Update app.py

app.py

@@ -1,7 +1,51 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+label_dict = {}
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+f = open('baseline_keras_model_labels.txt', 'r')
+
+lines = f.readlines()
+for line in lines:
+    index, label_name = line.split(' ')
+    label_dict[int(index)] = label_name.strip()
+f.close()
+
+from keras.models import load_model
+from PIL import Image, ImageOps
+import numpy as np
+import os
+
+# Load the model
+model = load_model('baseline_keras_model.h5')
+
+# Create the array of the right shape to feed into the keras model
+# The 'length' or number of images you can put into the array is
+# determined by the first position in the shape tuple, in this case 1.
+data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+# Replace this with the path to your image
+
+folder_path = 'x_test'
+
+y_pred = []
+
+def clf(name):
+
+    image = Image.open(fpath)
+    size = (224, 224)
+    image = ImageOps.fit(image, size, Image.ANTIALIAS)
+
+    #turn the image into a numpy array
+    image_array = np.asarray(image)
+    # Normalize the image
+    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+    # Load the image into the array
+    data[0] = normalized_image_array
+
+    # run the inference
+    prediction = model.predict(data)
+    class_index = np.argmax(prediction)
+    
+    return label_dict[class_index]
+
+iface = gr.Interface(fn=clf, inputs="image", outputs="text")
 iface.launch()