Update app.py

app.py

@@ -1,83 +1,41 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import os
 
-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;
-}
-"""
+fancy_css = """..."""
 
 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 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
+HF_TOKEN = os.environ.get("HF_TOKEN")
+client = InferenceClient(token=HF_TOKEN, model="google/flan-t5-small")
 
-    client = InferenceClient(
-        token=hf_token.token,
-        model="google/flan-t5-small",  # change if you want to call your own model repo
-    )
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    if HF_TOKEN is None:
+        yield "⚠️ No HF_TOKEN found. Please add it in your Space secrets."
+        return
 
-    # 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}?"})
+    try:
+        prompt = f"{system_message}\nUser: {message}\nAnswer:"
+        response = ""
 
-    prompt = "\n".join([f"{m['role']}: {m['content']}" for m in messages]) + "\nAnswer:"
+        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()
 
-    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()
+        if not response.strip():
+            yield "⚠️ No response from model (check model ID or token)."
 
+    except Exception as e:
+        yield f"⚠️ Error: {str(e)}"
 
 chatbot = gr.ChatInterface(
     fn=respond,
@@ -85,15 +43,13 @@ chatbot = gr.ChatInterface(
         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)"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
     ],
     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()
+    gr.Markdown("<h1 id='title'>🎶 Chord Bot (API-based) 🎶</h1>")
     chatbot.render()
 
 if __name__ == "__main__":