Upload app.py

app.py

@@ -1,7 +1,66 @@
 import gradio as gr
+import tensorflow as tf
+import numpy as np
+from numpy import asarray
 
-def greet(name):
-    return "Hello " + name + "!!"
+model = tf.keras.models.load_model("simple-CNN-model.2022-8-9.hdf5")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+def image_predict(img):
+    """
+    Displays dominant colors beyond a given threshold.
+    img : image input, for ex 'blue-car.jpg'
+    isize: input image size, for ex. 227
+    thr: chosen threshold value
+    """
+    thr=0
+    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)
+    
+    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)
+            resp = {}
+            resp[i] = f"{j}"
+            response.append(resp)
+        d = dict(map(dict.popitem, response))
+        print('the dictionary:', d)
+               
+        return dict(d)
+
+    else:
+        return str('No label was found')
+
+iface = gr.Interface(
+    title = "Product color tagging",
+    description = "App classifying images 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)), 
+    outputs=gr.Label(),
+    examples=['shoes1.jpg', 'shoes2.jpg'],
+    enable_queue=True,
+    interpretation="default",
+    debug=True
+    )
 iface.launch()
+
+
+