https://github.com/shaadclt/supplier-analysis-smolagents
This project provides tools for analyzing ice cream supplier data, calculating transportation costs, tariffs, and total procurement expenses. It leverages the power of smolagents to create intelligent agents that can perform complex calculations and answer natural language queries about supplier data.
https://github.com/shaadclt/supplier-analysis-smolagents
numpy pandas smolagents
Last synced: 5 months ago
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This project provides tools for analyzing ice cream supplier data, calculating transportation costs, tariffs, and total procurement expenses. It leverages the power of smolagents to create intelligent agents that can perform complex calculations and answer natural language queries about supplier data.
- Host: GitHub
- URL: https://github.com/shaadclt/supplier-analysis-smolagents
- Owner: shaadclt
- License: mit
- Created: 2025-04-28T06:00:49.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-04-28T06:16:59.000Z (7 months ago)
- Last Synced: 2025-05-13T13:56:18.525Z (6 months ago)
- Topics: numpy, pandas, smolagents
- Language: Jupyter Notebook
- Homepage:
- Size: 28.3 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Ice Cream Supplier Analysis using Smolagents🍦
This project demonstrates how to use **smolagents** to build a simple yet powerful agent that helps calculate transportation costs and tariffs for an ice cream supplier business.
## Overview
This project provides tools for analyzing ice cream supplier data, calculating transportation costs, tariffs, and total procurement expenses. It leverages the power of `smolagents` to create intelligent agents that can perform complex calculations and answer natural language queries about supplier data.
## About smolagents
This project showcases [smolagents](https://github.com/huggingface/smolagents), a powerful library that enables running sophisticated agents in just a few lines of code:
- ✨ **Simplicity**: The entire agent logic fits in ~1,000 lines of code with minimal abstractions
- 🧑💻 **First-class Code Agent support**: Unlike tools that use agents to write code, smolagents' CodeAgent writes its actions directly in code with secure execution in sandboxed environments (E2B or Docker)
- 🤗 **Hub integrations**: Share and pull tools or agents to/from the Hugging Face Hub
- 🌐 **Model-agnostic**: Works with any LLM (local transformers, ollama, Hub providers, OpenAI, Anthropic, and more via LiteLLM)
- 👁️ **Modality-agnostic**: Supports text, vision, video, and audio inputs
- 🛠️ **Tool-agnostic**: Compatible with tools from LangChain, MCP, or even Hub Spaces
## Features
- **Supplier Data Management**: Store and analyze supplier information including location, distance, pricing, and country of origin
- **Cost Calculation Tools**:
- Transportation cost calculation based on distance and volume
- Tariff calculation for international imports
- Total cost analysis including fixed fees
- **AI-Powered Analysis**: Uses smolagents with the Qwen2.5-72B-Instruct model for natural language interaction with your data
## Installation
```bash
# Clone the repository
git clone https://github.com/shaadclt/Supplier-Analysis-Smolagents.git
cd Supplier-Analysis-Smolagents
```
## Usage
### Basic Setup
```python
import pandas as pd
import numpy as np
from smolagents import tool, HfApiModel, CodeAgent
# Set up your HuggingFace API token
import os
os.environ['HF_API_KEY'] = "your_huggingface_token"
# Load your supplier data
suppliers_data = {
"name": ["Montreal Ice Cream Co", "Brain Freeze Brothers", "Toronto Gelato Ltd"],
"location": ["Montreal, QC", "Burlington, VT", "Toronto, ON"],
"distance_km": [120, 85, 400],
"canadian": [True, False, True],
"price_per_liter": [1.95, 1.91, 1.82],
"tasting_fee": [0, 12.50, 30.14]
}
suppliers_df = pd.DataFrame(suppliers_data)
```
### Creating Analysis Tools with smolagents
The power of smolagents lies in its simple yet powerful tool definition system:
```python
@tool
def calculate_transport_cost(distance_km: float, order_volume: float) -> float:
"""Calculate transportation cost based on distance and order size."""
trucks_needed = np.ceil(order_volume / 300)
cost_per_km = 1.20
return distance_km * cost_per_km * trucks_needed
@tool
def calculate_tariff(base_cost: float, is_canadian: bool) -> float:
"""Calculates tariff for Canadian imports."""
if is_canadian:
return base_cost * np.pi / 50
return 0
```
### Using the smolagents CodeAgent
```python
# Initialize a model using smolagents' model-agnostic approach
model = HfApiModel(
"Qwen/Qwen2.5-72B-Instruct",
provider="together",
max_tokens=4096,
temperature=0.1
)
# Create a CodeAgent that can use your custom tools
agent = CodeAgent(
model=model,
tools=[calculate_transport_cost, calculate_tariff],
max_steps=10,
additional_authorized_imports=["pandas", "numpy"],
verbosity_level=2
)
# Ask questions in natural language and get programmatic responses
response = agent.run("What is the transportation cost for 50 liters of ice cream over 10 kilometers?")
print(response)
```
## Advanced smolagents Features
This project demonstrates several key features of smolagents:
1. **Tool Composition**: Combine multiple tools to solve complex supply chain problems
2. **Secure Code Execution**: Run agent-generated code in a sandboxed environment
3. **Natural Language Interface**: Convert user questions into programmatic operations
4. **Model Flexibility**: Easily swap between different LLMs for your specific needs
## Requirements
- Python 3.8+
- pandas
- numpy
- smolagents
- Hugging Face API access
## Google Colab Support
This project includes a Google Colab notebook for easy exploration without local setup. The notebook demonstrates how to leverage smolagents to:
1. Load and analyze supplier data
2. Calculate transportation costs and tariffs
3. Use the CodeAgent to answer natural language questions
4. Visualize cost comparisons between suppliers
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE.txt) file for details.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.