README1.md

---
title: Chatbot
emoji: 💻
colorFrom: yellow
colorTo: green
sdk: gradio
sdk_version: 6.6.0
app_file: app.py
pinned: false
short_description: Chatbot
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

# Multi-User Memory Chatbot with LangChain, Tools, and Gradio

A conversational AI chatbot built with **LangChain**, **Gradio**, and multiple custom tools.  
This project supports:

- **multi-user chat sessions**
- **multiple threads per user**
- **memory-aware conversations**
- **tool calling**
- optional **tool activity display in the UI**

It is useful for experimenting with AI agents that can search, calculate, scrape websites, fetch weather, read files, and more.

---

## Features

- 👤 **Multiple users**
- 🧵 **Multiple chat threads per user**
- 🧠 **Conversation memory using `thread_id` and `user_id`**
- 🛠 **Tool-enabled chatbot**
- 🌐 **Web search using Tavily**
- 📚 **Wikipedia search**
- ☁️ **Weather lookup**
- 🧮 **Calculator**
- 🐍 **Python code execution**
- 📄 **Read local files**
- 🔎 **Scrape webpage text**
- 🧾 **Pretty-print JSON**
- 💾 **Save user preferences**
- 🖥 **System info tool**
- 🎨 **Gradio web interface**
- ✅ Optional **tool activity panel** to show which tools were called

---

## Project Structure

A recommended project structure:

```text
project/
│
├── tools.py               # All tool definitions
├── memory_chatbot.py      # Chatbot / LangChain agent backend
├── app.py                 # Gradio UI
├── .env                   # Environment variables
├── requirements.txt       # Python dependencies
└── README.md
```

> **Important:**  
> If your UI file imports `chatbot` like this:

```python
from memory_chatbot import chatbot
```

then your UI file should **not** also be named `memory_chatbot.py`, otherwise you may get a **circular import error**.  
Use a separate file name such as `app.py` for the Gradio interface.

---

## Tools Included

The project currently includes the following tools:

### 1. `time_date`
Returns today’s date in `yyyy-mm-dd` format.

### 2. `calculator`
Evaluates mathematical expressions.

### 3. `python_exec`
Executes Python code and returns local variables.

### 4. `get_weather`
Fetches current weather for a city using `wttr.in`.

### 5. `wikipedia_search`
Searches Wikipedia and returns a short summary.

### 6. `scrape_website`
Extracts text content from a webpage.

### 7. `read_file`
Reads the contents of a local file.

### 8. `format_json`
Formats and prettifies a JSON string.

### 9. `generate_sql`
Converts natural language into a placeholder SQL query.

### 10. `system_info`
Returns system/platform information.

### 11. `save_user_preference`
Stores user preference text (currently mocked).

### 12. `tool_tavily`
Performs web search using Tavily.

---

## Installation

## 1. Clone the repository

```bash
git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name
```

## 2. Create a virtual environment

### Windows

```bash
python -m venv venv
venv\Scripts\activate
```

### macOS / Linux

```bash
python3 -m venv venv
source venv/bin/activate
```

## 3. Install dependencies

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

If you do not already have a `requirements.txt`, you can use something like this:

```txt
gradio
langchain
langchain-core
langchain-tavily
python-dotenv
requests
wikipedia
beautifulsoup4
```

Depending on your chatbot backend, you may also need your model provider package, such as:

- `langchain-openai`
- `langchain-google-genai`
- `langchain-groq`
- or another LLM integration

---

## Environment Variables

Create a `.env` file in the root directory.

Example:

```env
TAVILY_API_KEY=your_tavily_api_key
OPENAI_API_KEY=your_openai_api_key
```

If you are using another model provider, replace `OPENAI_API_KEY` with the correct key for your setup.

---

## Running the App

Start the Gradio interface with:

```bash
python app.py
```

Then open the local Gradio URL shown in the terminal, usually:

```text
http://127.0.0.1:7860
```

---

## How It Works

### User and Thread Management

Each conversation is identified using:

- `user_id`
- `thread_id`

This allows the chatbot to maintain separate memory for:

- different users
- different chat sessions for the same user

### Chat Flow

When the user sends a message:

1. the message is wrapped as a `HumanMessage`
2. the chatbot is invoked with `thread_id` and `user_id`
3. the agent may call one or more tools
4. the final AI response is displayed in the chat UI
5. the conversation is stored under that user/thread pair

---

## Gradio UI Features

The Gradio app includes:

- **User selector**
- **New user button**
- **Thread selector**
- **New chat button**
- **Chat area**
- **Message input box**

This makes it easy to simulate multiple users and sessions in one interface.

---

## Showing Tool Calls in the UI

Yes — this project can be extended to show:

- which tool was called
- tool arguments
- tool output

For example, if the chatbot uses `get_weather("Delhi")`, the UI can display something like:

```text
Calling tool: get_weather
Arguments: {"city": "Delhi"}
Output: Delhi: +34°C, Sunny
```

This is useful for:

- debugging
- understanding agent behavior
- teaching/demo purposes

A good way to display this is by adding a `gr.Markdown()` or `gr.Textbox()` panel in the UI and parsing:

- `AIMessage.tool_calls`
- `ToolMessage.content`

---

## Example Use Cases

- Ask today’s date
- Calculate expressions like `25 * 12`
- Execute small Python snippets
- Look up weather in a city
- Search Wikipedia topics
- Scrape a webpage summary
- Read a local file
- Format JSON data
- Save user preferences

---

## Example Prompts

Try asking:

```text
What is today's date?
```

```text
Calculate 125 * 48
```

```text
What is the weather in London?
```

```text
Search Wikipedia for Artificial Intelligence
```

```text
Read the contents of sample.txt
```

```text
Format this JSON: {"name":"John","age":25}
```

---

## Security Warning

This project includes some tools that are **unsafe for public deployment** without restrictions.

### Risky tools

- `calculator` uses `eval()`
- `python_exec` uses `exec()`
- `read_file` can access local files
- `scrape_website` can fetch arbitrary URLs

### Why this matters

If exposed publicly, users may be able to:

- run arbitrary Python code
- read sensitive local files
- access internal resources
- misuse the server

### Recommendation

Use this project:

- for **local development**
- for **learning**
- for **internal testing only**

Before deploying publicly, you should:

- remove `eval()` and `exec()`
- restrict file access
- validate URLs
- sandbox execution
- add authentication and rate limiting

Tiny chatbot, big chaos potential. Better to keep the chaos leashed.

---

## Known Notes

### 1. Circular Import Risk

If your UI file is named `memory_chatbot.py` and also contains:

```python
from memory_chatbot import chatbot
```

you will likely get an import issue.

### Fix

Rename the Gradio UI file to something like:

```text
app.py
```

and keep the chatbot backend in:

```text
memory_chatbot.py
```

---

### 2. Tool Results May Differ

Some tools depend on external services:

- `wttr.in`
- `Wikipedia`
- `Tavily`
- websites being scraped

If those services are unavailable, the tool may fail or return unexpected results.

---

## Future Improvements

Possible enhancements for this project:

- real memory store integration
- persistent database for chat history
- streaming responses
- live tool-call updates in the UI
- better error handling
- authentication
- safer code execution sandbox
- SQL generation using LLM
- file upload support
- improved styling for the chat interface

---

## Sample `tools.py`

This file contains all your custom tools used by the chatbot agent.

It includes tools for:

- date/time
- calculator
- Python execution
- weather
- Wikipedia
- web scraping
- file reading
- JSON formatting
- SQL generation
- system information
- user preference saving
- Tavily search

---

## Sample Workflow

1. Start app
2. Select or create a user
3. Create a new thread
4. Ask a question
5. Agent decides whether to use tools
6. Tool result is returned
7. Final AI answer is shown
8. Conversation history is stored per user/thread

---

## Requirements Summary

Main libraries used:

- `gradio`
- `langchain`
- `langchain-core`
- `langchain-tavily`
- `requests`
- `wikipedia`
- `beautifulsoup4`
- `python-dotenv`

---

## License

This project is for educational and development use.  
Add your preferred license here, for example:

```text
MIT License
```

---

## Author

Built with LangChain, Gradio, and a healthy amount of agent curiosity.

If you want, you can customize this section with your name, GitHub profile, or portfolio link.

---

## Contributing

Contributions are welcome.

You can improve this project by:

- adding safer tool implementations
- improving UI design
- adding persistent memory
- improving tool logging
- integrating streaming
- adding tests

---

## Final Notes

This project is a great starting point if you want to learn:

- LangChain tools
- agent-based workflows
- memory-aware chat apps
- multi-thread chat management
- Gradio interfaces

If you are planning to deploy it publicly, please review the security concerns carefully before doing so.