb' as it is the target)
+        features_df_scaled = scaler.transform(features_df)
+
+        # Predict hemoglobin using the trained SVR model
+        hemoglobin = svr_model.predict(features_df_scaled)[0]
+
+        return f"Predicted Hemoglobin Value: {hemoglobin:.2f}"
+
+    except Exception as e:
+        print(f"An error occurred during prediction: {e}")
+        return "An error occurred during prediction. Please try again."
 
 # Gradio Interface setup
 def create_gradio_interface():
@@ -129,13 +131,16 @@ def create_gradio_interface():
     image_input = gr.Image(type="pil", label="Image (Upload Image)", interactive=True)
     age_input = gr.Number(label="Age", value=25, precision=0)
     gender_input = gr.Radio(choices=["Male", "Female"], label="Gender", value="Male")
-    
+
     # Set up the Gradio interface with the prediction function
-    gr.Interface(fn=predict_hemoglobin,
-                 inputs=[age_input, gender_input, image_input],
-                 outputs="text",
-                 live=True).launch(share=True)
+    iface = gr.Interface(fn=predict_hemoglobin,
+                         inputs=[age_input, gender_input, image_input],
+                         outputs="text",
+                         live=True,
+                         title="Hemoglobin Prediction")
+
+    iface.launch(share=True)  # Set share=True to create a public link
 
 # Run the Gradio app
 if __name__ == "__main__":
-    create_gradio_interface()
+    create_gradio_interface()