app.py

import spaces

import os
import shutil
import json
import torch
import torchaudio
import numpy as np
import logging
import warnings
import subprocess
import math
import random
import time
from pathlib import Path
from tqdm import tqdm
from PIL import Image
from huggingface_hub import snapshot_download
from omegaconf import DictConfig
import hydra
from hydra.utils import to_absolute_path
from transformers import Wav2Vec2FeatureExtractor, AutoModel
import mir_eval
import pretty_midi as pm
import gradio as gr
from gradio import Markdown
from music21 import converter
import torchaudio.transforms as T

# Custom utility imports
from utils import logger
from utils.btc_model import BTC_model
from utils.transformer_modules import *
from utils.transformer_modules import _gen_timing_signal, _gen_bias_mask
from utils.hparams import HParams
from utils.mir_eval_modules import (
    audio_file_to_features, idx2chord, idx2voca_chord,
    get_audio_paths, get_lab_paths
)
from utils.mert import FeatureExtractorMERT
from model.linear_mt_attn_ck import FeedforwardModelMTAttnCK

import matplotlib.pyplot as plt



# Suppress unnecessary warnings and logs
warnings.filterwarnings("ignore")
logging.getLogger("transformers.modeling_utils").setLevel(logging.ERROR)

# from gradio import Markdown

PITCH_CLASS = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']

tonic_signatures = ["A", "A#", "B", "C", "C#", "D", "D#", "E", "F", "F#", "G", "G#"]
mode_signatures = ["major", "minor"]  # Major and minor modes


pitch_num_dic = {
    'C': 0, 'C#': 1, 'D': 2, 'D#': 3, 'E': 4, 'F': 5,
    'F#': 6, 'G': 7, 'G#': 8, 'A': 9, 'A#': 10, 'B': 11
}

minor_major_dic = {
    'D-':'C#', 'E-':'D#', 'G-':'F#', 'A-':'G#', 'B-':'A#'
}
minor_major_dic2 = {
    'Db':'C#', 'Eb':'D#', 'Gb':'F#', 'Ab':'G#', 'Bb':'A#'
}

shift_major_dic = {
    'C': 0, 'C#': 1, 'D': 2, 'D#': 3, 'E': 4, 'F': 5,
    'F#': 6, 'G': 7, 'G#': 8, 'A': 9, 'A#': 10, 'B': 11
}

shift_minor_dic = {
    'A': 0, 'A#': 1, 'B': 2, 'C': 3, 'C#': 4, 'D': 5,  
    'D#': 6, 'E': 7, 'F': 8, 'F#': 9, 'G': 10, 'G#': 11, 
}

flat_to_sharp_mapping = {
    "Cb": "B", 
    "Db": "C#", 
    "Eb": "D#", 
    "Fb": "E", 
    "Gb": "F#", 
    "Ab": "G#", 
    "Bb": "A#"
}

segment_duration = 30
resample_rate = 24000
is_split = True

def normalize_chord(file_path, key, key_type='major'):
    with open(file_path, 'r') as f:
        lines = f.readlines()

    if key == "None":
        new_key = "C major"
        shift = 0
    else:
        #print ("asdas",key)
        if len(key) == 1:
            key = key[0].upper()
        else:
            key = key[0].upper() + key[1:]

        if key in minor_major_dic2:
            key = minor_major_dic2[key]
        
        shift = 0
        
        if key_type == "major":
            new_key = "C major"
            
            shift = shift_major_dic[key]
        else:
            new_key = "A minor"
            shift = shift_minor_dic[key]
    
    converted_lines = []
    for line in lines:
        if line.strip():  # Skip empty lines
            parts = line.split()
            start_time = parts[0]
            end_time = parts[1]
            chord = parts[2]  # The chord is in the 3rd column
            if chord == "N":
                newchordnorm = "N"
            elif chord == "X":
                newchordnorm = "X"
            elif ":" in chord:
                pitch = chord.split(":")[0]
                attr = chord.split(":")[1]
                pnum = pitch_num_dic [pitch]
                new_idx = (pnum - shift)%12
                newchord = PITCH_CLASS[new_idx]
                newchordnorm = newchord + ":" + attr
            else:
                pitch = chord
                pnum = pitch_num_dic [pitch]
                new_idx = (pnum - shift)%12
                newchord = PITCH_CLASS[new_idx]
                newchordnorm = newchord
            
            converted_lines.append(f"{start_time} {end_time} {newchordnorm}\n")
    
    return converted_lines

def sanitize_key_signature(key):
    return key.replace('-', 'b')

def resample_waveform(waveform, original_sample_rate, target_sample_rate):
    if original_sample_rate != target_sample_rate:
        resampler = T.Resample(original_sample_rate, target_sample_rate)
        return resampler(waveform), target_sample_rate
    return waveform, original_sample_rate




# def split_audio(waveform, sample_rate):
#     segment_samples = segment_duration * sample_rate
#     total_samples = waveform.size(0)
    
#     segments = []
#     # If the audio is shorter than the segment duration, just use the entire audio
#     if total_samples <= segment_samples:
#         segments.append(waveform)
#     else:
#         # Split the audio into segments of the specified duration
#         for start in range(0, total_samples, segment_samples):
#             end = min(start + segment_samples, total_samples)
#             segment = waveform[start:end]
#             segments.append(segment)
    
#     # Ensure we have at least one segment with a minimum length
#     if len(segments) == 0 or all(len(segment) < 100 for segment in segments):
#         # Create a padded segment if audio is too short
#         padded_segment = torch.zeros(segment_samples)
#         if total_samples > 0:
#             padded_segment[:total_samples] = waveform
#         segments = [padded_segment]
        
#     return segments

# def split_audio(waveform, sample_rate):
#     segment_samples = segment_duration * sample_rate
#     total_samples = waveform.size(0)

#     segments = []
#     # If the audio is shorter than the segment duration, just use the entire audio
#     if total_samples <= segment_samples:
#         segments.append(waveform)
#     else:
#         # Split the audio into segments of the specified duration
#         for start in range(0, total_samples, segment_samples):
#             end = min(start + segment_samples, total_samples)
#             segment = waveform[start:end]
#             segments.append(segment)
    
#     # Ensure we have at least one segment
#     if len(segments) == 0:
#         segments.append(waveform)

#     return segments

# def split_audio(waveform, sample_rate):
#     segment_samples = segment_duration * sample_rate
#     total_samples = waveform.size(0)

#     # Pad if shorter than one segment
#     if total_samples < segment_samples:
#         pad_size = segment_samples - total_samples
#         waveform = torch.nn.functional.pad(waveform, (0, pad_size))

#     segments = []
#     for start in range(0, waveform.size(0), segment_samples):
#         end = start + segment_samples
#         if end <= waveform.size(0):
#             segment = waveform[start:end]
#             segments.append(segment)

#     return segments

# def split_audio(waveform, sample_rate, segment_duration=10):
#     segment_samples = segment_duration * sample_rate
#     total_samples = waveform.size(0)

#     segments = []
#     for start in range(0, total_samples, segment_samples):
#         end = start + segment_samples
#         if end <= total_samples:
#             segment = waveform[start:end]
#             segments.append(segment)

#     # If no full segments were created, pad the short waveform
#     if len(segments) == 0:
#         pad_length = segment_samples - total_samples
#         padded_waveform = torch.nn.functional.pad(waveform, (0, pad_length))
#         segments.append(padded_waveform)

#     return segments

def split_audio(waveform, sample_rate):
    segment_samples = segment_duration * sample_rate
    total_samples = waveform.size(0)

    segments = []
    for start in range(0, total_samples, segment_samples):
        end = start + segment_samples
        if end <= total_samples:
            segment = waveform[start:end]
            segments.append(segment)
    
    # In case audio length is shorter than segment length.
    if len(segments) == 0: 
        segment = waveform
        segments.append(segment)

    return segments


def safe_remove_dir(directory):
    """
    Safely removes a directory only if it exists and is empty.
    """
    directory = Path(directory)
    if directory.exists():
        try:
            shutil.rmtree(directory)
        except FileNotFoundError:
            print(f"Warning: Some files in {directory} were already deleted.")
        except PermissionError:
            print(f"Warning: Permission issue encountered while deleting {directory}.")
        except Exception as e:
            print(f"Unexpected error while deleting {directory}: {e}")


def pad_to_30_seconds(waveform, sample_rate):
    target_len = 30 * sample_rate
    current_len = waveform.shape[-1]

    if current_len >= target_len:
        return waveform[:, :target_len]  # Truncate if longer
    else:
        pad_len = target_len - current_len
        padding = torch.zeros((waveform.shape[0], pad_len))
        padded_waveform = torch.cat((waveform, padding), dim=1)
        return padded_waveform

class Music2emo:
    def __init__(
        self,
        name="amaai-lab/music2emo",
        device="cuda:0",
        cache_dir=None,
        local_files_only=False,
    ):
        
        # use_cuda = torch.cuda.is_available()
        # self.device = torch.device("cuda" if use_cuda else "cpu")
        model_weights = "saved_models/J_all.ckpt"
        self.device = device

        self.feature_extractor = FeatureExtractorMERT(model_name='m-a-p/MERT-v1-95M', device=self.device, sr=resample_rate)
        self.model_weights = model_weights

        self.music2emo_model = FeedforwardModelMTAttnCK(
            input_size= 768 * 2,
            output_size_classification=56,
            output_size_regression=2
        )

        checkpoint = torch.load(self.model_weights, map_location=self.device, weights_only=False)
        state_dict = checkpoint["state_dict"]
        
        # Adjust the keys in the state_dict
        state_dict = {key.replace("model.", ""): value for key, value in state_dict.items()}
        
        # Filter state_dict to match model's keys
        model_keys = set(self.music2emo_model.state_dict().keys())
        filtered_state_dict = {key: value for key, value in state_dict.items() if key in model_keys}
        
        # Load the filtered state_dict and set the model to evaluation mode
        self.music2emo_model.load_state_dict(filtered_state_dict)
        
        self.music2emo_model.to(self.device)
        self.music2emo_model.eval()

        self.config = HParams.load("./inference/data/run_config.yaml")
        self.config.feature['large_voca'] = True
        self.config.model['num_chords'] = 170
        model_file = './inference/data/btc_model_large_voca.pt'
        self.idx_to_voca = idx2voca_chord()
        self.btc_model = BTC_model(config=self.config.model).to(self.device)

        if os.path.isfile(model_file):
            checkpoint = torch.load(model_file, map_location=self.device)
            self.mean = checkpoint['mean']
            self.std = checkpoint['std']
            self.btc_model.load_state_dict(checkpoint['model'])


        self.tonic_to_idx = {tonic: idx for idx, tonic in enumerate(tonic_signatures)}
        self.mode_to_idx = {mode: idx for idx, mode in enumerate(mode_signatures)}
        self.idx_to_tonic = {idx: tonic for tonic, idx in self.tonic_to_idx.items()}
        self.idx_to_mode = {idx: mode for mode, idx in self.mode_to_idx.items()}

        with open('inference/data/chord.json', 'r') as f:
            self.chord_to_idx = json.load(f)
        with open('inference/data/chord_inv.json', 'r') as f:
            self.idx_to_chord = json.load(f)
            self.idx_to_chord = {int(k): v for k, v in self.idx_to_chord.items()}  # Ensure keys are ints        
        with open('inference/data/chord_root.json') as json_file:
            self.chordRootDic = json.load(json_file)
        with open('inference/data/chord_attr.json') as json_file:
            self.chordAttrDic = json.load(json_file)



    def predict(self, audio, threshold = 0.5):

        feature_dir = Path("./inference/temp_out")
        output_dir = Path("./inference/output")
        
        # if feature_dir.exists():
        #     shutil.rmtree(str(feature_dir))
        # if output_dir.exists():
        #     shutil.rmtree(str(output_dir))
        
        # feature_dir.mkdir(parents=True)
        # output_dir.mkdir(parents=True)

        # warnings.filterwarnings('ignore')
        # logger.logging_verbosity(1)
        
        # mert_dir = feature_dir / "mert"
        # mert_dir.mkdir(parents=True)

        safe_remove_dir(feature_dir)
        safe_remove_dir(output_dir)

        feature_dir.mkdir(parents=True, exist_ok=True)
        output_dir.mkdir(parents=True, exist_ok=True)

        warnings.filterwarnings('ignore')
        logger.logging_verbosity(1)

        mert_dir = feature_dir / "mert"
        mert_dir.mkdir(parents=True, exist_ok=True)

        waveform, sample_rate = torchaudio.load(audio)
        waveform = pad_to_30_seconds(waveform, sample_rate)
        
        if waveform.shape[0] > 1:
            waveform = waveform.mean(dim=0).unsqueeze(0)
        waveform = waveform.squeeze()
        waveform, sample_rate = resample_waveform(waveform, sample_rate, resample_rate)

        # 🔍 Check duration
        duration_sec = waveform.shape[-1] / sample_rate
        is_split = duration_sec > 30.0
        print(f"Audio duration: {duration_sec:.2f} seconds | is_split = {is_split}")
    
        if is_split:
            segments = split_audio(waveform, sample_rate)
            for i, segment in enumerate(segments):
                segment_save_path = os.path.join(mert_dir, f"segment_{i}.npy")
                self.feature_extractor.extract_features_from_segment(segment, sample_rate, segment_save_path)
        else:
            segment_save_path = os.path.join(mert_dir, f"segment_0.npy")
            self.feature_extractor.extract_features_from_segment(waveform, sample_rate, segment_save_path)
            
        # if is_split:        
        #     segments = split_audio(waveform, sample_rate)
        #     for i, segment in enumerate(segments):
        #         segment_save_path = os.path.join(mert_dir, f"segment_{i}.npy")
        #         self.feature_extractor.extract_features_from_segment(segment, sample_rate, segment_save_path)
        # else:
        #     segment_save_path = os.path.join(mert_dir, f"segment_0.npy")
        #     self.feature_extractor.extract_features_from_segment(waveform, sample_rate, segment_save_path)

        embeddings = []
        layers_to_extract = [5,6]
        segment_embeddings = []
        for filename in sorted(os.listdir(mert_dir)):  # Sort files to ensure sequential order
            file_path = os.path.join(mert_dir, filename)
            if os.path.isfile(file_path) and filename.endswith('.npy'):
                segment = np.load(file_path)
                concatenated_features = np.concatenate(
                    [segment[:, layer_idx, :] for layer_idx in layers_to_extract], axis=1
                )
                concatenated_features = np.squeeze(concatenated_features)  # Shape: 768 * 2 = 1536
                segment_embeddings.append(concatenated_features)

        segment_embeddings = np.array(segment_embeddings)
        if len(segment_embeddings) > 0:
            final_embedding_mert = np.mean(segment_embeddings, axis=0)
        else:
            final_embedding_mert = np.zeros((1536,))

        final_embedding_mert = torch.from_numpy(final_embedding_mert)
        final_embedding_mert.to(self.device)

        # --- Chord feature extract ---

        audio_path = audio
        audio_id = audio_path.split("/")[-1][:-4]
        try:
            feature, feature_per_second, song_length_second = audio_file_to_features(audio_path, self.config)
        except:
            logger.info("audio file failed to load : %s" % audio_path)
            assert(False)
            
        logger.info("audio file loaded and feature computation success : %s" % audio_path)
        
        feature = feature.T
        feature = (feature - self.mean) / self.std
        time_unit = feature_per_second
        n_timestep = self.config.model['timestep']

        num_pad = n_timestep - (feature.shape[0] % n_timestep)
        feature = np.pad(feature, ((0, num_pad), (0, 0)), mode="constant", constant_values=0)
        num_instance = feature.shape[0] // n_timestep

        start_time = 0.0
        lines = []
        with torch.no_grad():
            self.btc_model.eval()
            feature = torch.tensor(feature, dtype=torch.float32).unsqueeze(0).to(self.device)
            for t in range(num_instance):
                self_attn_output, _ = self.btc_model.self_attn_layers(feature[:, n_timestep * t:n_timestep * (t + 1), :])
                prediction, _ = self.btc_model.output_layer(self_attn_output)
                prediction = prediction.squeeze()
                for i in range(n_timestep):
                    if t == 0 and i == 0:
                        prev_chord = prediction[i].item()
                        continue
                    if prediction[i].item() != prev_chord:
                        lines.append(
                            '%.3f %.3f %s\n' % (start_time, time_unit * (n_timestep * t + i), self.idx_to_voca[prev_chord]))
                        start_time = time_unit * (n_timestep * t + i)
                        prev_chord = prediction[i].item()
                    if t == num_instance - 1 and i + num_pad == n_timestep:
                        if start_time != time_unit * (n_timestep * t + i):
                            lines.append('%.3f %.3f %s\n' % (start_time, time_unit * (n_timestep * t + i), self.idx_to_voca[prev_chord]))
                        break

        save_path = os.path.join(feature_dir, os.path.split(audio_path)[-1].replace('.mp3', '').replace('.wav', '') + '.lab')
        with open(save_path, 'w') as f:
            for line in lines:
                f.write(line)

        # logger.info("label file saved : %s" % save_path)

        # lab file to midi file
        starts, ends, pitchs = list(), list(), list()

        intervals, chords = mir_eval.io.load_labeled_intervals(save_path)
        for p in range(12):
            for i, (interval, chord) in enumerate(zip(intervals, chords)):
                root_num, relative_bitmap, _ = mir_eval.chord.encode(chord)
                tmp_label = mir_eval.chord.rotate_bitmap_to_root(relative_bitmap, root_num)[p]
                if i == 0:
                    start_time = interval[0]
                    label = tmp_label
                    continue
                if tmp_label != label:
                    if label == 1.0:
                        starts.append(start_time), ends.append(interval[0]), pitchs.append(p + 48)
                    start_time = interval[0]
                    label = tmp_label
                if i == (len(intervals) - 1): 
                    if label == 1.0:
                        starts.append(start_time), ends.append(interval[1]), pitchs.append(p + 48)

        midi = pm.PrettyMIDI()
        instrument = pm.Instrument(program=0)

        for start, end, pitch in zip(starts, ends, pitchs):
            pm_note = pm.Note(velocity=120, pitch=pitch, start=start, end=end)
            instrument.notes.append(pm_note)

        midi.instruments.append(instrument)
        midi.write(save_path.replace('.lab', '.midi'))



        
        try:
            midi_file = converter.parse(save_path.replace('.lab', '.midi'))
            key_signature = str(midi_file.analyze('key'))
        except Exception as e:
            key_signature = "None"

        key_parts = key_signature.split()
        key_signature = sanitize_key_signature(key_parts[0])  # Sanitize key signature
        key_type = key_parts[1] if len(key_parts) > 1 else 'major'

        # --- Key feature (Tonic and Mode separation) --- 
        if key_signature == "None":
            mode = "major"
        else:
            mode = key_signature.split()[-1]
        
        encoded_mode = self.mode_to_idx.get(mode, 0)
        mode_tensor = torch.tensor([encoded_mode], dtype=torch.long).to(self.device)

        converted_lines = normalize_chord(save_path, key_signature, key_type)

        lab_norm_path = save_path[:-4] + "_norm.lab"
        
        # Write the converted lines to the new file
        with open(lab_norm_path, 'w') as f:
            f.writelines(converted_lines)

        chords = []
        
        if not os.path.exists(lab_norm_path):
            chords.append((float(0), float(0), "N"))
        else:
            with open(lab_norm_path, 'r') as file:
                for line in file:
                    start, end, chord = line.strip().split()
                    chords.append((float(start), float(end), chord))

        encoded = []
        encoded_root= []
        encoded_attr=[]
        durations = []

        for start, end, chord in chords:
            chord_arr = chord.split(":")
            if len(chord_arr) == 1:
                if chord_arr[0] == "X":
                    chord_arr[0] = "N"
                chordRootID = self.chordRootDic[chord_arr[0]]
                if chord_arr[0] == "N" or chord_arr[0] == "X":
                    chordAttrID = 0
                else:
                    chordAttrID = 1
            elif len(chord_arr) == 2:
                chordRootID = self.chordRootDic[chord_arr[0]]
                chordAttrID = self.chordAttrDic[chord_arr[1]]
            encoded_root.append(chordRootID)
            encoded_attr.append(chordAttrID)

            if chord in self.chord_to_idx:
                encoded.append(self.chord_to_idx[chord])
            else:
                print(f"Warning: Chord {chord} not found in chord.json. Skipping.")
            
            durations.append(end - start)  # Compute duration
        
        encoded_chords = np.array(encoded)
        encoded_chords_root = np.array(encoded_root)
        encoded_chords_attr = np.array(encoded_attr)
        
        # Maximum sequence length for chords
        max_sequence_length = 100  # Define this globally or as a parameter

        # Truncate or pad chord sequences
        if len(encoded_chords) > max_sequence_length:
            # Truncate to max length
            encoded_chords = encoded_chords[:max_sequence_length]
            encoded_chords_root = encoded_chords_root[:max_sequence_length]
            encoded_chords_attr = encoded_chords_attr[:max_sequence_length]
        
        else:
            # Pad with zeros (padding value for chords)
            padding = [0] * (max_sequence_length - len(encoded_chords))
            encoded_chords = np.concatenate([encoded_chords, padding])
            encoded_chords_root = np.concatenate([encoded_chords_root, padding])
            encoded_chords_attr = np.concatenate([encoded_chords_attr, padding])
            
        # Convert to tensor
        chords_tensor = torch.tensor(encoded_chords, dtype=torch.long).to(self.device)
        chords_root_tensor = torch.tensor(encoded_chords_root, dtype=torch.long).to(self.device)
        chords_attr_tensor = torch.tensor(encoded_chords_attr, dtype=torch.long).to(self.device)

        model_input_dic = {
            "x_mert": final_embedding_mert.unsqueeze(0),
            "x_chord": chords_tensor.unsqueeze(0),
            "x_chord_root": chords_root_tensor.unsqueeze(0),
            "x_chord_attr": chords_attr_tensor.unsqueeze(0),
            "x_key": mode_tensor.unsqueeze(0)
        }

        model_input_dic = {k: v.to(self.device) for k, v in model_input_dic.items()}
        classification_output, regression_output = self.music2emo_model(model_input_dic)
        # probs = torch.sigmoid(classification_output)

        tag_list = np.load ( "./inference/data/tag_list.npy")
        tag_list = tag_list[127:]
        mood_list = [t.replace("mood/theme---", "") for t in tag_list]
        threshold = threshold

        # Get probabilities
        probs = torch.sigmoid(classification_output).squeeze().tolist()

        # Include both mood names and scores
        predicted_moods_with_scores = [
            {"mood": mood_list[i], "score": round(p, 4)}  # Rounded for better readability
            for i, p in enumerate(probs) if p > threshold
        ]

        # Include both mood names and scores
        predicted_moods_with_scores_all = [
            {"mood": mood_list[i], "score": round(p, 4)}  # Rounded for better readability
            for i, p in enumerate(probs)
        ]


        # Sort by highest probability
        predicted_moods_with_scores.sort(key=lambda x: x["score"], reverse=True)

        valence, arousal = regression_output.squeeze().tolist()

        model_output_dic = {
            "valence": valence,
            "arousal": arousal,
            "predicted_moods": predicted_moods_with_scores,
            "predicted_moods_all": predicted_moods_with_scores_all
        }

        # predicted_moods = [mood_list[i] for i, p in enumerate(probs.squeeze().tolist()) if p > threshold]
        # valence, arousal = regression_output.squeeze().tolist()
        # model_output_dic = {
        #     "valence": valence,
        #     "arousal": arousal,
        #     "predicted_moods": predicted_moods
        # }

        return model_output_dic

# Music2Emo Model Initialization
if torch.cuda.is_available():
    music2emo = Music2emo()
else:
    music2emo = Music2emo(device="cpu")

# Plot Functions
def plot_mood_probabilities(predicted_moods_with_scores):
    """Plot mood probabilities as a horizontal bar chart."""
    if not predicted_moods_with_scores:
        return None

    # Extract mood names and their scores
    moods = [m["mood"] for m in predicted_moods_with_scores]
    probs = [m["score"] for m in predicted_moods_with_scores]

    # Sort moods by probability
    sorted_indices = np.argsort(probs)[::-1]
    sorted_probs = [probs[i] for i in sorted_indices]
    sorted_moods = [moods[i] for i in sorted_indices]

    # Create bar chart
    fig, ax = plt.subplots(figsize=(8, 4))
    ax.barh(sorted_moods[:10], sorted_probs[:10], color="#4CAF50")
    ax.set_xlabel("Probability")
    ax.set_title("Top 10 Predicted Mood Tags")
    ax.invert_yaxis()
    
    return fig

def plot_valence_arousal(valence, arousal):
    """Plot valence-arousal on a 2D circumplex model."""
    fig, ax = plt.subplots(figsize=(4, 4))
    ax.scatter(valence, arousal, color="red", s=100)
    ax.set_xlim(1, 9)
    ax.set_ylim(1, 9)

# Add midpoint lines
    ax.axhline(y=5, color='gray', linestyle='--', linewidth=1)  # Horizontal middle line
    ax.axvline(x=5, color='gray', linestyle='--', linewidth=1)  # Vertical middle line

    # Labels & Grid
    ax.set_xlabel("Valence (Positivity)")
    ax.set_ylabel("Arousal (Intensity)")
    ax.set_title("Valence-Arousal Plot")
    ax.legend()
    ax.grid(True, linestyle="--", alpha=0.6)

    return fig


# Prediction Formatting
def format_prediction(model_output_dic):
    """Format the model output in a structured format"""
    valence = model_output_dic["valence"]
    arousal = model_output_dic["arousal"]
    predicted_moods_with_scores = model_output_dic["predicted_moods"]
    predicted_moods_with_scores_all = model_output_dic["predicted_moods_all"]
    
    # Generate charts
    va_chart = plot_valence_arousal(valence, arousal)
    mood_chart = plot_mood_probabilities(predicted_moods_with_scores_all)

    # Format mood output with scores
    if predicted_moods_with_scores:
        moods_text = ", ".join(
            [f"{m['mood']} ({m['score']:.2f})" for m in predicted_moods_with_scores]
        )
    else:
        moods_text = "No significant moods detected."

    # Create formatted output
    output_text = f"""🎭 Predicted Mood Tags: {moods_text}

💖 Valence: {valence:.2f} (Scale: 1-9)  
⚡ Arousal: {arousal:.2f} (Scale: 1-9)"""

    return output_text, va_chart, mood_chart

# Gradio UI Elements
title="🎵 Music2Emo: Toward Unified Music Emotion Recognition"
description_text = """
<p> Upload an audio file to analyze its emotional characteristics using Music2Emo. The model will predict: 1) Mood tags describing the emotional content, 2) Valence score (1-9 scale, representing emotional positivity), and 3) Arousal score (1-9 scale, representing emotional intensity) 
<br/><br/> This is the demo for Music2Emo for music emotion recognition: <a href="https://arxiv.org/abs/2502.03979">Read our paper.</a>
</p>
"""

# Custom CSS Styling
css = """
.gradio-container {
    font-family: 'Inter', -apple-system, system-ui, sans-serif;
}
.gr-button {
    color: white;
    background: #4CAF50;
    border-radius: 8px;
    padding: 10px;
}
/* Add padding to the top of the two plot boxes */
.gr-box {
    padding-top: 25px !important;
}
"""

@spaces.GPU(duration=120)
def analyze_emotions(audio_path, threshold):
    if audio_path is None:
        return "❌ Please upload a valid audio file.", None, None
    try:
        model_output = music2emo.predict(audio_path, threshold)
        return format_prediction(model_output)
    except Exception as e:
        return f"⚠️ Error during prediction: {e}", None, None


with gr.Blocks(css=css) as demo:
    gr.HTML(f"<h1 style='text-align: center;'>{title}</h1>")
    gr.Markdown(description_text)

        # Notes Section
    gr.Markdown("""
    ### 📝 Notes:
    - **Supported audio formats:** MP3, WAV  
    - **Recommended:** High-quality audio files  
    """)
    
    with gr.Row():
        # Left Panel (Input)
        with gr.Column(scale=1):
            input_audio = gr.Audio(
                label="Upload Audio File",
                type="filepath"
            )
            threshold = gr.Slider(
                minimum=0.0,
                maximum=1.0,
                value=0.5,
                step=0.01,
                label="Mood Detection Threshold",
                info="Adjust threshold for mood detection"
            )
            predict_btn = gr.Button("🎭 Analyze Emotions", variant="primary")
        
        # Right Panel (Output)
        with gr.Column(scale=1):
            output_text = gr.Textbox(
                label="Analysis Results",
                lines=4,
                interactive=False  # Prevent user input
            )

            # Ensure both plots have padding on top
            with gr.Row(equal_height=True):
                mood_chart = gr.Plot(label="Mood Probabilities", scale=2, elem_classes=["gr-box"])
                va_chart = gr.Plot(label="Valence-Arousal Space", scale=1, elem_classes=["gr-box"])

    # predict_btn.click(
    #     fn=lambda audio, thresh: format_prediction(music2emo.predict(audio, thresh)),
    #     inputs=[input_audio, threshold],
    #     outputs=[output_text, va_chart, mood_chart]
    # )
    predict_btn.click(
        fn=analyze_emotions,
        inputs=[input_audio, threshold],
        outputs=[output_text, va_chart, mood_chart]
    )

    

# Launch the App
demo.queue().launch()