Update app.py

app.py

@@ -1,16 +1,14 @@
-import os
-import cv2
+import gradio as gr
 import joblib
 import numpy as np
 import pandas as pd
-import gradio as gr
+import cv2
 from skimage.color import rgb2hsv
 from skimage.measure import shannon_entropy
 from scipy.ndimage import generic_filter
 
-# Function to extract features from the image
+# Extract features from the image (same as your previous code)
 def extract_features(image_path):
-    # Load image and convert to RGB
     image = cv2.imread(image_path)
     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     
@@ -58,7 +56,7 @@ def extract_features(image_path):
     g4 = generic_filter(gray_image, g4_filter, size=3).mean()
     g5 = generic_filter(gray_image, g5_filter, size=3).mean()
 
-    # Return extracted features
+    # Return features
     return {
         "meanr": meanr,
         "meang": meang,
@@ -73,10 +71,10 @@ def extract_features(image_path):
         "g5": g5,
     }
 
-# Function to predict hemoglobin
+# Function to make predictions
 def predict_hemoglobin(age, gender, image):
     # Extract features from the image
-    features = extract_features(image.name)  # Get the image file path
+    features = extract_features(image)  # Use the file path directly
     
     # Add age and gender to the features
     features['age'] = age
@@ -85,37 +83,39 @@ def predict_hemoglobin(age, gender, image):
     # Convert features to DataFrame
     features_df = pd.DataFrame([features])
     
-    # Load pre-trained models
+    # Load the pre-trained models
     svr_model = joblib.load('svr_model.pkl')  # SVR model
-    scaler = joblib.load('minmax_scaler.pkl')        # MinMaxScaler
+    scaler = joblib.load('scaler.pkl')        # MinMaxScaler
     label_encoder = joblib.load('label_encoder.pkl')  # LabelEncoder
 
     # Apply MinMaxScaler transformation
     features_df_scaled = scaler.transform(features_df)
     
-    # Predict hemoglobin value
+    # Make the prediction
     hemoglobin = svr_model.predict(features_df_scaled)[0]
     
     return f"Predicted Hemoglobin Value: {hemoglobin:.2f}"
 
 # Gradio Interface
-def create_gradio_interface():
-    # Define the input components (age, gender, and image)
-    age_input = gr.Number(label="Age", value=25, interactive=True)
-    gender_input = gr.Dropdown(choices=["Male", "Female"], label="Gender", value="Male", interactive=True)
-    image_input = gr.Image(type="file", label="Image (Path to Image)", interactive=True)
-    
-    # Define the output component (prediction result)
-    output = gr.Textbox(label="Hemoglobin Prediction")
+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)
     
-    # Create the Gradio interface
-    gr.Interface(
-        fn=predict_hemoglobin,
-        inputs=[age_input, gender_input, image_input],
-        outputs=output,
-        live=True
-    ).launch()
+    return demo
 
-# Start the Gradio app
+# Launch the app
 if __name__ == "__main__":
-    create_gradio_interface()
+    gradio_interface().launch(share=True)  # Set `share=True` to create a public link