Ai chatbot with local image classification

Task c – Train and add a local image classification model and evaluate it: This should include the updated versions of the stage 1 files/documentation as well as your training program, a saved model and sample image files. Your updated python program provides this new feature: If the user inputs a question such as “What is img01.jpg”?, the chatbot should invoke a pre-trained convolutional neural network to provide the answer. You decide yourself which network architecture and training data set to use. If you wish, you can decide to use a different source of images instead of files, e.g., camera input, and also decide to trigger this feature in other ways. You will need to train your neural network using an image dataset with class labels. There are web sites that serve as a repository for many good data sets such as Kaggle.com or the UCI Machine Learning Repository. Don’t worry too much if it doesn’t fit that well with the topic of chatbot. A weak connection between the image topics and your chatbot topic is acceptable. Once trained, you should save the model (e.g., as a h5 file) and include in your submission. In your main chatbot program, you will import the pre-trained model and use it for serving user’s query on image classification. Report and explain the model’s performance using n-fold cross-validation method and a measurement of your choice. If the classification does not perform well discuss the causes and/or limitations. See the notes on week 14 lab sheet for more information.
3

4
• Task d: Add a cloud-based image classification service: The idea is that in addition to the model training, your chatbot can also analyse images based on a model that you develop as a web service on Azure cloud. You can use either the Image Analysis feature (pre-trained) or the Image Classification with Custom Vision (user-trained) or any relevant service of your choice in the Azure’s Computer Vision group of services. You will provide the user with result of this image classification alongside with the results of task c. You will need to make sure that your Azure web service is up and running by the time your system is tested by your tutor. See the notes on week 18 lab sheet for more information.
Submission Stage-2 (due date 24/03) - Includes tasks c and d.
Files to submit: A zip file containing Python program for model training (py or ipynb), updated python program of the chatbot (py or ipynb), sample image files for test, AIML file (xml), Q/A pairs (csv), KB file (csv). A separately submitted documentation (doc, docx or pdf) for all the four tasks that also includes demo video URL for the both submissions.

It will need to be done on spyder ide. I have a chatbot created and you will need to expand on it