Delete deepsite_backend.py

deepsite_backend.py

@@ -1,122 +0,0 @@
-import gradio as gr
-import torch
-from PIL import Image
-from transformers import BlipProcessor, BlipForConditionalGeneration, CLIPProcessor, CLIPModel
-from torchvision import transforms
-
-# Geräteeinstellung
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# BLIP-Modelle
-blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(device)
-
-# CLIP-Modelle
-clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
-clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-
-# Platzhalter für DeepDanbooru
-
-def danbooru_tagging(image):
-    return "1girl, bodysuit, sitting, wooden floor, solo"
-
-def generate_blip_caption(image):
-    raw_image = Image.open(image).convert("RGB")
-    inputs = blip_processor(raw_image, return_tensors="pt").to(device)
-    out = blip_model.generate(**inputs)
-    caption = blip_processor.decode(out[0], skip_special_tokens=True)
-    return caption
-
-def generate_clip_prompt(image, detail_level):
-    raw_image = Image.open(image).convert("RGB")
-    inputs = clip_processor(images=raw_image, return_tensors="pt").to(device)
-    outputs = clip_model.get_image_features(**inputs)
-    base_prompt = "a woman in a bodysuit on wooden floor"
-    if detail_level >= 4:
-        return base_prompt + ", cinematic lighting, ultra detailed, HDR"
-    elif detail_level == 3:
-        return base_prompt + ", moody atmosphere"
-    elif detail_level == 2:
-        return base_prompt + ", minimal shadows"
-    else:
-        return base_prompt
-
-def get_output(image, output_type, style, detail_level, tags, model_choice):
-    if model_choice == "BLIP":
-        if output_type == "Detailed Description":
-            return generate_blip_caption(image)
-        elif output_type == "Short Caption":
-            return generate_blip_caption(image).split(",")[0]
-        elif output_type == "Model Training Data":
-            return generate_blip_caption(image).lower().replace(" ", "_")
-        elif output_type == "AI Prompt":
-            return generate_clip_prompt(image, detail_level)
-    elif model_choice == "CLIP":
-        return generate_clip_prompt(image, detail_level)
-    elif model_choice == "DeepDanbooru":
-        return danbooru_tagging(image)
-    elif model_choice == "NSFW Detector":
-        return "(Simulierter NSFW-Klassifikator: Ergebnis nicht implementiert)"
-    return "[Keine gültige Auswahl getroffen]"
-
-with gr.Blocks(css="style.css") as app:
-    gr.Markdown("# NSFW Image to Text Generator ✨")
-
-    with gr.Row():
-        with gr.Column():
-            img = gr.Image(type="filepath", label="Upload Image")
-            output_type = gr.Dropdown([
-                "Detailed Description", "Short Caption", "Tag List", "AI Prompt", "Model Training Data"], 
-                label="Output Type", value="Detailed Description")
-            style = gr.Dropdown([
-                "Neutral", "Erotic", "Artistic", "Technical", "Literary", "BDSM", "Fetish"], 
-                label="Style", value="Neutral")
-            detail = gr.Slider(1, 5, step=1, value=3, label="Detail Level")
-            tags = gr.Textbox(label="Custom Tags (comma separated)")
-            model_choice = gr.Radio([
-                "CLIP", "BLIP", "DeepDanbooru", "NSFW Detector"], 
-                label="AI Model", value="BLIP")
-            btn_generate = gr.Button("Generate Text")
-
-        with gr.Column():
-            output = gr.Textbox(label="Generated Output", lines=8)
-            gr.Button("Enhance")
-            gr.Button("Shorten")
-            gr.Button("Rewrite")
-
-    btn_generate.click(get_output, 
-        inputs=[img, output_type, style, detail, tags, model_choice], 
-        outputs=output)
-
-    with gr.Tab("Prompt Tools"):
-        prompt_input = gr.Textbox(label="Prompt Builder")
-        btn_optimize = gr.Button("Optimize Prompt")
-        btn_random = gr.Button("Randomize")
-        optimized_output = gr.Textbox(label="Optimized Prompt")
-        btn_optimize.click(lambda p: p + ", ultra detailed", inputs=prompt_input, outputs=optimized_output)
-        btn_random.click(lambda: "a cyberpunk alley at night", outputs=optimized_output)
-
-    with gr.Tab("Training Data"):
-        btn_tags = gr.Button("Generate Tags")
-        tags_out = gr.Textbox(label="Training Tags")
-        btn_tags.click(lambda: "1girl, solo, black bodysuit, sitting", outputs=tags_out)
-
-        caption_mode = gr.Dropdown([
-            "Basic Caption", "Detailed Description", "Booru Style", "Natural Language"], 
-            label="Caption Generation")
-        btn_caption = gr.Button("Generate Caption")
-        caption_out = gr.Textbox(label="Training Caption")
-        btn_caption.click(lambda mode: {
-            "Basic Caption": "A woman posing for a photo",
-            "Detailed Description": "A woman in a futuristic city wearing a sleek bodysuit.",
-            "Booru Style": "1girl, bodysuit, city, night",
-            "Natural Language": "She stands still beneath neon lights, calm yet focused."
-        }.get(mode, ""), inputs=caption_mode, outputs=caption_out)
-
-        trigger_word = gr.Textbox(label="Trigger Word")
-        trigger_class = gr.Textbox(label="Class")
-        btn_lora = gr.Button("Prepare LoRA Training Data")
-        lora_out = gr.Textbox(label="LoRA Output")
-        btn_lora.click(lambda t, c: f"LoRA: {t}, class: {c}", inputs=[trigger_word, trigger_class], outputs=lora_out)
-
-app.launch()