Create app.py

app.py

@@ -0,0 +1,108 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from diffusers import DiffusionPipeline
+import torch
+import re
+import time
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+models = {
+    "Normal": "Qwen/Qwen3-0.6B",
+    "Thinking": "Qwen/Qwen2.5-1.5B-Instruct"
+}
+
+loaded_models = {}
+loaded_tokenizers = {}
+
+image_model_id = "SimianLuo/LCM_Dreamshaper_v7"
+image_pipe = DiffusionPipeline.from_pretrained(image_model_id)
+image_pipe.to(device)
+
+def get_chat_model(mode):
+    model_id = models[mode]
+    if model_id not in loaded_models:
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_id,
+            torch_dtype="auto",
+            device_map="auto"
+        )
+        loaded_models[model_id] = model
+        loaded_tokenizers[model_id] = tokenizer
+    return loaded_models[model_id], loaded_tokenizers[model_id]
+
+def chat_logic(user_input, mode):
+    model, tokenizer = get_chat_model(mode)
+    messages = [{"role": "user", "content": user_input}]
+    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    generated_ids = model.generate(**model_inputs, max_new_tokens=1024)
+    generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    cleaned_response = re.sub(r'<think>.*?</think>\s*\n?', '', response, flags=re.DOTALL)
+    return cleaned_response.strip()
+
+def image_logic(prompt, width, height, steps):
+    start_time = time.time()
+    final_prompt = f"{prompt}, centered and realistic (if applicable)"
+    yield "💥 IGNITING... (Image generator AI)...", None
+    
+    image = image_pipe(
+        prompt=final_prompt,
+        width=int(width),
+        height=int(height),
+        num_inference_steps=int(steps),
+        guidance_scale=8.0,
+        lcm_origin_steps=50,
+        output_type="pil"
+    ).images[0]
+    
+    duration = round(time.time() - start_time, 2)
+    yield f"💥 EXPLODED in {duration}s", image
+
+creeper_css = """
+body { background-color: #000000; }
+.gradio-container { background-color: #1e1e1e; border: 10px solid #2e8b57 !important; font-family: 'Courier New', Courier, monospace; color: #00ff00; }
+footer { display: none !important; }
+.gr-button-primary { background-color: #4A7023 !important; border: 4px solid #000 !important; color: white !important; font-weight: bold; text-transform: uppercase; }
+.gr-button-primary:hover { background-color: #5ea032 !important; box-shadow: 0 0 20px #2e8b57; }
+label span { color: #2e8b57 !important; font-weight: bold; font-size: 1.2em; }
+textarea, input, .gr-box, .gr-input { background-color: #2e2e2e !important; color: #00ff00 !important; border: 3px solid #4A7023 !important; }
+.tabs { border-bottom: 5px solid #4A7023 !important; }
+.tab-nav button.selected { background-color: #4A7023 !important; color: white !important; }
+"""
+
+with gr.Blocks(css=creeper_css, title="CREEPER AI HUB") as demo:
+    gr.Markdown("# 🟩 CREEPER AI HUB 🟩")
+    
+    with gr.Tabs():
+        with gr.TabItem("SSSSS-CHAT"):
+            gr.Markdown("### Qwen Chat System")
+            with gr.Row():
+                mode_radio = gr.Radio(choices=["Normal", "Thinking"], value="Normal", label="Select Brain Mode")
+            with gr.Column():
+                chat_input = gr.Textbox(lines=4, placeholder="Ssssss... Talk to the Creeper...", label="Message")
+                chat_output = gr.Textbox(label="Creeper Says")
+                chat_btn = gr.Button("EXPLODE TEXT", variant="primary")
+                
+            chat_btn.click(fn=chat_logic, inputs=[chat_input, mode_radio], outputs=chat_output)
+            
+        with gr.TabItem("TNT-IMAGE"):
+            gr.Markdown("### Image Generator System")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    img_prompt = gr.Textbox(label="Visual Idea", placeholder="A pixelated forest...", lines=3)
+                    with gr.Row():
+                        w_slider = gr.Slider(256, 768, 512, step=64, label="Block Width")
+                        h_slider = gr.Slider(256, 768, 512, step=64, label="Block Height")
+                    s_slider = gr.Slider(4, 12, 5, step=1, label="Detonation Steps")
+                    img_btn = gr.Button("EXPLODE IMAGE", variant="primary")
+                with gr.Column(scale=1):
+                    img_status = gr.Markdown("### Status: 🟢 Armed")
+                    img_output = gr.Image(label="Rendered Loot")
+                    
+            img_btn.click(fn=image_logic, inputs=[img_prompt, w_slider, h_slider, s_slider], outputs=[img_status, img_output])
+
+if __name__ == "__main__":
+    demo.launch()