Delete modules/tag_enhancer.py

modules/tag_enhancer.py

@@ -1,52 +0,0 @@
-import gradio as gr
-import re,torch
-from transformers import pipeline,AutoTokenizer,AutoModelForSeq2SeqLM
-
-device = "cpu" if torch.cuda.is_available() else "cpu" # Switched to CPU since we are using HF with no GPU
-
-def load_models():
-    try:
-        enhancer_medium = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance", device=device)
-        enhancer_long = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance-Long", device=device)
-        model_checkpoint = "gokaygokay/Flux-Prompt-Enhance"
-        tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
-        model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint).eval().to(device=device)
-        enhancer_flux = pipeline('text2text-generation', model=model, tokenizer=tokenizer, repetition_penalty=1.5, device=device)
-    except Exception as e:
-        print(e)
-        enhancer_medium = enhancer_long = enhancer_flux = None
-    return enhancer_medium, enhancer_long, enhancer_flux
-enhancer_medium, enhancer_long, enhancer_flux = load_models()
-
-def summarize_prompt(input_prompt, model_choice):
-    if model_choice == "Medium":
-        result = enhancer_medium("Enhance the description: " + input_prompt)
-        summarized_text = result[0]['summary_text']
-        
-        pattern = r'^.*?of\s+(.*?(?:\.|$))'
-        match = re.match(pattern, summarized_text, re.IGNORECASE | re.DOTALL)
-        
-        if match:
-            remaining_text = summarized_text[match.end():].strip()
-            modified_sentence = match.group(1).capitalize()
-            summarized_text = modified_sentence + ' ' + remaining_text
-    elif model_choice == "Flux":
-        result = enhancer_flux("Enhance prompt: " + input_prompt, max_length=256)
-        summarized_text = result[0]['generated_text']
-    else:  # Long
-        result = enhancer_long("Enhance the description: " + input_prompt)
-        summarized_text = result[0]['summary_text']
-    
-    return summarized_text
-
-def prompt_summarizer(character: str, series: str, general: str, model_choice: str):
-    characters = character.split(",") if character else []
-    serieses = series.split(",") if series else []
-    generals = general.split(",") if general else []
-    tags = characters + serieses + generals
-    cprompt = ",".join(tags) if tags else ""
-
-    output = summarize_prompt(cprompt, model_choice)
-    prompt = cprompt + ", " + output
-
-    return prompt, gr.update(interactive=True), gr.update(interactive=True)