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README.md

@@ -1,12 +1,6 @@
 ---
-title: PicPilot UI
-emoji: 🐨
-colorFrom: pink
-colorTo: indigo
+title: PicPilot-UI
+app_file: ui.py
 sdk: gradio
 sdk_version: 4.36.1
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

ui.py

@@ -0,0 +1,113 @@
+import gradio as gr
+import requests
+from pydantic import BaseModel
+from diffusers.utils import load_image
+from io import BytesIO
+
+
+
+sdxl_inference_endpoint = 'https://vikramsingh178-picpilot-server.hf.space/api/v1/product-diffusion/sdxl_v0_lora_inference'
+sdxl_batch_inference_endpoint = 'https://vikramsingh178-picpilot-server.hf.space/api/v1/product-diffusion/sdxl_v0_lora_inference/batch'
+kandinsky_inpainting_inference = 'https://vikramsingh178-picpilot-server.hf.space/api/v1/product-diffusion/kandinskyv2.2_inpainting'
+
+# Define the InpaintingRequest model
+class InputRequest(BaseModel):
+    prompt: str
+    num_inference_steps: int
+    guidance_scale: float
+    negative_prompt: str
+    num_images: int
+    mode: str
+
+class InpaintingRequest(BaseModel):
+    prompt: str
+    negative_prompt: str
+    num_inference_steps: int
+    strength: float
+    guidance_scale: float
+    mode: str
+
+async def generate_sdxl_lora_image(prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode):
+    # Prepare the payload for SDXL LORA API
+    payload = InputRequest(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        num_images=num_images,
+        mode=mode
+    ).dict()
+    
+    response = requests.post(sdxl_inference_endpoint, json=payload)
+    response = response.json()
+    url = response['url']
+    image = load_image(url)
+    return image
+
+
+
+def generate_outpainting(prompt, negative_prompt, num_inference_steps, strength, guidance_scale, mode, image):
+    # Convert the image to bytes
+    img_byte_arr = BytesIO()
+    image.save(img_byte_arr, format='PNG')
+    img_byte_arr = img_byte_arr.getvalue()
+    
+    # Prepare the payload for multipart/form-data
+    files = {
+        'image': ('image.png', img_byte_arr, 'image/png'),
+        'prompt': (None, prompt),
+        'negative_prompt': (None, negative_prompt),
+        'num_inference_steps': (None, str(num_inference_steps)),
+        'strength': (None, str(strength)),
+        'guidance_scale': (None, str(guidance_scale)),
+        'mode': (None, mode)
+    }
+    
+    response = requests.post(kandinsky_inpainting_inference, files=files)
+    response.raise_for_status()
+    response = response.json()
+    url = response['url']
+    image = load_image(url)
+    return image
+
+
+
+with gr.Blocks(theme='VikramSingh178/Webui-Theme') as demo:
+    with gr.Tab("SdxL-Lora"):
+       with gr.Row():
+            with gr.Column():
+              with gr.Group():
+                prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
+                negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
+                num_inference_steps = gr.Slider(minimum=1, maximum=1000, step=1, value=20, label="Inference Steps")
+                guidance_scale = gr.Slider(minimum=1.0, maximum=10.0, step=0.1, value=7.5, label="Guidance Scale")
+                num_images = gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Number of Images")
+                mode = gr.Dropdown(choices=["s3_json", "b64_json"], value="s3_json", label="Mode")
+                generate_button = gr.Button("Generate Image",variant='primary')
+            
+            with gr.Column(scale=1):
+              
+                image_preview = gr.Image(label="Generated Image",show_download_button=True,show_share_button=True,container=True)
+                generate_button.click(generate_sdxl_lora_image, inputs=[prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode], outputs=[image_preview])
+
+    with gr.Tab("Generate AI Background"):
+        with gr.Row():
+            with gr.Column():
+              with gr.Group():
+                image_input = gr.Image(type="pil", label="Upload Image")
+                prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
+                negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
+                num_inference_steps = gr.Slider(minimum=1, maximum=500, step=1, value=20, label="Inference Steps")
+                guidance_scale = gr.Slider(minimum=1.0, maximum=10.0, step=0.1, value=7.5, label="Guidance Scale")
+                strength = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=1, label="Strength")
+                mode = gr.Dropdown(choices=["s3_json", "b64_json"], value="s3_json", label="Mode")
+                generate_button = gr.Button("Generate Background", variant='primary')
+
+            with gr.Column(scale=1):
+            
+                image_preview = gr.Image(label="Image", show_download_button=True, show_share_button=True, container=True)
+                generate_button.click(generate_outpainting, inputs=[prompt, negative_prompt, num_inference_steps, strength, guidance_scale, mode, image_input], outputs=[image_preview])
+        
+demo.launch()
+
+