README.md

metadata

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, 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.

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⚡ Features

• 🔥 LLM-powered form extraction using Agent abstraction from pydantic_ai
• 🎙️ Voice chat with real-time streaming via 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

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🚀 Getting Started

1. Clone the Repo

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).

uv pip install -r requirements.txt

Or use uv directly:

uv venv
source .venv/bin/activate
uv pip install -r requirements.txt

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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:

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.

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4. Run the App Locally

python app.py

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🌐 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.

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🧪 Debug Tips

To simulate extraction alone:

python agents.py

Then type messages in the CLI to test how well the form is filled.

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📁 File Structure

├── 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

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🧠 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.

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🛑 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.

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🙏 Credits

• Built using pydantic_ai
• Voice support via fastrtc
• LLM backend: Llama 3.3 70B via Groq
• UI powered by Gradio

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❤️ Made with care by Rikhil