add interface and file reading to app.py

app.py

@@ -1,12 +1,89 @@
+### ----------------------------- ###
+###           libraries           ###
+### ----------------------------- ###
+
 from tensorflow.keras.models import load_model
-import gradio as gr
+import gradio as gr # remove later
 import numpy as np
-# from skimage import transform
-import h5py
+from yattag import Doc
+import h5py # remove later
+
+### -------------------------------- ###
+###           model loading          ###
+### -------------------------------- ###
 
 model = load_model('model.h5') # single file model
 labels = ['cheetah', 'hyena', 'jaguar', 'tiger'] # will need to be loaded from file in the order of your directories
 
+## -------------------------------- ###
+###            file reading          ###
+### -------------------------------- ###
+# placeholders in case info.txt does not exist
+placeholder =  "please create an info.txt to customize this text"
+title = bkgd = data_collection = priv_cons = bias_cons = ident_cons = img_src = membs = placeholder
+description = "An AI project created by [name], [name], and [name]"
+# check if info.txt is present
+if os.path.isfile("info.txt"):
+    # open info.txt in read mode
+    info = open("info.txt", "r")
+ 
+    # each line to a string
+    title = info.readline()
+    bkgd = info.readline()
+    data_collection = info.readline()
+    priv_cons = info.readline()
+    bias_cons = info.readline()
+    ident_cons = info.readline()
+    img_src = info.readline()
+    membs = info.readline()
+    
+    # close file
+    info.close()
+    
+# use yattag library to generate html
+doc, tag, text, line = Doc().ttl()
+# create html based on info.txt
+with tag('div'):
+  with tag('div', klass='my-div'):
+    line('h2', 'Project Background')
+    line('p', bkgd)
+  with tag('div', klass='my-div'):
+    line('h2', 'Data Collection')
+    line('p', data_collection)
+  with tag('div', klass='my-div'):
+    line('h2', 'Ethical Considerations')
+    with tag('ul'):
+      line('li', priv_cons)
+      line('li', bias_cons)
+      line('li', ident_cons)
+  with tag('div', klass='my-div'):
+    line('h2', 'Our Team')
+    line('p', membs)
+    doc.stag('img', src=img_src)
+    
+my_css = '''
+.my-div {
+  border: 2px solid black;
+  text-align: center;
+  margin: 10px;
+  padding: 5%;
+}
+ul {
+  display: inline-block;
+  text-align: left;
+}
+img {
+  display: block;
+  margin: auto;
+}
+.description {
+  text-align: center;
+}
+'''
+### ------------------------------- ###
+###        interface creation       ###
+### ------------------------------- ###
+
 def preprocess(image):
   image = np.array(image) / 255
   # image = transform.resize(image, (300, 300, 3))
@@ -25,4 +102,4 @@ def match_prediction_with_label(test_img):
 image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here")
 label = gr.outputs.Label(num_top_classes=4)
 
-gr.Interface(fn=match_prediction_with_label, inputs=image, outputs=label, capture_session=True).launch(share=True, debug=True)
+gr.Interface(fn=match_prediction_with_label, inputs=image, outputs=label, capture_session=True, article=doc.getvalue(), css=my_css, theme='huggingface', title=title, allow_flagging=False, description=description).launch(share=True, debug=True)