import json
import random
random.seed(999)
import torch
from transformers import Qwen2ForSequenceClassification, AutoTokenizer
import gradio as gr
from datetime import datetime
torch.set_grad_enabled(False)

model = Qwen2ForSequenceClassification.from_pretrained("Thouph/prompt2tag-qwen2-0.5b-v0.1", num_labels = 9940, device_map="cpu")
model.eval()
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B")

with open("tags_9940.json", "r") as file:
    allowed_tags = json.load(file)

allowed_tags = sorted(allowed_tags)

def create_tags(prompt, threshold):
    inputs = tokenizer(
        prompt,
        padding="do_not_pad",
        max_length=512,
        truncation=True,
        return_tensors="pt",
    )

    output = model(**inputs).logits
    output = torch.nn.functional.sigmoid(output)
    indices = torch.where(output > threshold)
    values = output[indices]
    indices = indices[1]
    values = values.squeeze()

    temp = []
    tag_score = dict()
    for i in range(indices.size(0)):
        temp.append([allowed_tags[indices[i]], values[i].item()])
        tag_score[allowed_tags[indices[i]]] = values[i].item()
    temp = [t[0] for t in temp]
    text_no_impl = " ".join(temp)
    current_datetime = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    print(f"{current_datetime}: finished.")
    return text_no_impl, tag_score

demo = gr.Interface(
    create_tags,
    inputs=[
        gr.TextArea(label="Prompt",),
        gr.Slider(minimum=0.00, maximum=1.00, step=0.01, value=0.40, label="Threshold")
    ],
    outputs=[
        gr.Textbox(label="Tag String"),
        gr.Label(label="Tag Predictions", num_top_classes=200),
    ],
    allow_flagging="never",
)

demo.launch()