app

app.py

@@ -380,7 +380,7 @@ def classify_image(image):
     clip_above_percentile=99,
     clip_below_percentile=0,
   )
-  return {labels[i]: float(prediction[i]) for i in range(100)} 
+  return img_grads
   
 image = gr.inputs.Image(shape=(299,299))
 label = gr.outputs.Image()
@@ -391,8 +391,8 @@ iface = gr.Interface(classify_image,image,label,
        	#	gr.outputs.Textbox(label="Engine issue score")], 
 	examples=["elephant.jpg"],
   title="Model interpretability with Integrated Gradients",
-	description = "Model interpretability with Integrated Gradients from <a href=\"https://keras.io/examples/vision/integrated_gradients/\">keras examples</a>.",
-        article = "Author: <a href=\"https://huggingface.co/joheras\">Jónathan Heras</a>"
+	description = "Model interpretability with Integrated Gradients.",
+        article = "Author: <a href=\"https://huggingface.co/joheras\">Jónathan Heras</a>. Based on the keras example from <a href=\"https://keras.io/examples/vision/integrated_gradients/\">A_K_Nain</a>"
 #	examples = ["sample.csv"],
 )