Update app.py

app.py

@@ -1,29 +1,58 @@
 import gradio as gr
 import numpy as np
-import pandas as pd
 from tensorflow.keras import models 
 
 import tensorflow as tf
 
+models_name = [
+    "VGG16",
+    "mobilenet_v2",
+    "DenseNet"
+]
+
 # open categories.txt in read mode
 categories = open("categories.txt", "r")
 labels = categories.readline().split(";")
 
-model = models.load_model('models/modelnet/best_model.h5')
+# create a radio
+radio = gr.inputs.Radio(models_name, default="DenseNet", type="value")
+
+def predict_image(image, model_name):
+
 
+    print("======================")
+    print(type(image))
+    print(type(model_name))
+    print("==========")
+    print(image)
+    print(model_name)
+    print("======================")
 
-def predict_image(image):
-    image = np.array(image) / 255
-    image = np.expand_dims(image, axis=0)
+    if model_name == "DenseNet":
+        image = np.array(image) / 255
+        image = np.expand_dims(image, axis=0)
+
+        model = "./models/" + model_name + "model.h5"
+        pred = model.predict(image)
+
+        pred = dict((labels[i], "%.2f" % pred[0][i]) for i in range(len(labels)))
+    else:
+        
+        image = Image.fromarray(np.uint8(image)).convert('RGB')
+        classifier = TorchVisionClassifierInference(
+            model_path = "./models/" + model_name
+        )
 
-    pred = model.predict(image)
+        pred = classifier.predict_image(img=image, return_str=False)
 
-    acc = dict((labels[i], "%.2f" % pred[0][i]) for i in range(len(labels)))
-    print(acc)
-    return acc
+        for key in pred.keys():
+            pred[key] = pred[key]/100
+    
 
+    print(pred)
+    return pred
 
-image = gr.inputs.Image(shape=(224, 224), label="Upload Your Image Here")
+image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here")
 label = gr.outputs.Label(num_top_classes=len(labels))
 
 samples = ['samples/basking.jpg', 'samples/blacktip.jpg', 'samples/blue.jpg', 'samples/bull.jpg', 'samples/hammerhead.jpg',