Update app.py

app.py

@@ -1,111 +1,30 @@
 import gradio as gr
-import os
-import traceback
-import requests
-
-# 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 (from Space secrets)
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-# API URL – using GPT-2 as demo, can be swapped for another model
-API_URL = "https://api-inference.huggingface.co/models/distilgpt2"
-
-
-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 prompt
-        prompt = f"{system_message}\nUser: {message}\nAnswer:"
-
-        headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-        payload = {
-            "inputs": prompt,
-            "parameters": {
-                "max_new_tokens": max_tokens,
-                "temperature": temperature,
-                "top_p": top_p,
-            },
-        }
-
-        # Call Hugging Face Inference API
-        response = requests.post(API_URL, headers=headers, json=payload)
-
-        if response.status_code != 200:
-            return f"⚠️ API Error {response.status_code}: {response.text}"
-
-        data = response.json()
-
-        # Extract generated text
-        if isinstance(data, list) and len(data) > 0 and "generated_text" in data[0]:
-            return data[0]["generated_text"].strip()
-        else:
-            return str(data)
-
-    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",
+from chord_identifier import identify_chord
+from huggingface_hub import InferenceClient
+
+# Load Hugging Face API client (example: flan-t5-small)
+client = InferenceClient("google/flan-t5-small")
+
+def predict_chord(notes: str):
+    # Split comma-separated input
+    note_list = [n.strip() for n in notes.split(",")]
+
+    # Try rule-based
+    chord = identify_chord(note_list)
+    if chord != "Unknown Chord":
+        return chord
+
+    # Fall back to LLM for reasoning
+    prompt = f"Identify the musical chord made of notes: {', '.join(note_list)}"
+    response = client.text_generation(prompt, max_new_tokens=20)
+    return response.strip()
+
+demo = gr.Interface(
+    fn=predict_chord,
+    inputs=gr.Textbox(lines=1, placeholder="Enter notes, e.g., C,E,G"),
+    outputs="text",
+    title="Chord Identifier (API-based)"
 )
 
-# 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()