using prof template

app.py

@@ -1,41 +1,100 @@
 import gradio as gr
-import requests
-import os
+from huggingface_hub import InferenceClient
 
-# Hugging Face Inference API endpoint (supported free model)
-API_URL = "https://api-inference.huggingface.co/models/t5-small"
+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;
+}
+"""
 
-# Use your HF token (store in Space secrets as HF_TOKEN)
-HF_TOKEN = os.environ.get("HF_TOKEN")
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+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.
+"""
 
-def chord_api(message: str, history: list[tuple[str, str]]):
-    """
-    Calls Hugging Face Inference API (t5-small) to classify chords.
-    """
-    prompt = f"What chord do these notes form? {message}"
-    response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
+def respond(
+    message,
+    history: list[dict[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    hf_token: gr.OAuthToken,
+):
+    if hf_token is None or not getattr(hf_token, "token", None):
+        yield "⚠️ Please log in with your Hugging Face account first."
+        return
 
-    if response.status_code == 200:
-        try:
-            return response.json()[0]["generated_text"]
-        except Exception:
-            return f"⚠️ Unexpected response: {response.text}"
+    client = InferenceClient(
+        token=hf_token.token,
+        model="google/flan-t5-small",  # change if you want to call your own model repo
+    )
 
-    elif response.status_code == 503:
-        return "⏳ Model is loading, please try again in a moment."
+    # Build structured messages → turn into a prompt
+    messages = [{"role": "system", "content": system_message}]
+    messages.extend(history)
+    messages.append({"role": "user", "content": f"What chord is {message}?"})
+
+    prompt = "\n".join([f"{m['role']}: {m['content']}" for m in messages]) + "\nAnswer:"
+
+    response = ""
+    for chunk in client.text_generation(
+        prompt,
+        max_new_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        stream=True,
+    ):
+        response += chunk
+        yield response.strip()
 
-    return f"❌ Error {response.status_code}: {response.text}"
 
-# Gradio chat interface
 chatbot = gr.ChatInterface(
-    fn=chord_api,
-    title="🎶 Chord Bot (API Version)",
-    description=(
-        "This version queries Hugging Face’s free Inference API (`t5-small`). "
-        "Enter notes like `C E G` to see what chord they form."
-    ),
+    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 (nucleus sampling)"),
+    ],
+    type="messages",
 )
 
+with gr.Blocks(css=fancy_css) as demo:
+    with gr.Row():
+        gr.Markdown("<h1 id='title'>🎶 Chord Bot (API-based) 🎶</h1>")
+        gr.LoginButton()
+    chatbot.render()
+
 if __name__ == "__main__":
-    chatbot.launch()
+    demo.launch()