Update app.py

app.py

@@ -1,16 +1,11 @@
+import gradio 
 from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
 from diffusers.utils import load_image
 from PIL import Image
 import torch
 import numpy as np
 import cv2
-
-prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
-negative_prompt = 'low quality, bad quality, sketches'
-
-image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")
-
-controlnet_conditioning_scale = 0.5  # recommended for good generalization
+import os 
 
 controlnet = ControlNetModel.from_pretrained(
     "diffusers/controlnet-canny-sdxl-1.0",
@@ -25,14 +20,39 @@ pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
 )
 pipe.enable_model_cpu_offload()
 
-image = np.array(image)
-image = cv2.Canny(image, 100, 200)
-image = image[:, :, None]
-image = np.concatenate([image, image, image], axis=2)
-image = Image.fromarray(image)
+def infer(image_in):
+    prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
+    negative_prompt = 'low quality, bad quality, sketches'
+
+    image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")
+
+    controlnet_conditioning_scale = 0.5  # recommended for good generalization
+
+
+
+    image = np.array(image)
+    image = cv2.Canny(image, 100, 200)
+    image = image[:, :, None]
+    image = np.concatenate([image, image, image], axis=2)
+    image = Image.fromarray(image)
+
+    images = pipe(
+        prompt, negative_prompt=negative_prompt, image=image, controlnet_conditioning_scale=controlnet_conditioning_scale,
+        ).images
+
+    images[0].save(f"hug_lab.png")
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        image_in = gr.Image(source="upload", type="filepath")
+        prompt = gr.Textbox(label="Prompt")
+        submit_btn = gr.Button("Submit")
+        result = gr.Image(label="Result")
 
-images = pipe(
-    prompt, negative_prompt=negative_prompt, image=image, controlnet_conditioning_scale=controlnet_conditioning_scale,
-    ).images
+    submit_btn.click(
+        fn = infer,
+        inputs = [image_in, prompt],
+        outputs = [result]
+    )
 
-images[0].save(f"hug_lab.png")
+demo.queue().launch()