# 🔍🤖 Vision Agent [](https://discord.gg/wPdN8RCYew)  [](https://badge.fury.io/py/vision-agent) 

Vision Agent is a library that helps you utilize agent frameworks to generate code to solve your vision task. Many current vision problems can easily take hours or days to solve, you need to find the right model, figure out how to use it and program it to accomplish the task you want. Vision Agent aims to provide an in-seconds experience by allowing users to describe their problem in text and have the agent framework generate code to solve the task for them. Check out our discord for updates and roadmaps!

Web Application

Try Vision Agent live on (note this may not be running the most up-to-date version) va.landing.ai

Documentation

Vision Agent Library Docs

Getting Started

Installation

To get started, you can install the library using pip:

bash pip install vision-agent

Ensure you have an OpenAI API key and set it as an environment variable (if you are using Azure OpenAI please see the Azure setup section):

bash export OPENAI_API_KEY="your-api-key"

Vision Agent

There are two agents that you can use. VisionAgent is a conversational agent that has access to tools that allow it to write an navigate python code and file systems. It can converse with the user in natural language. VisionAgentCoder is an agent specifically for writing code for vision tasks, such as counting people in an image. However, it cannot chat with you and can only respond with code. VisionAgent can call VisionAgentCoder to write vision code.

Basic Usage

To run the streamlit app locally to chat with VisionAgent, you can run the following command:

bash pip install -r examples/chat/requirements.txt export WORKSPACE=/path/to/your/workspace export ZMQ_PORT=5555 streamlit run examples/chat/app.py You can find more details about the streamlit app here.

Basic Programmatic Usage

```python

from visionagent.agent import VisionAgent agent = VisionAgent() resp = agent("Hello") print(resp) [{"role": "user", "content": "Hello"}, {"role": "assistant", "content": "{'thoughts': 'The user has greeted me. I will respond with a greeting and ask how I can assist them.', 'response': 'Hello! How can I assist you today?', 'letuser_respond': True}"}] resp.append({"role": "user", "content": "Can you count the number of people in this image?", "media": ["people.jpg"]}) resp = agent(resp) ```

Vision Agent Coder

Basic Usage

You can interact with the agent as you would with any LLM or LMM model:

```python

from vision_agent.agent import VisionAgentCoder agent = VisionAgentCoder() code = agent("What percentage of the area of the jar is filled with coffee beans?", media="jar.jpg") ```

Which produces the following code: ```python from visionagent.tools import loadimage, grounding_sam

def calculatefilledpercentage(imagepath: str) -> float: # Step 1: Load the image image = loadimage(image_path)

# Step 2: Segment the jar
jar_segments = grounding_sam(prompt="jar", image=image)

# Step 3: Segment the coffee beans
coffee_beans_segments = grounding_sam(prompt="coffee beans", image=image)

# Step 4: Calculate the area of the segmented jar
jar_area = 0
for segment in jar_segments:
    jar_area += segment['mask'].sum()

# Step 5: Calculate the area of the segmented coffee beans
coffee_beans_area = 0
for segment in coffee_beans_segments:
    coffee_beans_area += segment['mask'].sum()

# Step 6: Compute the percentage of the jar area that is filled with coffee beans
if jar_area == 0:
    return 0.0  # To avoid division by zero
filled_percentage = (coffee_beans_area / jar_area) * 100

# Step 7: Return the computed percentage
return filled_percentage

```

To better understand how the model came up with it's answer, you can run it in debug mode by passing in the verbose argument:

```python

agent = VisionAgentCoder(verbosity=2) ```

Detailed Usage

You can also have it return more information by calling chat_with_workflow. The format of the input is a list of dictionaries with the keys role, content, and media:

```python

results = agent.chatwithworkflow([{"role": "user", "content": "What percentage of the area of the jar is filled with coffee beans?", "media": ["jar.jpg"]}]) print(results) { "code": "from visionagent.tools import ..." "test": "calculatefilledpercentage('jar.jpg')", "testresult": "...", "plan": [{"code": "...", "test": "...", "plan": "..."}, ...], "working_memory": ..., } ```

With this you can examine more detailed information such as the testing code, testing results, plan or working memory it used to complete the task.

Multi-turn conversations

You can have multi-turn conversations with vision-agent as well, giving it feedback on the code and having it update. You just need to add the code as a response from the assistant:

python agent = va.agent.VisionAgentCoder(verbosity=2) conv = [ { "role": "user", "content": "Are these workers wearing safety gear? Output only a True or False value.", "media": ["workers.png"], } ] result = agent.chat_with_workflow(conv) code = result["code"] conv.append({"role": "assistant", "content": code}) conv.append( { "role": "user", "content": "Can you also return the number of workers wearing safety gear?", } ) result = agent.chat_with_workflow(conv)

Tools

There are a variety of tools for the model or the user to use. Some are executed locally while others are hosted for you. You can easily access them yourself, for example if you want to run owl_v2 and visualize the output you can run:

```python import vision_agent.tools as T import matplotlib.pyplot as plt

image = T.loadimage("dogs.jpg") dets = T.owlv2("dogs", image) viz = T.overlayboundingboxes(image, dets) plt.imshow(viz) plt.show() ```

You can also add custom tools to the agent:

```python import vision_agent as va import numpy as np

@va.tools.registertool(imports=["import numpy as np"]) def customtool(image_path: str) -> str: """My custom tool documentation.

Parameters:
    image_path (str): The path to the image.

Returns:
    str: The result of the tool.

Example
-------
>>> custom_tool("image.jpg")
"""

return np.zeros((10, 10))

```

You need to ensure you call @va.tools.register_tool with any imports it uses. Global variables will not be captured by register_tool so you need to include them in the function. Make sure the documentation is in the same format above with description, Parameters:, Returns:, and Example\n-------. You can find an example use case here as this is what the agent uses to pick and use the tool.

Can't find the tool you need and want add it to VisionAgent? Check out our vision-agent-tools repository where we add the source code for all the tools used in VisionAgent.

Additional Backends

Ollama

We also provide a VisionAgentCoder that uses Ollama. To get started you must download a few models:

bash ollama pull llama3.1 ollama pull mxbai-embed-large

llama3.1 is used for the OllamaLMM for OllamaVisionAgentCoder. Normally we would use an actual LMM such as llava but llava cannot handle the long context lengths required by the agent. Since llama3.1 cannot handle images you may see some performance degredation. mxbai-embed-large is the embedding model used to look up tools. You can use it just like you would use VisionAgentCoder:

```python

import vision_agent as va agent = va.agent.OllamaVisionAgentCoder() agent("Count the apples in the image", media="apples.jpg") ``` WARNING: VisionAgent doesn't work well unless the underlying LMM is sufficiently powerful. Do not expect good results or even working code with smaller models like Llama 3.1 8B.

Azure OpenAI

We also provide a AzureVisionAgentCoder that uses Azure OpenAI models. To get started follow the Azure Setup section below. You can use it just like you would use= VisionAgentCoder:

```python

import vision_agent as va agent = va.agent.AzureVisionAgentCoder() agent("Count the apples in the image", media="apples.jpg") ```

Azure Setup

If you want to use Azure OpenAI models, you need to have two OpenAI model deployments:

1. OpenAI GPT-4o model
2. OpenAI text embedding model

Then you can set the following environment variables:

```bash export AZUREOPENAIAPIKEY="your-api-key" export AZUREOPENAI_ENDPOINT="your-endpoint"

The deployment name of your Azure OpenAI chat model

export AZUREOPENAICHATMODELDEPLOYMENTNAME="yourgpt4omodeldeployment_name"

The deployment name of your Azure OpenAI text embedding model

export AZUREOPENAIEMBEDDINGMODELDEPLOYMENTNAME="yourembeddingmodeldeployment_name" ```

NOTE: make sure your Azure model deployment have enough quota (token per minute) to support it. The default value 8000TPM is not enough.

You can then run Vision Agent using the Azure OpenAI models:

python import vision_agent as va agent = va.agent.AzureVisionAgentCoder()

Q&A

How to get started with OpenAI API credits

1. Visit the OpenAI API platform to sign up for an API key.
2. Follow the instructions to purchase and manage your API credits.
3. Ensure your API key is correctly configured in your project settings.

Failure to have sufficient API credits may result in limited or no functionality for the features that rely on the OpenAI API. For more details on managing your API usage and credits, please refer to the OpenAI API documentation.