import tempfile
import torch
import torch.nn.functional as F
import torchaudio
import gradio as gr
from transformers import Wav2Vec2FeatureExtractor, AutoConfig
from models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification

# Load model and feature extractor
config = AutoConfig.from_pretrained("Gizachew/wev2vec-large960-agu-amharic")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("Gizachew/wev2vec-large960-agu-amharic")
model = Wav2Vec2ForSpeechClassification.from_pretrained("Gizachew/wev2vec-large960-agu-amharic")
sampling_rate = feature_extractor.sampling_rate

# List of test examples (you can replace these with your actual file paths)
test_example = [
    "test_examples/a5-06-02-02-60.wav",
    "test_examples/f2-04-02-02-65.wav",
    "test_examples/h3-06-02-02-41.wav",
    "test_examples/n1-01-01-01-25.wav",
    "test_examples/s4-06-01-02-51.wav"
]

# Define inputs and outputs for the Gradio interface
audio_input = gr.Audio(label="Upload file", type="filepath")
dropdown_input = gr.Dropdown(label="Choose an example audio file", choices=test_example)
text_output = gr.TextArea(label="Emotion Prediction Output", text_align="right", rtl=True, type="text")

def SER(audio):
    with tempfile.NamedTemporaryFile(suffix=".wav") as temp_audio_file:
        # Copy the contents of the uploaded audio file to the temporary file
        temp_audio_file.write(open(audio, "rb").read())
        temp_audio_file.flush()
        # Load the audio file using torchaudio
        speech_array, _sampling_rate = torchaudio.load(temp_audio_file.name)
        resampler = torchaudio.transforms.Resample(_sampling_rate)
        speech = resampler(speech_array).squeeze().numpy()
        inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
        inputs = {key: inputs[key] for key in inputs}

        with torch.no_grad():
            logits = model(**inputs).logits

        scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
        # Get the highest score and its corresponding label
        max_index = scores.argmax()
        label = config.id2label[max_index]
        score = scores[max_index]

        # Format the output string
        output = f"{label}: {score * 100:.1f}%"
        
        return output

def process_audio(audio_path):
    return SER(audio_path)

# Create the Gradio interface
iface = gr.Interface(
    fn=process_audio,
    inputs=[audio_input, dropdown_input],
    outputs=text_output
)

# Launch the Gradio app
iface.launch(share=True)