Update app.py

app.py

@@ -1,77 +1,76 @@
-import tensorflow as tf
-import gradio as gr
-import cv2
-
-json_file=open(r"dark_s.json","r")
-loaded_model_json=json_file.read()
-json_file.close()
-loaded_model= tf.keras.models.model_from_json(loaded_model_json)
-loaded_model.load_weights("dark_s.h5")
-
-json_file=open(r"eyes_d.json","r")
-loaded_model_json=json_file.read()
-json_file.close()
-loaded_model1 = tf.keras.models.model_from_json(loaded_model_json)
-loaded_model1.load_weights("eyes_d.h5")
-
-json_file=open(r"wrinkl.json","r")
-loaded_model_json=json_file.read()
-json_file.close()
-loaded_model2 = tf.keras.models.model_from_json(loaded_model_json)
-loaded_model2.load_weights("wrinkl.h5")
-
-
-def image_bounder(img1):
-    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +'haarcascade_frontalface_default.xml')
-    eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +'haarcascade_eye.xml')
-    gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
-    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
-
-    for (x,y,w,h) in faces:
-        cv2.rectangle(img1,(x,y),(x+w,y+h),(255,255,0),2)
-        roi_gray = gray[y:y+h, x:x+w]
-        roi_color = img1[y:y+h, x:x+w]
-
-        eyes = eye_cascade.detectMultiScale(roi_gray)
-
-        for (ex,ey,ew,eh) in eyes:
-            cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,127,255),2)
-
-    return img1
-
-
-def classifier(Imgarr):
-    l = []
-    img = Imgarr.reshape(-1, 50, 50, 3)
-    result = loaded_model.predict(img)
-    result = result[0]
-    if result[0] >= result[1]:
-        l.append("dark spots")
-    else:
-        l.append("no dark spots")
-
-    result = loaded_model1.predict(img)
-    result = result[0]
-    if result[0] >= result[1]:
-        l.append("no puffy eyes")
-    else:
-        l.append("puffy eyes")
-
-    result = loaded_model2.predict(img)
-    result = result[0]
-    if result[0] >= result[1]:
-        l.append("no wrinkles on face")
-    else:
-        l.append("wrinkles on face")
-
-    return "Predictions are : "+str(l)
-
-def out(Imgarr):
-    return [image_bounder(Imgarr),classifier(Imgarr)]
-
-interface = gr.Interface(out,gr.inputs.Image(shape=(300,300)),
-                          outputs = ["image","text"],
-                          description="Classifier of Aging Signs of Images",
-                          title="Aging Signs Classifier",
-                          examples=[['42.jpg'],['71.jpg'],['11.jfif'],['56.jpg'],['1836.png']])
+import tensorflow as tf
+import gradio as gr
+import cv2
+
+json_file=open(r"dark_s.json","r")
+loaded_model_json=json_file.read()
+json_file.close()
+loaded_model= tf.keras.models.model_from_json(loaded_model_json)
+loaded_model.load_weights("dark_s.h5")
+
+json_file=open(r"eyes_d.json","r")
+loaded_model_json=json_file.read()
+json_file.close()
+loaded_model1 = tf.keras.models.model_from_json(loaded_model_json)
+loaded_model1.load_weights("eyes_d.h5")
+
+json_file=open(r"wrinkl.json","r")
+loaded_model_json=json_file.read()
+json_file.close()
+loaded_model2 = tf.keras.models.model_from_json(loaded_model_json)
+loaded_model2.load_weights("wrinkl.h5")
+
+
+def image_bounder(img1):
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +'haarcascade_frontalface_default.xml')
+    eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +'haarcascade_eye.xml')
+    gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+    for (x,y,w,h) in faces:
+        cv2.rectangle(img1,(x,y),(x+w,y+h),(255,255,0),2)
+        roi_gray = gray[y:y+h, x:x+w]
+        roi_color = img1[y:y+h, x:x+w]
+
+        eyes = eye_cascade.detectMultiScale(roi_gray)
+
+        for (ex,ey,ew,eh) in eyes:
+            cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,127,255),2)
+
+    return img1
+
+def classifier(Imgarr):
+    l = []
+    img = Imgarr.reshape(-1, 50, 50, 3)
+    result = loaded_model.predict(img)
+    result = result[0]
+    if result[0] >= result[1]:
+        l.append("dark spots")
+    else:
+        l.append("no dark spots")
+
+    result = loaded_model1.predict(img)
+    result = result[0]
+    if result[0] >= result[1]:
+        l.append("no puffy eyes")
+    else:
+        l.append("puffy eyes")
+
+    result = loaded_model2.predict(img)
+    result = result[0]
+    if result[0] >= result[1]:
+        l.append("no wrinkles on face")
+    else:
+        l.append("wrinkles on face")
+
+    return "Predictions are : "+str(l)
+
+def out(Imgarr):
+    return [image_bounder(Imgarr),classifier(Imgarr)]
+
+interface = gr.Interface(out,gr.inputs.Image(shape=(300,300)),
+                          outputs = ["image","text"],
+                          description="Classifier of Aging Signs of Images",
+                          title="Aging Signs Classifier",
+                          examples=[['42.jpg'],['71.jpg'],['11.jfif'],['56.jpg'],['1836.png']])
 interface.launch()