Update app.py

app.py

@@ -2,12 +2,22 @@ import gradio as gr
 import joblib
 import numpy as np
 import pandas as pd
+from PIL import Image
 import cv2
 from skimage.color import rgb2hsv
 from skimage.measure import shannon_entropy
 from scipy.ndimage import generic_filter
 
-# Extract features from the image (same as your previous code)
+# Function to check image validity
+def check_image_format(image_path):
+    try:
+        with Image.open(image_path) as img:
+            img.verify()  # Verify if it's a valid image
+        return True
+    except (IOError, SyntaxError):
+        return False
+
+# Extract features from the image
 def extract_features(image_path):
     image = cv2.imread(image_path)
     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
@@ -73,25 +83,23 @@ def extract_features(image_path):
 
 # Function to make predictions
 def predict_hemoglobin(age, gender, image):
-    # Extract features from the image
-    features = extract_features(image)  # Use the file path directly
+    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."
     
-    # Add age and gender to the features
+    features = extract_features(image)
     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
 
-    # Apply MinMaxScaler transformation
+    # Load models
+    svr_model = joblib.load('svr_model.pkl')
+    scaler = joblib.load('scaler.pkl')
+    label_encoder = joblib.load('label_encoder.pkl')
+
     features_df_scaled = scaler.transform(features_df)
-    
-    # Make the prediction
     hemoglobin = svr_model.predict(features_df_scaled)[0]
     
     return f"Predicted Hemoglobin Value: {hemoglobin:.2f}"
@@ -118,4 +126,4 @@ def gradio_interface():
 
 # Launch the app
 if __name__ == "__main__":
-    gradio_interface().launch(share=True)  # Set `share=True` to create a public link
+    gradio_interface().launch(share=True)