Update app.py

app.py

@@ -1,69 +1,41 @@
 import gradio as gr
 import numpy as np
-from PIL import Image
-import os
-import json
+import pandas as pd
+from tensorflow.keras import models 
 
-from hugsvision.inference.TorchVisionClassifierInference import TorchVisionClassifierInference
+import tensorflow as tf
 
-models_name = [
-    "VGG16",
-    "ShuffleNetV2",
-    "mobilenet_v2"
-]
-
-colname = "mobilenet_v2"
-
-radio = gr.inputs.Radio(models_name, default="mobilenet_v2", type="value", label=colname)
-print(radio.label)
-
-def predict_image(image, model_name):
-
-    image = Image.fromarray(np.uint8(image)).convert('RGB')
+# open categories.txt in read mode
+categories = open("categories.txt", "r")
+labels = categories.readline().split(";")
 
-    print("======================")
-    print(type(image))
-    print(type(model_name))
-    print("==========")
-    print(image)
-    print(model_name)
-    print("======================")
+model = models.load_model('models/modelnet/best_model.h5')
 
-    # image = np.array(image) / 255
-    # image = np.expand_dims(image, axis=0)
 
-    classifier = TorchVisionClassifierInference(
-        model_path = "./models/" + colname,
-    )
+def predict_image(image):
+    image = np.array(image) / 255
+    image = np.expand_dims(image, axis=0)
 
-    pred = classifier.predict_image(img=image)
-    print(pred)
+    pred = model.predict(image)
 
-    acc = dict((labels[i], 0.0) for i in range(len(labels)))
-    acc[pred] = 100.0
+    acc = dict((labels[i], "%.2f" % pred[0][i]) for i in range(len(labels)))
     print(acc)
     return acc
-    # return pred
 
-# open categories.txt in read mode
-categories = open("categories.txt", "r")
-labels = categories.readline().split(";")
 
-image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here")
-print(image)
+image = gr.inputs.Image(shape=(224, 224), label="Upload Your Image Here")
 label = gr.outputs.Label(num_top_classes=len(labels))
 
-samples = ['./samples/basking.jpg', './samples/blacktip.jpg']
-# , './samples/blacktip.jpg', './samples/blue.jpg', './samples/bull.jpg', './samples/hammerhead.jpg',
-#         './samples/lemon.jpg', './samples/mako.jpg', './samples/nurse.jpg', './samples/sand tiger.jpg', './samples/thresher.jpg', 
-#         './samples/tigre.jpg', './samples/whale.jpg', './samples/white.jpg', './samples/whitetip.jpg']
+samples = ['samples/basking.jpg', 'samples/blacktip.jpg', 'samples/blue.jpg', 'samples/bull.jpg', 'samples/hammerhead.jpg',
+        'samples/lemon.jpg', 'samples/mako.jpg', 'samples/nurse.jpg', 'samples/sand tiger.jpg', 'samples/thresher.jpg', 
+        'samples/tigre.jpg', 'samples/whale.jpg', 'samples/white.jpg', 'samples/whitetip.jpg']
         
 interface = gr.Interface(
     fn=predict_image, 
-    inputs=[image, radio], 
+    inputs=image, 
     outputs=label, 
     capture_session=True, 
     allow_flagging=False, 
-    # examples=samples
+    examples=samples
 )
 interface.launch()