text box to tell percenatge of face area and image viewer to check the detected face added

app.py

@@ -5,6 +5,7 @@ import cv2
 from tensorflow.keras.preprocessing import image
 from tensorflow.keras.models import load_model
 
+
 # Load the pre-trained face detection model
 face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
 
@@ -23,6 +24,7 @@ def classify_image(img):
 
     # Detect faces in the image
     faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+    
     # Check if any faces were detected
     if len(faces) > 0:
         #print("Human face detected in the image!")
@@ -35,9 +37,6 @@ def classify_image(img):
         #print(sorted(face_area_list))
         big_face_area = sorted(face_area_list)[-1]
         img_area = img_copy.shape[0] * img_copy.shape[1]
-        print(big_face_area)
-        print(img_area)
-        print("% Area of the largest face in image:", (big_face_area/img_area)*100)
         perc_area = (big_face_area/img_area)*100
         if perc_area>7:
             img = image.img_to_array(img)
@@ -47,21 +46,17 @@ def classify_image(img):
             # Use the model to make a prediction
             prediction = model.predict(img)[0]
             # Map the predicted class to a label
-            dic = {'NSFW': float(prediction[1]), 'NSFW': float(prediction[0]),'SFW':float(prediction[2])}
+            dic = {'NSFW': float(prediction[1]), 'CART': float(prediction[0]),'SFW':float(prediction[2])}
         else :
-            dic = {'SFW': float(0), 'NSFW': float(1)}
-        # Display the image with the detected faces
-        #cv2_imshow(img)
-        #cv2.imshow('Detected faces', img_copy)
-        #cv2.waitKey(0)
-        #cv2.destroyAllWindows()
-        # Preprocess the input image
+            dic = {'CART': float(0),'SFW': float(0), 'NSFW': float(1)}
+            
         
     else:
-        dic = {'SFW': float(0), 'NSFW': float(1)}
+        dic = {'CART': float(0),'SFW': float(0), 'NSFW': float(1)}
+        perc_area = "could not detected face"
         #print("No human face detected in the image.")
     
-    return dic
+    return [dic, perc_area, img_copy]
 
 def classify_url(url):
     # Load the image from the URL
@@ -73,8 +68,15 @@ def classify_url(url):
 
 # Define the GRADIO output interface
 examples = [f"example{i}.jpg" for i in range(1,9)]
+
+# Define the GRADIO output interfaces
+output_interfaces = [
+    gr.outputs.Label(num_top_classes=3),
+    gr.outputs.Textbox(label="% Area of the largest face in image"),
+    gr.outputs.Image(type="pil", label="Detected Faces")
+]
 # Define the GRADIO app
-app = gr.Interface(classify_image, gr.Image(shape=(224, 224)), outputs="label", allow_flagging="never", examples = examples,title="NSFW/SFW Classifier")
+app = gr.Interface(classify_image, gr.Image(shape=(224, 224)), outputs=output_interfaces, allow_flagging="never", examples = examples,title="NSFW/SFW Classifier")
 
 # Start the GRADIO app
 app.launch()