Update app.py

app.py

@@ -1,15 +1,3 @@
-import gradio as gr
-import cv2
-import onnxruntime
-import numpy as np
-
-onnx_model_vgg19_path = "./vgg19-30epochs.onnx"
-onnx_model_inceptionv3_path = "./InceptionV3-20epochs.onnx"
-onnx_model_resnet101_path = "./Resnet101-30epochs.onnx"
-onnx_model_vgg16_path = "./vgg16-20epochs.onnx"
-
-labels = ['Ajloun Castle', 'Hadrians Arch', 'Petra-siq', 'Roman Ruins-Jerash', 'Roman amphitheater', 'The Cardo Maximus of Jerash', 'Wadi Rum', 'petra-Treasury', 'umm qais']
-
 def predict_image(image_path, model):
     if model == "InceptionV3":
         img_size = (550, 475)
@@ -47,15 +35,13 @@ def predict_image(image_path, model):
         output_name = model_resnet101.get_outputs()[0].name
         prediction = model_resnet101.run(None, {input_name: img})
 
-    softmax_output = np.exp(prediction[0][0]) / np.sum(np.exp(prediction[0][0]), axis=-1)
-    max_confidence = np.max(softmax_output)
-    predicted_class = np.argmax(softmax_output)
+    softmax_output = np.exp(prediction[0][0]) 
+    top_classes = np.argsort(softmax_output)[::-1][:3]
+    top_probabilities = softmax_output[top_classes]
 
-    if max_confidence < 0.2:
-        return "Hmm, it's a bit tricky. Feel free to add another image, and I'll do my best to make a guess!"
+    results = [(labels[class_index], round(probability * 100, 2)) for class_index, probability in zip(top_classes, top_probabilities)]
 
-    predicted_label = labels[predicted_class]
-    return predicted_label
+    return results
 
 inputs_image = [
     gr.Image(type="filepath", label="Input Image"),
@@ -66,7 +52,7 @@ inputs_image = [
         "Resnet101",
     ], type="value", label="Select_model")
 ]
-outputs_text = [gr.Textbox(label="Site")]
+outputs_text = [gr.Textbox(label="Top Predictions")]
 interface_image = gr.Interface(
     inputs=inputs_image,
     fn=predict_image,