Update app.py

app.py

@@ -1,7 +1,59 @@
 import gradio as gr
+import torch
+from diffusers import FluxPipeline
+from PIL import Image
+import spaces
 
-def greet(name):
-    return "Hello " + name + "!!"
+@spaces.GPU
+def load_flux_model():
+    pipe = FluxPipeline.from_pretrained(
+        "black-forest-labs/FLUX.1-dev", 
+        torch_dtype=torch.bfloat16
+    ).to("cuda")
+    return pipe
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+flux_pipe = load_flux_model()
+
+def generate_design(images, apparel_type, guidance_scale=7.0, steps=30, num_outputs=1):
+    if not images:
+        raise gr.Error("Please upload at least one image.")
+
+    # Create a reference embedding by averaging CLIP image embeddings
+    # This gives the model a style/content reference
+    ref_embeds = []
+    for img in images:
+        img = img.convert("RGB")
+        embed = flux_pipe.encode_image(img)
+        ref_embeds.append(embed)
+    combined_embed = torch.mean(torch.stack(ref_embeds), dim=0)
+
+    prompt = f"Design a {apparel_type} inspired by the given references, fashionable, photorealistic, detailed fabric textures."
+
+    outputs = flux_pipe(
+        prompt=prompt,
+        image_embeds=combined_embed,  # use custom conditioning
+        guidance_scale=guidance_scale,
+        num_inference_steps=steps,
+        num_images_per_prompt=num_outputs
+    ).images
+
+    return outputs
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Fashion Design Generator 👗✨")
+
+    with gr.Row():
+        with gr.Column():
+            images = gr.Image(type="pil", label="Upload reference images", tool="editor", image_mode="RGB", sources=["upload"], elem_id="multi_upload", interactive=True, value=None, every=1, container=True, show_label=True, mirror_webcam=False, height=300, allow_multiple=True)
+            apparel_type = gr.Textbox(label="Type of Apparel", placeholder="e.g., summer dress, jacket, kurta")
+            guidance = gr.Slider(1, 15, 7.0, 0.1, label="Guidance Scale")
+            steps = gr.Slider(10, 50, 30, 1, label="Steps")
+            num_outputs = gr.Slider(1, 4, 1, 1, label="Number of Designs")
+            btn = gr.Button("Generate Designs", variant="primary")
+        with gr.Column():
+            gallery = gr.Gallery(label="Generated Designs", height=300)
+
+    btn.click(generate_design, inputs=[images, apparel_type, guidance, steps, num_outputs], outputs=gallery)
+
+if __name__ == "__main__":
+    demo.launch()