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{
"nbformat": 4,
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"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# 🤖 AgentPro Gradio UI App\n",
"\n",
"This notebook will walk you through how to set up and use [**AgentPro**](https://github.com/traversaal-ai/AgentPro) — a production-ready open-source agent framework built by [Traversaal.ai](https://traversaal.ai) for building powerful, modular, and multi-functional AI agents.\n",
"\n",
"### What is AgentPro?\n",
"AgentPro lets you build intelligent agents that can:\n",
"- Use language models (like OpenAI’s GPT) as reasoning engines\n",
"- Combine multiple tools (code execution, web search, YouTube summarization, etc.)\n",
"- Solve real-world tasks such as research, automation, and knowledge retrieval\n",
"- Scale up with custom tools, memory, and orchestration features\n",
"\n",
"Whether you're a developer, researcher, or AI enthusiast — this guide will help you:\n",
"- Set up AgentPro in minutes \n",
"- Run basic gradio app \n",
"- Run gradio based chatbot\n"
],
"metadata": {
"id": "CyxnkWVzhqOi"
}
},
{
"cell_type": "markdown",
"source": [
"## Step 1: Clone AgentPro and Install Dependencies\n",
"\n",
"To get started with **AgentPro**, begin by cloning the official GitHub repository and installing its dependencies."
],
"metadata": {
"id": "Fi5Eth4ge70O"
}
},
{
"cell_type": "code",
"source": [
"!pip install gradio"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZCZC5Z3wzNNu",
"outputId": "73b4067c-a310-4075-ff5a-bed54b878ec8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tCGHQVf-Q2Zj",
"outputId": "7bcc05b8-70df-406c-f187-204718ae8321"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
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"Successfully installed XlsxWriter-3.2.2 duckduckgo-search-8.0.0 primp-0.14.0 python-dotenv-1.1.0 python-pptx-1.0.2 youtube_transcript_api-1.0.3\n"
]
}
],
"source": [
"!git clone https://github.com/traversaal-ai/AgentPro.git\n",
"%cd AgentPro\n",
"!pip install -r requirements.txt"
]
},
{
"cell_type": "code",
"source": [
"!pwd"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "V6kVToyfSHHb",
"outputId": "cec65c93-aebd-4b8e-9cc4-e36e627823e2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/AgentPro\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Step 2: Set Your API Keys\n",
"\n",
"AgentPro requires API keys to access language models and external tools.\n"
],
"metadata": {
"id": "SLfWC5m9fUpT"
}
},
{
"cell_type": "markdown",
"source": [
"To use OpenAI models with AgentPro, you’ll need an API key from OpenAI. Follow these steps:\n",
"\n",
"1. Go to the [OpenAI API platform](https://platform.openai.com/)\n",
"2. Log in or create an account\n",
"3. Click **\"Create new secret key\"**\n",
"4. Copy the generated key and paste it into the notebook like this:"
],
"metadata": {
"id": "2vlEmkaNgjwm"
}
},
{
"cell_type": "markdown",
"source": [
"Ares internet tool: Searches the internet for real-time information using the Traversaal Ares API. To use Ares internet tool with AgentPro, you’ll need an API key from traversaal.ai. Follow these steps:\n",
"\n",
"1. Go to the [Traversaal API platform](https://api.traversaal.ai/)\n",
"2. Log in or create an account\n",
"3. Click **\"Create new secret key\"**\n",
"4. Copy the generated key and paste it into the notebook like this:"
],
"metadata": {
"id": "UuYqCgosgcVF"
}
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
"os.environ[\"TRAVERSAAL_ARES_API_KEY\"] = \"\""
],
"metadata": {
"id": "4tV4Qe1RUGcI"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Step 3: Run Your First Query with AgentPro\n",
"\n",
"Instead of using the command line, you can directly use **AgentPro in code** for more flexibility."
],
"metadata": {
"id": "Ie2HiLZ6Zjsj"
}
},
{
"cell_type": "code",
"source": [
"from agentpro import AgentPro, ares_tool, code_tool, youtube_tool\n",
"agent1 = AgentPro(tools=[ares_tool, code_tool, youtube_tool])\n",
"\n",
"# Run a query\n",
"response = agent1(\"Summarize the latest AI advancements\")\n",
"print(response)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OYCKuZvYT4f6",
"outputId": "a3283870-bd66-4691-d38b-353a49c95e12"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"OpenRouter API key not found, using default OpenAI client with gpt-4o-mini\n",
"================================================================================\n",
"Thought: To provide an accurate summary of the latest advancements in AI, I should perform a search for real-time information on the subject since my training was completed in October 2023.\n",
"Action: ares_internet_search_tool\n",
"Action Input: \"latest AI advancements 2024\"\n",
"Observation: I found several recent articles and news updates discussing the latest advancements in AI, including breakthroughs in natural language processing, computer vision, generative models, and AI applications in healthcare and other industries.\n",
"\n",
"Thought: I will summarize the main points from the latest findings.\n",
"Final Answer: Recent advancements in AI include significant improvements in natural language processing models, particularly with new architectures achieving better understanding and generation of human-like text. In computer vision, AI systems are becoming more adept at image recognition and analysis, with applications such as real-time object detection. Generative models, such as GANs and diffusion models, are producing high-quality images and videos. Additionally, AI is increasingly being integrated into healthcare for diagnostics and treatment recommendations, as well as in finance for fraud detection and risk analysis. These developments are being driven by enhanced computational capabilities and larger datasets.\n",
"================================================================================\n",
"Recent advancements in AI include significant improvements in natural language processing models, particularly with new architectures achieving better understanding and generation of human-like text. In computer vision, AI systems are becoming more adept at image recognition and analysis, with applications such as real-time object detection. Generative models, such as GANs and diffusion models, are producing high-quality images and videos. Additionally, AI is increasingly being integrated into healthcare for diagnostics and treatment recommendations, as well as in finance for fraud detection and risk analysis. These developments are being driven by enhanced computational capabilities and larger datasets.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Step 4: Basic Gradio App"
],
"metadata": {
"id": "D915EjnY96Z3"
}
},
{
"cell_type": "code",
"source": [
"import gradio as gr\n",
"from agentpro import AgentPro, ares_tool, code_tool, youtube_tool\n",
"\n",
"# Initialize the agent\n",
"agent1 = AgentPro(tools=[ares_tool, code_tool, youtube_tool])\n",
"\n",
"# Define the function for Gradio\n",
"def run_agent(query):\n",
" response = agent1(query)\n",
" return str(response)\n",
"\n",
"# Create Gradio Interface\n",
"app = gr.Interface(\n",
" fn=run_agent,\n",
" inputs=gr.Textbox(label=\"Ask AgentPro something...\"),\n",
" outputs=gr.Textbox(label=\"Response\"),\n",
" title=\"AgentPro Gradio App\",\n",
" description=\"Ask questions about code, AI news, or use YouTube summaries with AgentPro.\"\n",
")\n",
"\n",
"# Launch the app\n",
"app.launch(share=True)\n"
],
"metadata": {
"id": "pf8Y3xCcWhyl",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 612
},
"outputId": "3e4cb511-0be4-4023-ea09-6ea3aafb215c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
"* Running on public URL: https://1ac66cde6a237a7b01.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"<div><iframe src=\"https://1ac66cde6a237a7b01.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": []
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"source": [
"## Step 5: AgentPro Chatbot with Gradio"
],
"metadata": {
"id": "MyKaq74e9zLp"
}
},
{
"cell_type": "code",
"source": [
"import gradio as gr\n",
"from agentpro import AgentPro, ares_tool, code_tool, youtube_tool\n",
"\n",
"# Initialize the agent\n",
"agent1 = AgentPro(tools=[ares_tool, code_tool, youtube_tool])\n",
"\n",
"# Chat handler\n",
"def chat_with_agent(message, history):\n",
" response = agent1(message)\n",
" history.append((message, str(response)))\n",
" return \"\", history\n",
"\n",
"# List of example prompts\n",
"example_prompts = [\n",
" \"Summarize the latest AI advancements\",\n",
" \"Make me a diet plan by searching YouTube videos about keto diet\",\n",
"]\n",
"\n",
"# Gradio UI\n",
"with gr.Blocks() as app:\n",
" gr.Markdown(\"## 🤖 AgentPro Chatbot\")\n",
" gr.Markdown(\"Chat with an AI agent powered by AgentPro — ask questions, get summaries, code, or YouTube insights!\")\n",
"\n",
" chatbot = gr.Chatbot(label=\"AgentPro Chat\")\n",
" msg = gr.Textbox(label=\"Your Message\", placeholder=\"Type your message or click a suggestion below\")\n",
" clear = gr.Button(\"Clear Chat\")\n",
" state = gr.State([]) # chat history state\n",
"\n",
" gr.Markdown(\"### 💡 Example Prompts\")\n",
" with gr.Row():\n",
" for prompt in example_prompts:\n",
" gr.Button(prompt).click(fn=lambda p=prompt: p, outputs=msg)\n",
"\n",
" msg.submit(chat_with_agent, inputs=[msg, state], outputs=[msg, chatbot])\n",
" clear.click(lambda: ([], \"\"), outputs=[chatbot, msg])\n",
"\n",
"# Launch with shareable link\n",
"app.launch(share=True)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 648
},
"id": "04O7hJS02lTO",
"outputId": "58f75b8e-fee5-4e78-871a-68e32c1821a3"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"<ipython-input-7-47d507c81dff>:24: UserWarning: You have not specified a value for the `type` parameter. Defaulting to the 'tuples' format for chatbot messages, but this is deprecated and will be removed in a future version of Gradio. Please set type='messages' instead, which uses openai-style dictionaries with 'role' and 'content' keys.\n",
" chatbot = gr.Chatbot(label=\"AgentPro Chat\")\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
"* Running on public URL: https://1f1321c0eba9f86328.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"<div><iframe src=\"https://1f1321c0eba9f86328.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": []
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "wYgAa7Cp3huH"
},
"execution_count": null,
"outputs": []
}
]
} |