Upload app.py

app.py

@@ -0,0 +1,54 @@
+import cv2
+import tensorflow as tf
+import gradio as gr
+
+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 classifier(Imgarr):
+    l = []
+    #img1 = cv2.imread(Imgarr)
+    img = cv2.resize(Imgarr,(50,50))
+    img = img.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 l
+
+interface = gr.Interface(classifier,gr.inputs.Image(shape=(50,50)),outputs = "text",
+                          description="Classifier of images of daisy plants, dandelion, roses, sunflowers, and tulips",
+                          title="Flower Image Classifier",
+                          examples=[['42.jpg'],['56.jpg'],['64.jpg']])
+interface.launch()