Update app.py

app.py

@@ -1,44 +1,23 @@
-import gradio as gr
+pip install diffusers accelerate safetensors transformers
 
-# Define the main chat response function.
-def chat_response(user_input):
-    # Your chat response logic here.
-    return "Hello, I am a chatbot. How can I help you?"
+import PIL
+import requests
+import torch
+from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
 
-# Create the Gradio interface.
-iface = gr.Interface(
-    fn=chat_response,
-    inputs=gr.Textbox(label="User Input"),
-    outputs=gr.Textbox(label="Chatbot Response"),
-)
+model_id = "timbrooks/instruct-pix2pix"
+pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)
+pipe.to("cuda")
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
 
-# Add the code editor and debug screen.
-code_editor = gr.Code(label="Code Editor")
-debug_screen = gr.Textbox(label="Debug Screen")
+url = "https://raw.githubusercontent.com/timothybrooks/instruct-pix2pix/main/imgs/example.jpg"
+def download_image(url):
+    image = PIL.Image.open(requests.get(url, stream=True).raw)
+    image = PIL.ImageOps.exif_transpose(image)
+    image = image.convert("RGB")
+    return image
+image = download_image(url)
 
-# Define the function to execute the Python code.
-def execute_code(code):
-    try:
-        # Execute the code and capture the output.
-        output = eval(code)
-        debug_screen.update(f"Code executed successfully.\nOutput: {output}")
-    except Exception as e:
-        debug_screen.update(f"Error executing code: {e}")
-
-# Create the Gradio interface for the code execution.
-code_execution_iface = gr.Interface(
-    fn=execute_code,
-    inputs=code_editor,
-    outputs=debug_screen,
-)
-
-# Add the screens to the main interface.
-iface.cache_examples(
-    [
-        ("What is the capital of France?", "Paris"),
-        ("What is the square root of 16?", 4),
-    ]
-)
-
-# Launch the Gradio interface.
-iface.launch()
+prompt = "turn him into cyborg"
+images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images
+images[0]