chore: Add Gradio-based image enhancement and captioning functionality

app.py

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+import gradio as gr
+import spaces
+import torch
+from gradio_client import Client, handle_file
+from colorama import Fore, Style
+from diffusers import AutoPipelineForImage2Image
+from PIL import Image
+
+joy_client = Client("fancyfeast/joy-caption-alpha-two")
+qwen_client = Client("Qwen/Qwen2.5-72B-Instruct")
+
+pipeline = AutoPipelineForImage2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+
+lora_ids = {
+    "Realism": "XLabs-AI/flux-RealismLora",
+    "Cartoonism": "aleksa-codes/flux-ghibsky-illustration",
+}
+
+def load_lora(lora_name):
+    print(f"Loading LoRA model: {lora_name}")
+    global pipeline
+    pipeline.unload_lora_weights()
+    pipeline.load_lora_weights(lora_ids[lora_name])
+    pipeline.enable_model_cpu_offload()
+    print(f"{Fore.GREEN}LoRA model loaded{Style.RESET_ALL}")
+
+def describe_image(image_path):
+    print(f"Describing image: {image_path}")
+    image_description = joy_client.predict(
+            input_image=handle_file(image_path),
+            caption_type="Descriptive",
+            caption_length="long",
+            extra_options=[],
+            name_input="",
+            custom_prompt="",
+            api_name="/stream_chat"
+    )[-1]
+    print(f"{Fore.GREEN}{image_description}{Style.RESET_ALL}")
+    return image_description
+
+def refine_prompt(image_description):
+    print(f"Improving prompt: {image_description}")
+    qwen_prompt = f"""This is the description of the image: {image_description}
+
+And those some good AI Art Prompts:
+- a cat on a windowsill gazing out at a starry night sky and distant city lights
+- a fisherman casting a line into a peaceful village lake surrounded by quaint cottages
+- cozy mountain cabin covered in snow, with smoke curling from the chimney and a warm, inviting light spilling through the windows
+- Mykonos
+- an orange Lamborghini driving down a hill road at night with a beautiful ocean view in the background, side view, no text
+- a small Yorkie on a windowsill during a snowy winter night, with a warm, cozy glow from inside and soft snowflakes drifting outside
+- serene Japanese garden with a koi pond and a traditional tea house, nestled under a canopy of cherry blossoms in full bloom
+- the most beautiful place in the universe
+
+Based on what i gave you, Write a great short AI Art Prompt for me that is based on the image description above (Don't write anything else, just the prompt)
+"""
+    refined_prompt = qwen_client.predict(
+            query=qwen_prompt,
+            history=[],
+            system="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
+            api_name="/model_chat"
+    )[1][0][-1]
+    print(f"{Fore.GREEN}{refined_prompt}{Style.RESET_ALL}")
+    return refined_prompt
+
+@spaces.GPU
+def img2img_infer(image_path, image_description):
+    pil_image = Image.open(image_path)
+    width, height = pil_image.size
+    enhanced_image = pipeline(f'GHIBSKY style, {image_description}', image=pil_image).images[0]
+    enhanced_image = enhanced_image.resize((width, height))
+    return enhanced_image
+
+
+with gr.Blocks(title="Magnific") as demo:
+    with gr.Row():
+        with gr.Column():
+            image_path = gr.Image(label="Image", type="filepath")
+            lora_dropdown = gr.Dropdown(label="LoRA Model", choices=list(lora_ids.keys()), value=None)
+            describe_btn = gr.Button(value="Describe Image", variant="primary")
+            with gr.Row(equal_height=True):
+                image_description = gr.Textbox(label="Image Description", scale=4)
+                refine_prompt_btn = gr.Button(value="Refine", variant="primary", scale=1)
+            submit_btn = gr.Button(value="Submit", variant="primary")
+        enhanced_image = gr.Image(label="Enhanced Image", type="pil")
+
+    lora_dropdown.change(load_lora, inputs=lora_dropdown)
+    refine_prompt_btn.click(refine_prompt, inputs=image_description, outputs=image_description)
+    describe_btn.click(describe_image, inputs=image_path, outputs=image_description)
+    submit_btn.click(img2img_infer, inputs=[image_path, image_description], outputs=enhanced_image)
+
+demo.queue().launch(share=False)