adaptation table output

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 from numpy import asarray
+from datetime import datetime
+
 
 model = tf.keras.models.load_model("simple-CNN-model.2022-8-9.hdf5")
 
@@ -16,32 +18,30 @@ def image_predict(img):
     global model
     if model is None:
         model = tf.keras.models.load_model("models/simple-CNN-model.2022-8-9.hdf5")
-        print('model is loaded')
-    
+        
     data = np.asarray(img)
-    print("data is: ", data, type(data)) 
-    
+        
     ndata = np.expand_dims(data, axis=0)
     y_prob = model.predict(ndata/255)
-    print('Yprob:', y_prob)
-    y_prob.argmax(axis=-1)
-    print('yprob', y_prob)
+    #y_prob.argmax(axis=-1)
     
+    now = datetime.now()
+    print("--------")
+    print("data and time: ", now)
+            
     colorlabels = ['beige', 'black', 'blue', 'brown', 'gold', 'green', 'grey', 'orange', 'pink', 'purple', 'red', 'silver', 'tan', 'white', 'yellow']
-    print('color', [sorted(colorlabels)[i] for i in np.where(np.ravel(y_prob)>thr)[0]])
-    print('values', [np.ravel(y_prob)[i] for i in list(np.where(np.ravel(y_prob)>thr)[0])])
     coltags = [sorted(colorlabels)[i] for i in np.where(np.ravel(y_prob)>thr)[0]]
     colprob = [np.ravel(y_prob)[i] for i in list(np.where(np.ravel(y_prob)>thr)[0])]
     
     if len(coltags) > 0:
         response = []
         for i,j in zip(coltags, colprob):
-            print(i,j)
+            #print(i,j)
             resp = {}
-            resp[i] = f"{j}"
+            resp[i] = float(j)
             response.append(resp)
         d = dict(map(dict.popitem, response))
-        print('the dictionary:', d)
+        print('colors: ', d)
                
         return dict(d)
 
@@ -49,8 +49,8 @@ def image_predict(img):
         return str('No label was found')
 
 iface = gr.Interface(
-    title = "Product color tagging",
-    description = "App classifying images on different colors",
+    title = "Object color tagging",
+    description = "App classifying objects on different colors",
     article = "<p style='text-align: center'><a href='https://www.rrighart.com' target='_blank'>Webpage</a></p>",
     fn=image_predict,
     inputs=gr.Image(shape=(227,227)),