import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification, EsmForSequenceClassification
from transformers import set_seed
import torch
import torch.nn as nn
import warnings
from tqdm import tqdm
import gradio as gr

warnings.filterwarnings('ignore')
device = "cpu"
model_checkpoint1 = "facebook/esm2_t12_35M_UR50D"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint1)


class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.bert1 = EsmForSequenceClassification.from_pretrained(model_checkpoint1, num_labels=3000)  # 3000
        self.bn1 = nn.BatchNorm1d(256)
        self.bn2 = nn.BatchNorm1d(128)
        self.bn3 = nn.BatchNorm1d(64)
        self.relu = nn.LeakyReLU()
        self.fc1 = nn.Linear(3000, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 64)
        self.output_layer = nn.Linear(64, 2)
        self.dropout = nn.Dropout(0.3)  # 0.3

    def forward(self, x):
        with torch.no_grad():
            bert_output = self.bert1(input_ids=x['input_ids'],
                                     attention_mask=x['attention_mask'])
        # output_feature = bert_output["logits"]
        # print(output_feature.size())
        # output_feature = self.bn1(self.fc1(output_feature))
        # output_feature = self.bn2(self.fc1(output_feature))
        # output_feature = self.relu(self.bn3(self.fc3(output_feature)))
        # output_feature = self.dropout(self.output_layer(output_feature))
        output_feature = self.dropout(bert_output["logits"])
        output_feature = self.dropout(self.relu(self.bn1(self.fc1(output_feature))))
        output_feature = self.dropout(self.relu(self.bn2(self.fc2(output_feature))))
        output_feature = self.dropout(self.relu(self.bn3(self.fc3(output_feature))))
        output_feature = self.dropout(self.output_layer(output_feature))
        # return torch.sigmoid(output_feature),output_feature
        return torch.softmax(output_feature, dim=1)


def AMP(test_sequences, model):
    # 保持 AMP 函数不变，只处理传入的 test_sequences 数据
    max_len = 18
    test_data = tokenizer(test_sequences, max_length=max_len, padding="max_length", truncation=True,
                          return_tensors='pt')
    model = model.to(device)
    model.eval()
    out_probability = []
    with torch.no_grad():
        predict = model(test_data)
        out_probability.extend(np.max(np.array(predict.cpu()), axis=1).tolist())
        test_argmax = np.argmax(predict.cpu(), axis=1).tolist()
    id2str = {0: "non-AMP", 1: "AMP"}
    return id2str[test_argmax[0]], out_probability[0]


def classify_sequence(sequence):
    # Check if the sequence is a valid amino acid sequence and has a length of at least 3
    valid_amino_acids = set("ACDEFGHIKLMNPQRSTVWY")
    sequence = sequence.upper()

    if all(aa in valid_amino_acids for aa in sequence) and len(sequence) >= 3:
        result, probability = AMP(sequence, model)
        return "yes" if result == "AMP" else "no"
    else:
        return "Invalid Sequence"


# 加载模型
model = MyModel()
model.load_state_dict(torch.load("best_model.pth", map_location=torch.device('cpu')))

if __name__ == "__main__":
    title = """<h1 align="center">🔥AMP Sequence Detector</h1>"""
    css = ".json {height: 527px; overflow: scroll;} .json-holder {height: 527px; overflow: scroll;}"
    theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="blue", neutral_hue="green",
                           text_size=gr.themes.sizes.text_lg)
    with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""",
                      theme=theme) as demo:

        gr.Markdown("<h1>Diff-AMP</h1>")
        gr.HTML(title)


        gr.Markdown(
            "<p align='center' style='font-size: 20px;'>🔥Welcome to Antimicrobial Peptide Recognition Model. See our <a href='https://github.com/wrab12/diff-amp'>Project</a></p>")
        gr.HTML(
            '''<center>
  <a href="https://huggingface.co/spaces/jackrui/ampD?duplicate=true">
    <img src="https://bit.ly/3gLdBN6" alt="Duplicate Space">
  </a>
</center>''')
        gr.HTML(
            '''<center>🌟Note: This is an antimicrobial peptide recognition model derived from Diff-AMP, which is a branch of a comprehensive system integrating generation, recognition, and optimization. In this recognition model, you can simply input a sequence, and it will predict whether it is an antimicrobial peptide. Due to limited website capacity, we can only perform simple predictions.
    If you require large-scale computations, please contact my email at wangrui66677@gmail.com. Feel free to reach out if you have any questions or inquiries.</center>''')

    # gr.Markdown(
    #     """
    #
    # # Welcome to Antimicrobial Peptide Recognition Model
    # This is an antimicrobial peptide recognition model derived from Diff-AMP, which is a branch of a comprehensive system integrating generation, recognition, and optimization. In this recognition model, you can simply input a sequence, and it will predict whether it is an antimicrobial peptide. Due to limited website capacity, we can only perform simple predictions.
    # If you require large-scale computations, please contact my email at wangrui66677@gmail.com. Feel free to reach out if you have any questions or inquiries.
    #
    #     """)

    # 添加示例输入和输出
        examples = [
        ["QGLFFLGAKLFYLLTLFL"],
        ["FLGLLFHGVHHVGKWIHGLIHGHH"],
        ["GLMSTLKGAATNAAVTLLNKLQCKLTGTC"]
    ]

    # 创建 Gradio 接口并应用美化样式和示例
        iface = gr.Interface(
        fn=classify_sequence,
        inputs="text",
        outputs="text",
        # title="AMP Sequence Detector",
        examples=examples
    )
        gr.Markdown(
            "<p align='center'><img src='https://pic4.zhimg.com/v2-eb2a7c0e746e67d1768090eec74f6787_b.jpg'></p>")
        gr.Markdown("<p align='center' style='font-size: 20px;'>Related job links in the same series: </p>")
                    
        gr.Markdown("<p align='center'><a href='https://huggingface.co/spaces/jackrui/ampG'><img style='margin:-0.8em 0 2em 0;' src='https://shields.io/badge/Diff_AMP-Generator-blue' alt='Diff_AMP-Generator-blue'></a></p>"
                    "<p align='center'><a href='https://huggingface.co/spaces/jackrui/ampPP'><img style='margin:-0.8em 0 2em 0;' src='https://shields.io/badge/Diff_AMP-property_prediction-blue' alt='Diff_AMP-property_prediction-blue'></a></p>")
        gr.Markdown('''📝 **Citation**
If our work is useful for your research, please consider citing:
```
waiting...
```
📋 **License**

None

📧 **Contact**

If you have any questions, please feel free to reach me out at <b>wangrui66677@gmail.com</b>.

🤗 **Find Me:**
<style type="text/css">
td {
    padding-right: 0px !important;
}
</style>
<table>
<tr>
    <td><a href="https://github.com/wrab12"><img style="margin:-0.8em 0 2em 0" src="https://img.shields.io/github/followers/wrab12?style=social" alt="Github Follow"></a></td>
    
</tr>
</table>
<center><img src='https://api.infinitescript.com/badgen/count?name=jackrui/ampD&ltext=Visitors&color=6dc9aa' alt='visitors'></center>
"""
''')

    demo.launch()