import gradio as gr
import torch
import soundfile as sf
import os
import numpy as np

import os
import soundfile as sf
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, Wav2Vec2ForSequenceClassification
from collections import Counter

device = torch.device("cpu")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
model = Wav2Vec2ForSequenceClassification.from_pretrained("facebook/wav2vec2-base-960h", num_labels=2).to(device)
# model_path = "dysarthria_classifier12.pth"
model_path = 'model_weights2.pth'
# model_path = '/home/user/app/dysarthria_classifier12.pth'
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))

# if os.path.exists(model_path):
#     print(f"Loading saved model {model_path}")
#     model.load_state_dict(torch.load(model_path))


title = "Upload an mp3 file for parkinsons detection! (Thai Language)"
description = """
The model was trained on Thai audio recordings with the following sentences: 
ชาวไร่ตัดต้นสนทำท่อนซุง\n
ปูม้าวิ่งไปมาบนใบไม้ (เน้นใช้ริมฝีปาก)\n
อีกาคอยคาบงูคาบไก่ (เน้นใช้เพดานปาก)\n
เพียงแค่ฝนตกลงที่หน้าต่างในบางครา\n
“อาาาาาาาาาาา”\n
“อีีีีีีีีี”\n
“อาาาา” (ดังขึ้นเรื่อยๆ)\n
“อาา อาาา อาาาาา”\n
<img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px>
"""





def predict(file_path):
    max_length = 100000

    model.eval()
    with torch.no_grad():
        wav_data, _ = sf.read(file_path.name)
        inputs = processor(wav_data, sampling_rate=16000, return_tensors="pt", padding=True)

        input_values = inputs.input_values.squeeze(0)  
        if max_length - input_values.shape[-1] > 0:
            input_values = torch.cat([input_values, torch.zeros((max_length - input_values.shape[-1],))], dim=-1)
        else:
            input_values = input_values[:max_length]
        input_values = input_values.unsqueeze(0).to(device)
        inputs = {"input_values": input_values}

        logits = model(**inputs).logits
        logits = logits.squeeze()
        predicted_class_id = torch.argmax(logits, dim=-1).item()

    return predicted_class_id
# gr.Interface(
#     fn=predict,
#     inputs="file",
#     outputs="text",
#     title=title,
#     description=description,
# ).launch()

iface = gr.Interface(fn=predict, inputs="file", outputs="text")
iface.launch()