Update app.py

app.py

@@ -1,24 +1,16 @@
 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
+import numpy as np
+import joblib
+import os
 from skimage.measure import shannon_entropy
+from skimage.color import rgb2hsv
 from scipy.ndimage import generic_filter
+import cv2
 
-# 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
+# Function to extract features from the image
 def extract_features(image_path):
+    # Load image and convert to RGB
     image = cv2.imread(image_path)
     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     
@@ -81,49 +73,70 @@ def extract_features(image_path):
         "g5": g5,
     }
 
-# Function to make predictions
+# Function to check if the image is a valid file format
+def check_image_format(image):
+    try:
+        img = cv2.imread(image)
+        if img is not None:
+            return True
+        else:
+            return False
+    except Exception as e:
+        return False
+
+# 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)
-    features['Age'] = age
-    features['Gender'] = 1 if gender.lower() == 'male' else 0
+    
+    # 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 models
+    # Load the trained model, scaler, and label encoder
     svr_model = joblib.load('svr_model.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}"
 
-# Gradio Interface
-def gradio_interface():
-    with gr.Blocks() as demo:
-        gr.Markdown("## Hemoglobin Prediction from Image Features")
-        
-        with gr.Row():
-            age = gr.Number(label="Age", value=25)
-            gender = gr.Dropdown(choices=["Male", "Female"], label="Gender", value="Male")
-        
-        image_input = gr.Image(type="filepath", label="Upload Image (Path to Image)", interactive=True)
-        
-        output = gr.Textbox(label="Predicted Hemoglobin Value")
-        
-        # Prediction button
-        predict_btn = gr.Button("Predict Hemoglobin")
-        
-        predict_btn.click(fn=predict_hemoglobin, inputs=[age, gender, image_input], outputs=output)
+# Gradio Interface setup
+def create_gradio_interface():
+    # Define the inputs and outputs for the Gradio interface
+    image_input = gr.Image(type="filepath", label="Image (Path to Image)", interactive=True)
+    age_input = gr.Number(label="Age", value=25, precision=0)
+    gender_input = gr.Radio(choices=["Male", "Female"], label="Gender", value="Male")
     
-    return demo
+    # 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)
 
-# Launch the app
+# Run the Gradio app
 if __name__ == "__main__":
-    gradio_interface().launch(share=True)
+    create_gradio_interface()