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title: Customer_Support_Agent
app_file: app.py
sdk: gradio
sdk_version: 5.23.3
---
# 💬 Customer Support Assistant
An AI-powered voice + text chatbot built with [`pydantic_ai`](https://github.com/roboflow/pydantic-ai), powered by Llama 3.3 70B via Cerebras/Groq. It processes customer issues, detects emotional tone, and records support requests into a structured data table. Deployable on Gradio Spaces and usable locally with both text and audio input.
---
## ⚡ Features
- 🔥 **LLM-powered form extraction** using `Agent` abstraction from `pydantic_ai`
- 🎙️ **Voice chat** with real-time streaming via [`fastrtc`](https://github.com/Rikhil-Rai/fastrtc)
- 🧠 **Memory-aware responses** using `message_history`
- 📊 **Live DataFrame updates** for structured customer requests
- 💾 **Persistent CSV logging**
- 🛠️ One-click Gradio UI with tabs for Chat + Customer Data
---
## 🚀 Getting Started
### 1. Clone the Repo
```bash
git clone https://github.com/your-username/customer-support-assistant
cd customer-support-assistant
```
### 2. Install Dependencies
> ⚠️ Make sure you use `uv` to ensure proper dependency resolution (especially for `pydantic_ai`).
```bash
uv pip install -r requirements.txt
```
> Or use `uv` directly:
```bash
uv venv
source .venv/bin/activate
uv pip install -r requirements.txt
```
---
### 3. Setup `.env` File
Create a `.env` file in the root directory.
Refer to `settings.py` for required fields. At a minimum, you will need:
```env
CEREBRAS_API_KEY=your_api_key
CEREBRAS_BASE_URL=https://api.groq.com/openai/v1
```
You can optionally set environment variables for your own STT/TTS models as required by `fastrtc`.
---
### 4. Run the App Locally
```bash
python app.py
```
---
## 🌐 Deploy on Gradio Spaces
1. Add your `CEREBRAS_API_KEY` and `CEREBRAS_BASE_URL` as secrets in the Gradio Space.
2. Make sure `fastrtc` audio support is configured in the hardware tab.
3. Gradio Spaces will auto-launch the app via `app.py`.
---
## 🧪 Debug Tips
To simulate extraction alone:
```bash
python agents.py
```
Then type messages in the CLI to test how well the form is filled.
---
## 📁 File Structure
```plaintext
├── app.py # Main Gradio UI
├── agents.py # Agent config + LLM interaction logic
├── settings.py # Handles env/config
├── form_prompt.txt # System prompt for form extraction
├── response_prompt.txt # System prompt for response generation
├── data.csv # Auto-generated CSV storage
├── requirements.txt
├── README.md
```
---
## 🧠 What the AI Extracts
The form agent will pull out:
- `customername`: Extracted from input or marked as `"unknown"`
- `requesttype`: e.g., `"billing"`, `"technical support"`
- `issue`: 50-line description of the issue
- `emotion`: `"angry"`, `"happy"`, etc.
---
## 🛑 Known Limitations
- Longform audio may require silence-based segmentation tuning.
- This project assumes inputs are customer support related. General queries may misfire.
- Error handling is basic — add guards if scaling for production use.
---
## 🙏 Credits
- Built using [pydantic_ai](https://github.com/roboflow/pydantic-ai)
- Voice support via [fastrtc](https://github.com/Rikhil-Rai/fastrtc)
- LLM backend: Llama 3.3 70B via [Groq](https://groq.com/)
- UI powered by [Gradio](https://gradio.app/)
---
## ❤️ Made with care by Rikhil
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