Building BloomBot: A Comprehensive Guide to Creating an AI-Powered Pregnancy Companion Using Gemini API

Solution approach for BloomBot

1. Problem Definition and Goals

Objective:

• Develop BloomBot, an AI-powered chatbot tailored for expecting mothers to provide:
  • Pregnancy tips
  • Nutrition advice by week
  • Emotional support resources
  • A conversational interface for queries

Key Requirements:

• AI-Powered Chat: Leverage Gemini for generative responses.
• User Interface: Interactive and user-friendly chatbot interface.
• Customization: Adapt responses based on pregnancy stages.
• Scalability: Handle concurrent user interactions efficiently.

---

2. Architecture Overview

Key Components:

1. Frontend:

   • Tool: Tkinter for desktop GUI.
   • Features: Buttons, dropdowns, text areas for interaction.

2. Backend:

   • Role: Acts as a bridge between the frontend and Gemini API.
   • Tech Stack: Python with google.generativeai for Gemini API integration.

3. Gemini API:

   • Purpose: Generate responses for user inputs.
   • Capabilities Used: Content generation, chat handling.

4. Environment Configuration:

   • Secure API key storage using .env file and dotenv.

---

3. Solution Workflow

Frontend Interaction:

• Users interact with BloomBot via a Tkinter-based GUI:
  • Buttons for specific tasks (e.g., pregnancy tips, nutrition advice).
  • A dropdown for selecting pregnancy weeks.
  • A text area for displaying bot responses.

Backend Processing:

1. Task-Specific Prompts:
   • Predefined prompts for tasks like fetching pregnancy tips or emotional support.
   • Dynamic prompts (e.g., week-specific nutrition advice).
2. Free-Form Queries:
   • Use the chat feature of Gemini to handle user inputs dynamically.
3. Response Handling:
   • Parse and return Gemini's response to the frontend.

Gemini API Integration:

• Models Used: gemini-1.5-flash.
• API methods like generate_content for static prompts and start_chat for conversational queries.

---

4. Implementation Details

Backend Implementation

Key Features:

1. Pregnancy Tip Generator:
   • Prompt: "Give me a helpful tip for expecting mothers."
   • Method: generate_content.
2. Week-Specific Nutrition Advice:
   • Dynamic prompt: "Provide nutrition advice for week {week} of pregnancy."
   • Method: generate_content.
3. Emotional Support Resources:
   • Prompt: "What resources are available for emotional support for expecting mothers?"
   • Method: generate_content.
4. Chat Handler:
   • Start a conversation: start_chat.
   • Handle free-form queries.

Code Snippet:

class ExpectingMotherBotBackend:
    def __init__(self, api_key):
        self.api_key = api_key
        genai.configure(api_key=self.api_key)
        self.model = genai.GenerativeModel("models/gemini-1.5-flash")
    
    def get_pregnancy_tip(self):
        prompt = "Give me a helpful tip for expecting mothers."
        result = self.model.generate_content(prompt)
        return result.text if result.text else "Sorry, I couldn't fetch a tip right now."

    def get_nutrition_advice(self, week):
        prompt = f"Provide nutrition advice for week {week} of pregnancy."
        result = self.model.generate_content(prompt)
        return result.text if result.text else "I couldn't fetch nutrition advice at the moment."

    def get_emotional_support(self):
        prompt = "What resources are available for emotional support for expecting mothers?"
        result = self.model.generate_content(prompt)
        return result.text if result.text else "I'm having trouble fetching emotional support resources."

    def chat_with_bot(self, user_input):
        chat = self.model.start_chat()
        response = chat.send_message(user_input)
        return response.text if response.text else "I'm here to help, but I didn't understand your query."

---

Frontend Implementation

Key Features:

1. Buttons and Inputs:
   • Fetch pregnancy tips, nutrition advice, or emotional support.
2. Text Area:
   • Display bot responses with a scrollable interface.
3. Dropdown:
   • Select pregnancy week for tailored nutrition advice.

Code Snippet:

class ExpectingMotherBotFrontend:
    def __init__(self, backend):
        self.backend = backend
        self.window = tk.Tk()
        self.window.title("BloomBot: Pregnancy Companion")
        self.window.geometry("500x650")
        self.create_widgets()

    def create_widgets(self):
        title_label = tk.Label(self.window, text="BloomBot: Your Pregnancy Companion")
        title_label.pack()

        # Buttons for functionalities
        tip_button = tk.Button(self.window, text="Get Daily Pregnancy Tip", command=self.show_pregnancy_tip)
        tip_button.pack()
        
        self.week_dropdown = ttk.Combobox(self.window, values=[str(i) for i in range(1, 51)], state="readonly")
        self.week_dropdown.pack()
        nutrition_button = tk.Button(self.window, text="Get Nutrition Advice", command=self.show_nutrition_advice)
        nutrition_button.pack()

        support_button = tk.Button(self.window, text="Emotional Support", command=self.show_emotional_support)
        support_button.pack()

        self.response_text = tk.Text(self.window)
        self.response_text.pack()

    def show_pregnancy_tip(self):
        tip = self.backend.get_pregnancy_tip()
        self.display_response(tip)

    def show_nutrition_advice(self):
        week = self.week_dropdown.get()
        advice = self.backend.get_nutrition_advice(int(week))
        self.display_response(advice)

    def show_emotional_support(self):
        support = self.backend.get_emotional_support()
        self.display_response(support)

    def display_response(self, response):
        self.response_text.delete(1.0, tk.END)
        self.response_text.insert(tk.END, response)

---

5. Deployment

Steps:

1. Environment Setup:
   • Install required packages: pip install tkinter requests google-generativeai python-dotenv.
   • Set up .env with the Gemini API key.
2. Testing:
• Ensure prompt-response functionality works as expected.
• Test UI interactions and Gemini API responses.

6. Monitoring and Maintenance

• Usage Analytics: Track interactions for feature improvements.
• Error Handling: Implement better fallback mechanisms for API failures.
• Feedback Loop: Regularly update prompts based on user feedback.

Evaluating the success of an AI&ML use case

              Data science team has finished development of the current version of the ML model & has reported an accuracy or error metric. But you are not sure how to put that number in context. Whether that number is good or not good enough.

                In one of my previous blogs, I have addressed the issue of AI investment and how long before the business can know if the engagement has some potential or it is not going anywhere. This blog can be considered an extension of the above-mentioned blog. If you haven’t checked it out already, please visit : https://anirbandutta-ideasforgood.blogspot.com/2023/07/investment-on-developing-ai-models.html

                In my previous blog, I spoke about the KPIs like Accuracy and Error as thumb-rules to quickly assess on the potential success of the use case. In this blog, I will try to add more specificity or relativeness to it.

                Fundamentally, to evaluate the latest performance KPI of your AI&ML model, there are 3 ways you can go about it, in independence or in combination.

Consider human level performance metric.

For AI use cases which has the primary objective of replacing human effort, this can be considered the primary success metric. For example, if for a particular process the current human error stands at 5%, and the AI can have less or equal to 5% error rate, it can be determined a valuable model. Because AI with the same error rate, bring along with it - smart automation, speeding up the process, negligible down-time etc.

Example: Tasks which needs data entry can easily be replicated by AI. But the success criteria for AI does not need to be 100% accuracy for adoption, but just must match the accuracy which the human counterpart was delivering, to be adopted for real word deployment.

Base Model metric

In use cases for which problem areas getting addressed are more theoretical in nature, or the discovery of the business problem that can get addressed is in progress, its best to create a quick simple base model and then try to improve the model with each iteration.

For example: Currently I am working on a system to determine if a content is created by AI or not. For the lack of any past reference based to which the accuracy can be compared, I have taken this approach to determine the progress.

Satisfying & optimizing metric

We outline both, a metric that we want the model to do as good as possible (we call this optimizing metric) while also meeting some minimum standard which makes it functional and valuable in real life scenarios (we call this satisfying metric)

Example: For Home Voice Assistant, the optimizing metric would be the accuracy of a model hearing exactly what someone said. The satisfying metric would be that the model does not take more than 100 ms to process what was said.

AI TRUST & ADOPTION – THE METRICS TO MONITOR

 

Trust is critical to AI adoption. With more deployment of next generation of AI models, building trust on these systems becomes even more vital and difficult. For example, although with the amazing capabilities Generative AI, LLMs are delivering, it brings along with it the trouble of it being larger, complex, and opaque than ever. This makes identification of the right metrics and continuously monitoring and reporting them imperative.

Below are some of the most critical metrics that every organization & business should be continuously monitoring and have the capability to report them as and when necessary.

DATA

•       Date of instances

•       Date processed.

•       Owner & steward

•       Who created it?

•       Who funded it?

•       Who’s the intended user?

•       Who’s accountable?

•       What do instances (i.e., rows) represent?

•       How many instances are there?

•       Is it all of them or was it sampled?

•       How was it sampled?

•       How was it collected?

•       Are there any internal or external keys?

•       Are there target variables?

•       Descriptive statistics and distributions of important and sensitive variables

•       How often is it updated?

•       How long are old instances retained?

•       Applicable regulations (e.g., HIPAA)

 

MODELS

•       Date trained.

•       Owner & steward

•       Who created it?

•       Who funded it?

•       Who’s the intended user?

•       Who’s accountable?

•       What do instances (i.e., rows) represent?

•       What does it predict?

•       Features

•       Description of its training & validation data sets

•       Performance metrics

•       When was it trained?

•       How often is it retrained?

•       How long are old versions retained?

•       Ethical and regulatory considerations

 

BIAS remains one of the most difficult KPI to define & measure. Hence, I am excited to find some measures which can contribute measuring presence of BIAS in some format.

•          Demographic representation: Does a dataset have the same distribution of sensitive subgroups as the target population?
•          Equality of opportunity: Like equalized odds, but only checks the true positive rate.
•          Average odds difference: The difference between the false positive and true positive
•          Demographic parity: Are model prediction averages about the same overall and for sensitive subgroups? For example, if we’re predicting the likelihood to pay a phone bill on time, does it predict about the same pay rate for men and women? A t-test, Wilcoxon test, or bootstrap test could be used.
•          Equalized odds: For Boolean classifiers that predict true or false, are the true positive and false positive rates about the same for sensitive subgroups? For example, is it more accurate for young adults than for the elderly?
•          Average odds difference: The difference between the false positive and true positive
•          Odds ratio: Positive outcome rate divided by the negative outcome rate. For example, (likelihood that men pay their bill on time) / (likelihood that men don’t pay their bill on time) compared to that for women.
•          Disparate impact: Ratio of the favorable prediction rate for a sensitive subgroup to that of the overall population.
•          Predictive rate parity: Is model accuracy about the same for different sensitive subgroups? Accuracy can be measured by things such as precision, F-score, AUC, mean squared error, etc.

But considering all the above, we must be very sensitive and be cognizant of the business & social context while identifying our above mentioned “sensitive group.”

By no means, it is a exhaustive list, but only a start towards a safer & fairer digital ecosystem. I will try my best to consolidate new information.

 

Thanking dataiku, some information collected from dataiku report: How to build trustworthy AI systems.

Investment on developing AI&ML models – timelines & diminishing return

 

One of the most popular questions that I often get asked by the stakeholders is about the timelines required for a ML model to finish development. I will try to address the subtlety of this topic in this writeup.

AI development is a unique scenario where you are expected to deliver an innovation. It is a special case where the resource required is uncertain. And hence it is sometimes very difficult to understand when & where to “STOP”.

When I talk to businesses one of the questions, what I stress about the most, is for them to define what an “MVP” solution is to them. That is with what minimum accuracy or maximum error rate the AI solution would still be useful for their business.

If you are investing on AI use cases one of the concepts, I would recommend you understand is – AI resourcing & diminishing return. Please look at the graph below –

 

            So, what I suggest to the AI investors are if you haven’t reached an MVP by the point of maximum return, “STOP”. For example – By the end of PoMR if the model is still with an error rate of 30%, and that is something that does not work for your business, may be AI cannot solve this for you. Maybe it needs a completely different approach to solve this. Whatever is the case, deploying more resource is not the solution.

            Driving from my experience with all the AI&ML use cases I have worked for almost a decade now, a general thumb rule which I recommend is – The accuracy or error rate, that you get at the end of 3 months is your Point of maximum return. You should reach an MVP by then. Beyond that it should be fine tuning or customizing to specific business needs. By then if it it’s still miles apart from your business objective, may be its time to pull the plug.

            This is again an opinion piece, and these has been my experience. Will be glad to hear how the journey has been for you.

AI, ML, Data Science - frequent consultation advises to leadership

 In this current post I have tried to compile most questions, discussions and queries I come across while consulting Data Science road maps with leaders and managers. I am hoping this compilation will add value to the other leaders and managers too who may have at some point wondered about them but didn’t get the opportunity to have those discussions. Many of you may have a better response or more exposure to some of the questions, I have tried to compile it based on the best knowledge I have and how I go about explaining them.

Please reach out, if you think you have a question or point that appears a lot during consultation and is worth discussed upon.

1. Should we build this product or capability in-house or get it from a vendor?

A vendor product will always be a generalized one to cut across as many businesses as possible as they thrive on repeat-ability. While when you build in-house you can make it more customized to a smaller set of use cases or scenarios and may be create a better differentiation.

So please ask yourself –

· When partnering with a vendor what role do I play? What stops the vendor from partnering with the business directly in future? What is my value addition, is there any risk I may become insignificant.

· What kind of team I have? If you have a great engineering team may be you want to do more stuffs in-house and keep a bigger piece of pie for yourself.

· What is my core capability? Is the capability needed in line with our core skill or is it something we want to learn, then maybe we should do it in house, or it is something we just want to get done, then may be the best way is to get a vendor involved.

2. We have created certain Analytics use cases. But we see several other teams also creating similar use cases.

Differentiation of analytics product or use cases are driven by each and combination of below –

a) Deep domain knowledge

b) Combination of data from different systems brought together on a dynamic big data system

c) Deep or mature algorithm applied

If your use cases are easy to replicate it’s most probably on a shallow data, with very general domain knowledge applied with basic Data Science techniques.

3. Are we using the kind of AI that is used for technologies like Self Driving car?

Yes and No. Internally all these technologies uses combinations of neural net and reinforcement learning. We also for different use cases have used variation of same and similar technologies. But technologies like self-driving car works on image or vision data, which we generally don’t do. Our use cases are mostly based on numerical, text and language processing data.

4. Vendor says their product is being used by Amazon. So should we go ahead and buy it?

May be it is being used by Amazon or any similarly big companies, but ask the vendor if their product is being used for a mission critical process or for some PoC or to store or process data like click-stream data which is not business critical. This makes all the difference, if the logos vendors show you are using the vendors technology for business critical projects or some non-critical processes.

5. We are showing the use case to the business but it’s not making much of an impact.

Story telling needs to be improved. Every analytics use case must be associated with a story that should end with the business making or saving money. If the story cannot relate how the engineering will improve the customer’s financial bottom-line, the customer business does not care about it, irrespective of how good the engineering is.

6. Now we have a data scientist in our team. Can we now expect more insights from our data?

Data Scientists alone cannot ensure project success. Data Engineers, Big Data and Cloud Infra Engineers are equally important part of the technical team. Without the infrastructure in place and data being stored in the infra in proper format, Data Scientists cannot do his or her magic.

7. We are finding very difficult to hire data scientists and big data developers.

Though there is no dearth of CVs, finding genuinely talented people with actual knowledge and production implementation knowledge is difficult. And among the few, most are already paid well and on good projects. So whenever a decision is taken to hire senior data science talents, a 6 month time frame should be kept in hand.

8. What is the difference between ML and AI?

Though you will find several answers to this in the internet one good way I have found to explain it to a business person, without the jargons is as below. By definition ML comes within the broader scope of AI. But to understand better and remember, Ai is something that is built to replicate human behavior. A program is called a successful AI when it can pass a Turing Test. A system is said to pass a Turing test when we cannot differentiate the intelligence coming from a machine and a human. ML is a system which you create to find pattern in a big data set that is too big for a human brain to comprehend. On a lighter note – if you ask a machine 1231*1156 and it answers it in a fraction of a second it is ML and if it pauses, makes some comment and answers after 5 mins, like a human, it is AI.

9. Why aren’t we using a big data Hadoop architecture but using RDBMS like MSSQL. Oracle.

RDBMS products like MSSQL, Oracle are still viable analytics products and are not replaceable by Big Data tools for many scenarios. Deciding on a data store or a data processing engine involves a lot of factors like ACID-BASE properties, type and size of data, current implementation, skill set of the technical team etc. So doing an analytics project does not make Hadoop or NoSQL product default.

10. Here is some data, give me some insight.

This is the first line of any failed initiative. A project which is not clear about the business problem it wants to solve is sure to fail. Starting on an analytics project without a clear goal in mind and for the sake of just adding a data science project to the portfolio and no road-map how this will eventually contribute to company goal, is a waste of resource and will only end in failure.

AI/ML and human brain - Similarities and their implication in Corporate and Political campaigns

 The premise of this article is how our brain is similar to a machine, and the biases and errors the machine experiences are experienced by the brain too, and in some cases this may be leveraged to drive success at a marketing or electoral campaign.

1. The good orator - No election has ever been won without a good orator at the helm. However good your policies are at the end of the day it needs to be sold. A good orator is like quality data to a machine. To drive learning in a machine, if we even want to propagate biases in some way, it needs to be clearly and strategically delivered. This delivery of data, to steer the mind and machine in a certain way, have also been called Propaganda in warfare.

2. The 360 degree delivery – To drive a target candidate to take a certain action, stimulus must be provided from every direction. It is widely accepted the same objective can be achieved with much less effort with stimulus from multiple dimension, rather than larger one-dimensional effort. It is the same reason we come across our favorite brand across multiple platforms like TV, radio, newspaper, social media, online ads etc. According to a successful marketing platform ‘personalized messaging across email, SMS, direct mail, and more, alongside personalized online response’ leads to a much successful marketing campaign. AI systems inspired by this characteristic of the brain is always advised to be built around with data from as many diverse source systems as possible.

3. What is in it for me - Human beings by the very nature of their existence and survival instinct mostly react to news and events that directly affect their well-being. The same idea is implemented in reinforcement learning where the agent takes measure to fulfill objective which in our case is survival. So to make your target audience to take notice of any policy or idea, it should be narrated as tightly coupled with the audiences’ well-being. It should answer their basic question how it will affect me.

4. Relative rather than absolute – Human brains intuitively understand something relative much better than anything absolute. If you ask most people if a deal is good, they will generally say it is good or bad based on how other people are getting deals. In the same way you can manipulate a machine to label something on a particular high or low range, by strategically infusing data on the other end of the scale. In the same way during a political or marketing campaign it is not enough to advertise your positives but also important to emphasize your opponents weaknesses.

5. Confirmation bias – Definition - “Confirmation bias occurs from the direct influence of desire on beliefs. When people would like a certain idea or concept to be true, they end up believing it to be true. They are motivated by wishful thinking. This error leads the individual to stop gathering information when the evidence gathered so far confirms the views or prejudices one would like to be true.” This is a psychological error that can always be used to one’s advantage during a political or marketing campaign. Political and Corporate organizations have at various times taken advantages of this by implementing a biased belief system at an early age of life or consumption cycle.

6. ML bias – Machine Learning applications develop inherent biases when fed data which may be tilted towards a certain stereotypical trend due to the flawed nature in which society develops. Like real-world example of a machine learning model that’s designed to differentiate between men and women in pictures. When the training data contains more pictures of women in kitchens than men in kitchens, or more pictures of men writing computer code than women writing computer code, then algorithm is trained to make incorrect inferences about the gender of people engaged in those activities. Human brain can be manipulated in the same way too. If you give enough example to a brain, associating people of certain characteristic to certain nature of actions – either good or bad, human brains inherently start associating these people to those activities without extensive thought.

But however amazing our brain is, it still have certain flaws which have been inherited by ML and AI processes as these are inspired by the brain itself. But I guess all these imperfections are what keeps us human.