A workflow demonstrating how the Image Analysis Agent can process and analyze visual content, detect objects, and generate detailed descriptions.

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Quick Start

1

Install Package

First, install the PraisonAI Agents package:

pip install praisonaiagents
2

Set API Key

Set your OpenAI API key as an environment variable:

export OPENAI_API_KEY=your_api_key_here
3

Create Script

Create a new file image_analysis.py:

from praisonaiagents import Agent, Task, PraisonAIAgents

# Create Image Analysis Agent
image_agent = Agent(
    name="ImageAnalyst",
    role="Image Analysis Specialist",
    goal="Analyze images and videos to extract meaningful information",
    backstory="""You are an expert in computer vision and image analysis.
    You excel at describing images, detecting objects, and understanding visual content.""",
    llm="gpt-4o-mini",
    self_reflect=False
)

# Create tasks for different types of analysis
task1 = Task(
    name="analyze_landmark",
    description="Describe this famous landmark and its architectural features.",
    expected_output="Detailed description of the landmark's architecture and significance",
    agent=image_agent,
    images=["https://example.com/landmark.jpg"]
)

task2 = Task(
    name="analyze_local_image",
    description="What objects can you see in this image? Describe their arrangement.",
    expected_output="Detailed description of objects and their spatial relationships",
    agent=image_agent,
    images=["local_image.jpg"]
)

# Create PraisonAIAgents instance
agents = PraisonAIAgents(
    agents=[image_agent],
    tasks=[task1, task2],
    process="sequential",
    verbose=1
)

# Run analysis
agents.start()

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Understanding Image Analysis

The Image Analysis Agent is designed to process and understand visual content through several key capabilities:

  1. Image Input: Supports both URL and local file inputs
  2. Object Detection: Identifies and locates objects within images
  3. Architectural Analysis: Specialized in analyzing architectural features
  4. Spatial Understanding: Describes spatial relationships between objects

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Features

Multiple Input Sources

Support for both URL and local image files.

Object Detection

Advanced object detection and recognition.

Architectural Analysis

Specialized analysis of architectural features.

Detailed Descriptions

Generation of comprehensive visual descriptions.

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Example Usage

# Example: Analyzing a landmark image
landmark_task = Task(
    name="analyze_famous_landmark",
    description="Analyze this historical landmark",
    expected_output="Architectural details and historical significance",
    agent=image_agent,
    images=["https://example.com/landmark.jpg"]
)

# Run single task
agents = PraisonAIAgents(
    agents=[image_agent],
    tasks=[landmark_task],
    process="sequential"
)
agents.start()

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Next Steps

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