create app.py

app.py

@@ -0,0 +1,52 @@
+
+import PIL
+import requests
+import torch
+import gradio as gr
+import random
+from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
+
+model_id = "timbrooks/instruct-pix2pix"
+pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16", safety_checker=None)
+pipe.to("cuda")
+pipe.enable_attention_slicing()
+
+#image = PIL.Image.open("./example.png")
+#image = PIL.ImageOps.exif_transpose(image)
+#image = image.convert("RGB")
+#image
+#prompt = "turn him into cyborg"
+#pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1).images[0]
+
+counter = 0
+
+def chat(image_in, message, history): #, progress=gr.Progress(track_tqdm=True)):
+    #progress(0, desc="Starting...")
+    global counter 
+    counter += 1
+    #if message == "revert": --to add revert functionality later
+    if counter > 1:
+      # Open the image
+      image_in = Image.open("./edited_image.png")
+    prompt = message #eg - "turn him into cyborg"
+    edited_image = pipe(prompt, image=image_in, num_inference_steps=20, image_guidance_scale=1).images[0]
+    edited_image.save("edited_image.png") #("./edited_image.png")
+    history = history or []
+    add_text_list = ["There you go", "Enjoy your image!", "Nice work! Wonder what you gonna do next!", "Way to go!", "Does this work for you?", "Something like this?"]
+    #Resizing the image for better display
+    response =  random.choice(add_text_list) + '<img src="/file=edited_image.png" style="width: 200px; height: 200px;">'
+    history.append((message, response))
+    return history, history
+
+with gr.Blocks() as demo:
+    with gr.Row():
+      with gr.Column():
+        image_in = gr.Image(type='pil', label="Original Image")
+        text_in = gr.Textbox()
+        state_in = gr.State()
+        b1 = gr.Button('Edit the image!')
+      chatbot = gr.Chatbot() 
+    b1.click(chat,[image_in, text_in, state_in], [chatbot, state_in])
+
+#demo.queue()
+demo.launch(debug=True, width="80%", height=1500)