---
title: MLOps AI Agent
emoji: ♻️
colorFrom: indigo
colorTo: green
sdk: gradio
sdk_version: 6.0.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: Analyse csv, train, deploy, and get endpoint API.
tags:
  - mcp-in-action-track-enterprise

---

# 🧠 MLOps Agent: Auto Analysis, Training & Deployment

A fully automated AI powered MLOps agent that you can run in a browser:
Upload a CSV → Auto-analyze → Train a model → Deploy → Get example inference code.

This app uses:

* **Gradio** (streaming UI)
* **OpenAI Responses API** (`gpt-5-mini` / `gpt-5`)
* **Remote MCP server** for tool calling
* **CSV→Model→Deployment** pipeline
* **Streaming chat UX** with collapsible technical details
* **Automatic Python & curl example code generation**

>Link to the LinkedIn post: https://www.linkedin.com/feed/update/urn:li:ugcPost:7399748605563277312/

>Link to the YouTube video: https://www.youtube.com/watch?v=dzVgoG1uk0M

## 🚀 Features

### 📋 File Size Limits

* **Hard cap**: 10MB maximum file size
* **Soft cap**: 1.5MB recommended for optimal performance
* **Large file handling**: If a file exceeds the soft cap, training will automatically use only the top 10K rows for processing
* This optimization speeds up the training process and returns model IDs and responses faster

### 1. Natural Chat Interface

Chat like a normal assistant — ask questions, discuss data, clarify tasks.

### 2. Automatic CSV Analysis

Upload any structured CSV and ask:

> “Analyze this dataset”

The agent will produce:

* Data shape
* Target detection
* Basic stats
* Data quality issues
* Recommendations

### 3. Model Training

Ask:

> “Train a model on this dataset”

The agent will automatically:

* Train a classifier or regressor
* Evaluate metrics (Accuracy, F1, ROC-AUC…)
* Summarize key performance signals

### 4. Automated Deployment

Ask:

> “Deploy the model”

The MCP backend handles deployment and returns:

* Deployment status
* Example Python inference code
* Example curl usage

### 5. Clean, Structured Responses

Every response includes:

**Key Summary → Example Usage → Technical Details (collapsible)**

* Main explanation in clear Markdown
* Python + curl examples outside the collapsible
* Tools, parameters, and logs inside a `<details>` block

### 6. Full Streaming Output

All assistant messages stream token-by-token for fast UX.



## 🖥️ UI Overview

### Chat + File Upload

* Drag and drop a CSV
* Type queries (analysis, training, deployment, follow-ups)
* Live streaming assistant responses

### Collapsible Technical Details

The agent always displays a `<details>` block containing:

* Tool names used
* Parameters passed
* Training logs
* Deployment logs
* Raw JSON (if available)

This keeps the main summary clean while still exposing full transparency.

### Clickable Examples

* Pre-loaded examples for quick testing
* Triggers analysis, training, deployment, and auto-deployment
* Uses the included `data/heart.csv` and `data/housing.csv` dataset automatically



## 📦 Installation

Clone the repo:

```bash
git clone https://huggingface.co/spaces/MCP-1st-Birthday/mlops-agent
cd mlops-agent
```

Install dependencies:

```bash
pip install -r requirements.txt
```

Set your environment variable:

```bash
export OPENAI_API_KEY="your-key-here"
```

Run the app:

```bash
python app.py
```




## 🧬 Architecture

```
User → Gradio Chat UI
      → OpenAI Responses API (streaming)
          → MCP Server (tools: analyze, train, deploy)
              → Outputs come back as structured signals
→ Assistant formats them using strict markdown schema
→ Gradio displays stream + collapsible logs
```


## 🛠️ Tech Stack

* Python 3.10+
* Gradio 6.x
* OpenAI SDK
* Remote MCP Tool Server (auto-train, evaluate, deploy)
* Streaming I/O architecture