Create app.py

app.py

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+import gradio as gr
+import tempfile
+import random
+import json
+import re
+import pretty_midi
+import subprocess
+import os
+
+# Audio playback support
+try:
+    import pygame
+    pygame.mixer.init()
+    PYGAME_AVAILABLE = True
+except Exception as e:
+    print(f"[WARNING] pygame mixer init failed: {e}")
+    PYGAME_AVAILABLE = False
+
+# === LLM APIs ===
+def query_llm(prompt, model_name=None):
+    if model_name and model_name != "OpenAI":
+        import requests
+        response = requests.post("http://localhost:11434/api/generate", json={"model": model_name, "prompt": prompt, "stream": False})
+        return response.json().get("response", "")
+    else:
+        import openai
+        openai.api_key = "your-api-key"  # Replace or load from environment
+        response = openai.ChatCompletion.create(
+            model="gpt-4",
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.7
+        )
+        return response["choices"][0]["message"]["content"]
+
+# === Step 1: Parse intent ===
+def get_intent_from_prompt(prompt, model_name):
+    system_prompt = f"""
+    Extract the musical intent from this prompt.
+    Return JSON with keys: tempo (int), key (A-G#), scale (major/minor), genre (e.g., lo-fi, trap), emotion, instrument.
+    Prompt: '{prompt}'
+    """
+    response = query_llm(system_prompt, model_name)
+    match = re.search(r'\{.*\}', response, re.DOTALL)
+    if match:
+        try:
+            return json.loads(match.group(0))
+        except json.JSONDecodeError:
+            return {"tempo": 120, "key": "C", "scale": "major", "genre": "default", "emotion": "neutral", "instrument": "piano"}
+    return {"tempo": 120, "key": "C", "scale": "major", "genre": "default", "emotion": "neutral", "instrument": "piano"}
+
+# === Step 2: Melody planning ===
+def get_melody_from_intent(intent, model_name):
+    melody_prompt = f"""
+    You are a music composer.
+    Based on this musical intent:
+    {json.dumps(intent)}
+
+    Generate a melody plan using a list of 16 notes with pitch (A-G#), octave (3-6), and duration (0.25 to 1.0 seconds).
+    Output ONLY valid JSON like:
+    [
+        {{"note": "D", "octave": 4, "duration": 0.5}},
+        {{"note": "F", "octave": 4, "duration": 1.0}}
+    ]
+    """
+    response = query_llm(melody_prompt, model_name)
+    print(f"\n[DEBUG] LLM Response for melody:\n{response}\n")
+    match = re.search(r'\[.*\]', response, re.DOTALL)
+    if match:
+        try:
+            melody = json.loads(match.group(0))
+            if isinstance(melody, list) and len(melody) > 0:
+                return melody
+        except json.JSONDecodeError as e:
+            print(f"[ERROR] Melody JSON decode error: {e}")
+
+    print("[WARNING] Using fallback melody.")
+    return [
+        {"note": "C", "octave": 4, "duration": 0.5},
+        {"note": "E", "octave": 4, "duration": 0.5},
+        {"note": "G", "octave": 4, "duration": 0.5},
+        {"note": "B", "octave": 4, "duration": 0.5},
+    ]
+
+# === Step 3: MIDI generation ===
+def midi_from_plan(melody, tempo):
+    midi = pretty_midi.PrettyMIDI()
+    instrument = pretty_midi.Instrument(program=0)
+    time = 0.0
+    seconds_per_beat = 60.0 / tempo
+
+    note_map = {"C": 0, "C#": 1, "D": 2, "D#": 3, "E": 4, "F": 5, "F#": 6,
+                "G": 7, "G#": 8, "A": 9, "A#": 10, "B": 11}
+
+    for note_info in melody:
+        try:
+            pitch = 12 * (note_info["octave"] + 1) + note_map[note_info["note"].upper()]
+            duration = float(note_info["duration"])
+            start = time
+            end = time + duration
+            instrument.notes.append(pretty_midi.Note(
+                velocity=100, pitch=pitch, start=start, end=end
+            ))
+            time = end
+        except:
+            continue
+
+    midi.instruments.append(instrument)
+    return midi
+
+# === Generate audio preview from MIDI ===
+def midi_to_wav(midi_path):
+    try:
+        import tempfile
+        import subprocess
+        import os
+
+        # Convert MIDI to WAV using FluidSynth if installed, else fallback to empty
+        wav_path = tempfile.NamedTemporaryFile(delete=False, suffix=".wav").name
+
+        # Use fluidsynth if available, else skip audio preview
+        fluidsynth_cmd = ["fluidsynth", "-ni", "/usr/share/sounds/sf2/FluidR3_GM.sf2", midi_path, "-F", wav_path, "-r", "44100"]
+        result = subprocess.run(fluidsynth_cmd, capture_output=True)
+        if result.returncode != 0:
+            print("[WARNING] FluidSynth conversion failed or is not installed.")
+            return None
+        return wav_path
+    except Exception as e:
+        print(f"[ERROR] midi_to_wav failed: {e}")
+        return None
+
+# === Main function to generate MIDI and audio preview ===
+def generate_midi_and_audio(prompt, model_name):
+    intent = get_intent_from_prompt(prompt, model_name)
+    melody = get_melody_from_intent(intent, model_name)
+    midi = midi_from_plan(melody, intent.get("tempo", 120))
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mid") as tmp:
+        midi.write(tmp.name)
+        midi_path = tmp.name
+
+    audio_path = None
+    if PYGAME_AVAILABLE:
+        audio_path = midi_path  # We'll use pygame to play midi directly if possible
+
+    return midi_path, audio_path
+
+# === Get Ollama models ===
+def get_ollama_models():
+    try:
+        result = subprocess.run(["ollama", "list"], capture_output=True, text=True)
+        models = [line.split()[0] for line in result.stdout.strip().splitlines()[1:]]
+        return ["OpenAI"] + models
+    except Exception as e:
+        return ["OpenAI"]
+
+# === Gradio UI ===
+models = get_ollama_models()
+
+demo = gr.Interface(
+    fn=generate_midi_and_audio,
+    inputs=[
+        gr.Textbox(label="Music Prompt"),
+        gr.Dropdown(choices=models, label="LLM Model", value=models[0])
+    ],
+    outputs=[
+        gr.File(label="🎵 Download MIDI File"),
+        gr.Audio(label="🎧 Audio Preview (MIDI Playback, if supported)", type="filepath")
+    ],
+    title="🎼 Music Command Prompt (MCP Agent)",
+    description="Describe your music idea and download a generated MIDI file. Choose from local or OpenAI LLMs."
+)
+
+demo.launch(mcp_server=True)