added video 2 and 3

app.py

@@ -60,14 +60,18 @@ def freedatatolb(amount_of_data):
         verbose = True, # If True, prints information about code execution
     )
     return results
-data_amount = st.slider("choose amout of data to add to labelbox", 250, 1000)
+data_amount = st.slider("choose amout of data to add to labelbox", 500, 1000)
 if st.button("Add data to your Labelbox"):
     st.write(f"adding {data_amount} datarows to Labelbox instance")
     bing = freedatatolb(data_amount)
-    st.write(bing)
-             
 st.title("SECTION 2")
-st.title("Auto Image classifier training and inference: Imagnet Weights")
+st.header("Create project and bulk classify images")
+st.video("https://storage.googleapis.com/app-videos/Setting%20up%20Platform%20for%20Training%20a%20Model.mp4")
+st.write("this video will help you set up a project for storing bulk classifications")
+st.video("https://storage.googleapis.com/app-videos/Bulk%20Classification%20and%20Training%20Our%20Model.mp4")
+st.write("this video teaches how to bulk classify the images and set up our model for training")             
+st.title("SECTION 3")
+st.header("Auto Image classifier training and inference: Imagnet Weights")
 
 # -*- coding: utf-8 -*-
 """
@@ -83,7 +87,7 @@ def train_and_inference(api_key, ontology_id, model_run_id):
     model_run_id = model_run_id #get the model run ID from the settings gear icon on the right side of your Model Run
     # st.write('1')
     import pydantic
-    st.write(pydantic.__version__)
+    # st.write(pydantic.__version__)
 
     import numpy as np
     # st.write('2')
@@ -206,7 +210,7 @@ def train_and_inference(api_key, ontology_id, model_run_id):
         download_and_save_image(image_url, destination_folder, filename)
 
     """#Train Model"""
-    st.write(labeldict)
+    # st.write(labeldict)
 
     import tensorflow as tf
     from tensorflow.keras.preprocessing.image import ImageDataGenerator
@@ -314,7 +318,7 @@ def train_and_inference(api_key, ontology_id, model_run_id):
     from tensorflow.errors import InvalidArgumentError  # Add this import
     ontology = client.get_ontology(ontology_id)
     label_list = []
-    st.write(ontology)
+    # st.write(ontology)
     for datarow in model_run.export_labels(download=True):
         try:
             label, confidence = make_prediction(datarow['Labeled Data'])