#AICC 3, SOUND OF NATURE
# %%
import torchaudio
import torchaudio.transforms as T
import os

# %%
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torchvision import models, transforms
from PIL import Image
from torchvision.transforms import functional as TF
import torch.nn.functional as F

# %%
train = pd.read_csv('train.csv')
train.head()

# %%
spectograme = []
for i in range(len(train)):
    audio = os.path.join("./audio",f"{train['sampleID'][i]}.wav")
    waveform, sr = torchaudio.load(audio)
    mel_spec = T.MelSpectrogram(
    sample_rate=sr,
    n_mels=128,
    n_fft=1024,
    hop_length=512
    )(waveform)
    mel_spec_db = T.AmplitudeToDB()(mel_spec)
    spectograme.append(mel_spec_db)
    # print(audio)
spectograme

# %%
def spec_to_image(spec):

    if spec.dim() == 2:
        spec = spec.unsqueeze(0)  
    spec = F.interpolate(spec.unsqueeze(0), size=(224, 224), mode='bilinear').squeeze(0)
    spec = spec.repeat(3, 1, 1)
    normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )
    spec = normalize(spec)
    return spec

# %%
train = pd.read_csv('train.csv')
train.head()

# %%
class_map = {'cat':0, "pig":1, "hen":2, "water_drops":3,"rain":4,"dog":5,"sheep":6,"cow":7,"chirping_birds":8}

# %%
class AudioDataset(Dataset):
    def __init__(self, path):
        self.df = pd.read_csv(path)
    def __len__(self):
        return len(self.df)
    def __getitem__(self, idx):
        row = self.df.iloc[idx]
        spec = torch.tensor(spectograme[idx], dtype=torch.float32)
        img = spec_to_image(spec)
        label = class_map[row['class']]
        return img, label

# %%
train_ds = AudioDataset("./train.csv")
train_dataloader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=0)

# %%
train['class'].unique()

# %%
train['class'].unique()

# %%
device = torch.device('cuda')

# %%
weights = models.ResNet50_Weights.IMAGENET1K_V2
model = models.resnet50(weights=weights)
model.fc = nn.Linear(2048,9)

for param in model.parameters():
    param.requires_grad = False
for param in model.fc.parameters():
    param.requires_grad = True
model = model.to(device)
optimizer= optim.Adam(model.fc.parameters(), lr=1e-3)
criterion = nn.CrossEntropyLoss()

# %%
for epoch in range(40):
    total_loss = 0
    for spectogram, label in train_dataloader:
        spectogram = spectogram.to(device)
        label = label.to(device)
        optimizer.zero_grad()
        output = model(spectogram)
        loss = criterion(output,label)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    print(f"Epoch {epoch}: loss={total_loss/len(train_dataloader):.4f}")
    if epoch == 20:
        for param in model.parameters():
            param.requires_grad = True
        optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

# %%
test = pd.read_csv('test.csv')

# %%
spectograme_test = []
for i in range(len(test)):
    audio = os.path.join("./audio",f"{test['sampleID'][i]}.wav")
    waveform, sr = torchaudio.load(audio)
    mel_spec = T.MelSpectrogram(
    sample_rate=sr,
    n_mels=128,
    n_fft=1024,
    hop_length=512
    )(waveform)
    mel_spec_db = T.AmplitudeToDB()(mel_spec)
    spectograme_test.append(mel_spec_db)
    # print(audio)
spectograme_test

# %%
class AudioDatasetTest(Dataset):
    def __init__(self, path):
        self.df = pd.read_csv(path)
    def __len__(self):
        return len(self.df)
    def __getitem__(self, idx):
        spec = torch.tensor(spectograme_test[idx], dtype=torch.float32)
        img = spec_to_image(spec)
        return img

# %%
test_ds = AudioDatasetTest("./test.csv")
test_loader = DataLoader(test_ds,batch_size=32, num_workers=0, shuffle=False)

# %%
preds= []
for spectogram in test_loader:
    with torch.no_grad():
        spectogram = spectogram.to(device)
        pred = model(spectogram)
        _,pred = torch.max(pred, 1)
        preds.append(pred.cpu())
        preds_final = torch.cat(preds)
preds_final

# %%
preds_final = preds_final.numpy()

# %%
test = pd.read_csv('test.csv')
test.head()

# %%


# %%
class_map = {'cat':0, "pig":1, "hen":2, "water_drops":3,"rain":4,"dog":5,"sheep":6,"cow":7,"chirping_birds":8}

# %%
rev = {}
for label,value in class_map.items():
    rev[value] = label
rev

# %%
preds_final_rev = []
for i in preds_final:
    preds_final_rev.append(rev[i])
preds_final_rev

# %%
subs = pd.DataFrame({
    "sampleID":test['sampleID'],
    "class":preds_final_rev
}).to_csv("subs.csv",index=False)

# %%



