Build a Multimodal Agent for Product Ingredient Analysis

Unlock the Secrets of Product Ingredients with a Multimodal AI Agent! Tired of deciphering complex ingredient lists? This article shows you how to build a powerful Product Ingredient Analyzer using Gemini 2.0, Phidata, and Tavily Web Search. Say goodbye to time-consuming individual ingredient searches and hello to instant, actionable insights!

Key Learning Outcomes

This tutorial will guide you through:

• Designing a multimodal AI agent architecture leveraging Phidata and Gemini 2.0 for vision-language tasks.
• Integrating Tavily Web Search for enhanced context and information retrieval within your agent workflow.
• Building a Product Ingredient Analyzer Agent that expertly combines image processing and web search for detailed product analysis.
• Mastering the art of crafting effective system prompts and instructions to optimize agent performance in multimodal scenarios.
• Developing a user-friendly Streamlit UI for real-time image analysis, nutritional information, and personalized health recommendations.

This article is part of the Data Science Blogathon.

Table of Contents

• Understanding Multimodal Systems
• Real-World Multimodal Applications
• The Power of Multimodal Agents
• Constructing Your Product Ingredient Analyzer Agent
• Essential Links
• Conclusion
• Frequently Asked Questions

Understanding Multimodal Systems

Multimodal systems are designed to process and interpret diverse data types simultaneously – including text, images, audio, and video. Vision-language models like Gemini 2.0 Flash, GPT-4o, Claude Sonnet 3.5, and Pixtral-12B excel at recognizing the intricate relationships between these modalities, extracting valuable knowledge from complex inputs. This article focuses on vision-language models that analyze images and generate textual explanations. These systems seamlessly blend computer vision and natural language processing to interpret visual information based on user prompts.

Real-World Multimodal Applications

Multimodal systems are revolutionizing various industries:

• Finance: Instantly understand complex financial terms by simply taking a screenshot.
• E-commerce: Obtain detailed ingredient analysis and health insights by photographing product labels.
• Education: Gain simplified explanations of complex diagrams and concepts from textbooks.
• Healthcare: Receive clear explanations of medical reports and prescription labels.

The Power of Multimodal Agents

The shift towards multimodal agents represents a significant advancement in AI interaction. Here's why they're so effective:

• Simultaneous processing of visual and textual data leads to more precise and context-rich responses.
• Complex information is simplified, making it easily accessible to a wider audience.
• Users upload a single image for comprehensive analysis, eliminating the need for manual ingredient searches.
• Combining web search and image analysis delivers more complete and reliable insights.

Constructing Your Product Ingredient Analyzer Agent

Let's build the Product Ingredient Analysis Agent step-by-step:

Step 1: Setting Up Dependencies

We'll need:

• Gemini 2.0 Flash: For powerful multimodal processing.
• Tavily Search: For seamless web search integration.
• Phidata: To orchestrate the agent system and manage workflows.
• Streamlit: To create a user-friendly web application.

!pip install phidata google-generativeai tavily-python streamlit pillow

Step 2: API Setup and Configuration

Obtain API keys from:

• Gemini API key: https://www.php.cn/link/feac4a1c91eb74bfce13cb7c052c233b
• Tavily API key: https://www.php.cn/link/c73ff6dceadedf3652d678cd790ff167

from phi.agent import Agent
from phi.model.google import Gemini # needs a api key
from phi.tools.tavily import TavilyTools # also needs a api key

import os
TAVILY_API_KEY = "<replace-your-api-key>"
GOOGLE_API_KEY = "<replace-your-api-key>"
os.environ['TAVILY_API_KEY'] = TAVILY_API_KEY
os.environ['GOOGLE_API_KEY'] = GOOGLE_API_KEY</replace-your-api-key></replace-your-api-key>

Step 3: System Prompt and Instructions

Clear instructions are crucial for optimal LLM performance. We'll define the agent's role and responsibilities:

SYSTEM_PROMPT = """
You are an expert Food Product Analyst specialized in ingredient analysis and nutrition science. 
Your role is to analyze product ingredients, provide health insights, and identify potential concerns by combining ingredient analysis with scientific research. 
You utilize your nutritional knowledge and research works to provide evidence-based insights, making complex ingredient information accessible and actionable for users.
Return your response in Markdown format. 
"""

INSTRUCTIONS = """
* Read ingredient list from product image 
* Remember the user may not be educated about the product, break it down in simple words like explaining to 10 year kid
* Identify artificial additives and preservatives
* Check against major dietary restrictions (vegan, halal, kosher). Include this in response. 
* Rate nutritional value on scale of 1-5
* Highlight key health implications or concerns
* Suggest healthier alternatives if needed
* Provide brief evidence-based recommendations
* Use Search tool for getting context
"""

Step 4: Defining the Agent Object

The Phidata Agent is configured to process markdown and operate based on the system prompt and instructions. Gemini 2.0 Flash is used as the reasoning model, and Tavily Search is integrated for efficient web search.

agent = Agent(
    model = Gemini(),
    tools = [TavilyTools()],
    markdown=True,
    system_prompt = SYSTEM_PROMPT,
    instructions = INSTRUCTIONS
)

Step 5: Multimodal Image Processing

Provide the image path or URL, along with a prompt, to initiate analysis. Examples using both approaches are provided in the original article.

Step 6 & 7: Streamlit Web App Development (Detailed code in original article)

A Streamlit application is created to provide a user-friendly interface for image upload, analysis, and result display. The app includes tabs for example products, image uploads, and live photo capture. Image resizing and caching are implemented for optimal performance.

Essential Links

• Full code: [Insert GitHub link here]
• Deployed App: [Insert deployed app link here]

Conclusion

Multimodal AI agents are transforming how we interact with and understand complex information. The Product Ingredient Analyzer demonstrates the power of combining vision, language, and web search to provide accessible, actionable insights.

Frequently Asked Questions

• Q1. Open-Source Multimodal Vision-Language Models: LLaVA, Pixtral-12B, Multimodal-GPT, NVILA, and Qwen are examples.
• Q2. Is Llama 3 Multimodal?: Yes, Llama 3 and Llama 3.2 Vision models are multimodal.
• Q3. Multimodal LLM vs. Multimodal Agent: An LLM processes multimodal data; an agent uses LLMs and other tools to perform tasks and make decisions based on multimodal inputs.

Remember to replace the placeholders with your actual API keys. The complete code and deployed app links should be added for a complete and functional guide.

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