app.py

import gradio as gr
from huggingface_hub import InferenceClient
import os
import traceback

# Styling
fancy_css = """
#main-container {
    background-color: #f0f0f0;
    font-family: 'Arial', sans-serif;
}
.gradio-container {
    max-width: 700px;
    margin: 0 auto;
    padding: 20px;
    background: white;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
    border-radius: 10px;
}
.gr-button {
    background-color: #4CAF50;
    color: white;
    border: none;
    border-radius: 5px;
    padding: 10px 20px;
    cursor: pointer;
    transition: background-color 0.3s ease;
}
.gr-button:hover {
    background-color: #45a049;
}
.gr-chat {
    font-size: 16px;
}
#title {
    text-align: center;
    font-size: 2em;
    margin-bottom: 20px;
    color: #333;
}
"""

# System prompt specialized for chord bot
CHORD_SYSTEM_PROMPT = """You are a music theory expert specialized in chord identification.
Given a list of notes (like "C E G" or "D F# A C"), identify the chord name.
Always respond with the chord name and a short explanation of the intervals.
"""

# Hugging Face API token (in Space secrets)
HF_TOKEN = os.environ.get("HF_TOKEN")

# Inference client
client = InferenceClient(token=HF_TOKEN)

def respond(message, history, system_message, max_tokens, temperature, top_p):
    if HF_TOKEN is None:
        return "⚠️ No HF_TOKEN found. Please add it in your Space secrets."

    try:
        # Build a simple prompt
        prompt = f"{system_message}\nUser: {message}\nAnswer:"

        # Raw POST to Hugging Face Inference API
        output = client.post_json(
            "https://api-inference.huggingface.co/models/gpt2",  # ✅ change model here if needed
            {
                "inputs": prompt,
                "parameters": {
                    "max_new_tokens": max_tokens,
                    "temperature": temperature,
                    "top_p": top_p,
                },
            },
        )

        # Parse response
        if isinstance(output, list) and len(output) > 0 and "generated_text" in output[0]:
            response = output[0]["generated_text"]
        else:
            response = str(output)

        return response.strip()

    except Exception as e:
        tb = traceback.format_exc()
        return f"⚠️ Error: {str(e)}\n\nTraceback:\n{tb}"


# Gradio ChatInterface
chatbot = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Textbox(value=CHORD_SYSTEM_PROMPT, label="System message"),
        gr.Slider(minimum=1, maximum=512, value=128, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
    ],
    type="messages",
)

# Layout
with gr.Blocks(css=fancy_css) as demo:
    gr.Markdown("<h1 id='title'>🎶 Chord Bot (API-based) 🎶</h1>")
    chatbot.render()

if __name__ == "__main__":
    demo.launch()