Google Gen AI 5-days Intensive Course - Capstone Project - AI Food Agent by Haszeli

🍽️ NakMakanApa – A Generative AI Companion for Personalized Meal Discovery

🧠 Introduction

Every Malaysian has faced the timeless question: “Nak makan apa?” ("What do I want to eat?"). Whether you're a busy parent, a health-conscious professional, or someone simply staring at a fridge of random ingredients, deciding what to eat can be surprisingly stressful.

NakMakanApa is a Generative AI-powered food recommendation system that turns that daily dilemma into an intelligent, personalized experience. Built as part of the GenAI Intensive Course Capstone Project, this solution blends natural language understanding, image recognition, and AI-driven reasoning to guide users toward delicious, healthy, and culturally relevant meals.

🌟 What Makes NakMakanApa Special?

This isn’t just a recipe finder. NakMakanApa is a full-featured AI agent capable of:

• 🗣️ Understanding user prompts like “saya nak makanan pedas dan sihat untuk jantung” (I want something spicy and heart-healthy)

• 🖼️ Interpreting images of fridge contents to identify usable ingredients

• 🧠 Personalizing meal suggestions based on preferences, health goals, and local cuisine

• 📦 Fetching recipes via vector search and fallback to Gemini LLM if needed

• 🧾 Generating structured summaries and advice about the meal’s health benefits

• 📄 Exporting everything as a clean PDF or TXT document

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🔧 How It Works – The Process

The project unfolds in 8 major steps:

1. Prepare a structured recipe dataset as a DataFrame (df_recipes)

2. Install and configure required libraries (LangChain, FAISS, Gemini SDK, YOLOv8, etc.)

3. Allow image input – users can upload a fridge/ingredient photo

4. Detect ingredients via YOLOv8 and match them with user preferences (multilingual supported!)

5. Search local FAISS vector index for best recipe matches

6. Fallback to Gemini if no good match is found, auto-updating the dataset & vector DB

7. Summarize recipe & highlight health benefits, giving users a clear reason for the recommendation

8. Generate PDF or TXT output, with beautiful formatting, for sharing or saving offline

Example of the screen on kaggle

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💡 GenAI Capabilities Applied

Capability	Application
📊 Structured Output / JSON Mode	Recipes returned in structured JSON: title, ingredients, steps, tags
🧠 Image Understanding	YOLOv8 used to detect food from photos
📚 Retrieval-Augmented Generation (RAG)	Blend of FAISS vector search with Gemini fallback
🔍 Vector Store	Efficient similarity search on recipes using semantic embeddings
🤖 Agents (Bonus)	The system behaves like an AI agent orchestrating input parsing, generation, and summarization seamlessly

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🧪 Results & Achievements

✅ Users can receive personalized meal suggestions from both text and image input
✅ Recipes include clear, AI-generated summaries explaining their health value
✅ The system handles English and Malay prompts with ease
✅ Users can export results to PDF, making meal planning effortless
✅ Every process includes robust error handling for reliability

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🚀 What’s Next?

This project is just the beginning. Future enhancements include:

• 📱 A mobile app version for everyday usage

• 🧮 Nutritional analysis (calories, macros, allergens)

• 🛒 Smart shopping list generation

• 🌏 Expansion to Middle Eastern and global cuisines

• 👩‍🍳 Community recipes and feedback-driven learning

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👨‍🎓 Final Thoughts

NakMakanApa is more than a technical showcase—it’s a vision for how Generative AI can enrich daily life in culturally meaningful and health-conscious ways. By combining NLP, computer vision, RAG, and AI reasoning into a seamless user flow, we created an experience that feels human, helpful, and very Malaysian.

This project proves that AI can do more than automate tasks—it can guide, support, and inspire better living.

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Food Discovery AI Agent

 # 🍽️ Food Discovery AI Agent (Malaysian/Asian Cuisine)

This project is a **proof-of-concept AI agent** that recommends **what to eat** and **where to go** based on:

- ✅ User preferences and dietary restrictions  

- 🕒 Time of day (e.g., lunch, dinner)  

- 🌦️ Real-time weather at your current location  

- 📍 Your destination or current location  

- 🧠 LLM-powered reasoning (OpenAI GPT-3.5)  

### 💡 How It Works

1. **User Input**: Collects dietary preferences, time, and destination

2. **Weather Fetch**: Pulls real-time weather via OpenWeather API

3. **Place Discovery**: Uses Google Places API to find nearby restaurants or hawkers

4. **LLM Reasoning**: GPT analyzes the context and filters/summarizes results

5. **Visual Map**: Recommended spots are plotted on an interactive map

6. **Export**: CSV file download available

7. **Summary Report**: Optional AI-written recap of the session

### 🔐 API Keys Required

- OpenWeatherMap

- Google Places

- OpenAI (for summary)

Built with ❤️ for Malaysian/Asian food lovers 🍜🇲🇾  

The code is accessible at https://www.kaggle.com/code/nurulhaszeliahmad/agentfoodiet

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3 Pillars of Leading in the Age of AI - My Personal View

 

Introduction

In an era of rapid technological advancement, technical expertise alone is insufficient for effective leadership. The most successful tech leaders of the future will be those who master three essential pillars: AI-driven decision-making, systems thinking, and human-centric leadership. From my personal experience, reading, and observations, I believe these three principles are essential for effective leadership in today’s advanced technology environment. Here’s why these three pillars matter — and how you can apply them to future-proof your leadership.

Pillar 1: AI-Driven Decision Making

From Data to Insight

AI is more than just a tool — it’s a game-changer for decision-making. By leveraging AI to analyze project performance, customer sentiment, and team dynamics, leaders can make data-driven decisions with greater accuracy and speed. For example, during a major corporate merger, you can use sentiment analysis tools to track employee morale across teams. The data revealed early warning signs of disengagement, allowing us to intervene before it escalated into a productivity crisis. Source

Ethical AI: Augmenting, Not Replacing, Judgment

AI should enhance human decision-making, not replace it. As tech leaders, we must ask: Does this tool amplify human intelligence or override it? Ethical AI adoption means ensuring transparency, fairness, and accountability in how we deploy these technologies. Source

Pillar 2: Systems Thinking

Zoom Out, Then Zoom In

Tech leaders must balance big-picture vision with attention to detail. A systems-thinking approach ensures that solutions align with business objectives while remaining adaptable. For example, while developing a healthcare app, we could start by mapping the end-to-end user journey before reverse-engineering the tech stack. This approach ensured a seamless user experience while optimizing backend efficiency. Source

Resilience by Design

Modern architecture must be adaptive and resilient. A single point of failure can jeopardize an entire operation, so designing for scalability and flexibility is crucial. Case Study: An e-commerce platform experienced sudden traffic spikes during peak sales events, such as Black Friday. By implementing AI-driven auto-scaling and leveraging microservices architecture, we achieved 99.99% uptime, even during unexpected surges. This approach not only ensured seamless performance but also optimized resource utilization and reduced operational costs. Source

Pillar 3: Human-Centric Leadership

Bridging the Soft Skills Gap

Technical failures are rarely the primary reason projects go off course. According to a report, 70% of project failures stem from poor communication, misalignment, and team disconnects — not technical shortcomings. Source

The Approach: “No-Agenda” Check-ins

Leadership is about more than managing tasks — it’s about understanding people. One of the most effective strategies has been hosting weekly “no-agenda” check-ins. These informal meetings allow team members to bring up concerns before they become blockers, fostering a culture of trust and open communication. Source

Conclusion: The Future Belongs to Adaptive Leaders

To stay ahead in the age of AI, leaders must strike the right balance between technical acumen and human intuition. The most impactful leaders will be those who can seamlessly integrate AI-driven insights, systems-level thinking, and people-first leadership. 

What’s your non-negotiable leadership principle? Let’s discuss!

#TechLeadership #AI #SystemsThinking #ProjectManagement #FutureOfWork

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Will AI replace the Project Manager?

Introduction

Artificial intelligence is transforming industries at an unprecedented pace, and project management is no exception. Yet, amid all the talk of automation and digital transformation, a common fear emerges: Will AI replace another role, the project manager?

The reality is quite the opposite. AI is not here to take over but to enhance human capabilities, making project managers more effective, strategic, and valuable than ever before. By automating repetitive tasks, improving decision-making, and mitigating risks, AI allows project managers to focus on what truly matters—leadership, innovation, and value creation.

In this article, I’ll share how AI has revolutionized my project management approach and why the future belongs to PMs who embrace it.

AI-PM Partnership: A Game-Changer for Efficiency

Automation ≠ Replacement

AI excels at handling repetitive, time-consuming tasks such as scheduling, data entry, and progress tracking. But rather than replacing human intuition and leadership, AI acts as a force multiplier, allowing project managers to focus on strategy, stakeholder alignment, and team motivation.

Example: AI-powered tools like ClickUp and Monday.com now analyze historical data and team performance to predict project delays. This foresight helps project managers proactively address potential bottlenecks rather than react to crises.

Risk Mitigation: Seeing Problems Before They Arise

One of AI’s most powerful contributions to project management is its ability to identify and mitigate risks before they escalate. Machine learning algorithms can analyze vast amounts of data to detect patterns that humans might overlook, helping teams make informed decisions.

Case in Point: During a recent cloud migration project, we integrated an AI-driven risk assessment tool. The system identified a 92% chance of cost overruns due to scope creep—weeks before the issue would have surfaced. This early warning allowed us to recalibrate scope and budget, ultimately preventing financial losses and ensuring a smooth transition.

Real-World Case Study: AI in Action

The Challenge

A client approached us with an ambitious goal: launching a new product in just four months instead of the planned six. Given the compressed timeline, efficient resource management and rapid decision-making were critical.

How AI Transformed the Project

1. Resource Allocation Optimization
2. Automated Reporting & Insights

The Outcome

✅ Projects delivered on time.

✅ 25% budget surplus due to optimized resource allocation.

✅ Higher team morale with reduced administrative burden

How to Start Leveraging AI in Your Projects

The good news? You don’t need a PhD in AI to start incorporating these tools into your workflow. Here are three simple steps to begin:

1. Audit Your Workflow
2. Experiment with AI-powered tools
3. Upskill Your Team

Conclusion: AI Is Your Co-Pilot, Not Your Replacement

AI isn’t here to take your job—it’s here to make you an unstoppable project manager. By embracing AI-driven insights, automation, and predictive capabilities, you can make smarter decisions, deliver projects more efficiently, and drive greater impact for your teams and stakeholders.

Your Turn: How are you integrating AI into your project management workflow? What’s your biggest challenge in adopting AI? Let’s discuss this in the comments!

#AI #ProjectManagement #FutureOfWork #TechLeadership #Innovation

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