app.py

app.py

@@ -0,0 +1,58 @@
+import torch
+from diffusers import StableDiffusionInstructPix2PixPipeline
+from diffusers.utils import load_image
+from PIL import Image as im
+import requests
+import io
+import gradio as gr
+
+API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image"
+headers = {"Authorization": "Bearer HF_TOKEN"}
+
+model_id = "instruction-tuning-sd/cartoonizer"
+pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+    model_id, torch_dtype=torch.float16, use_auth_token=True
+  ).to("cuda")
+
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.content
+
+def cartoonizer(input_img,bg_prompt):
+  if input_img is not None:
+    data = im.fromarray(input_img)
+    data = data.resize((300,300))
+    org_image = load_image(data)
+    cart_image = pipeline("Cartoonize the following image", image=org_image).images[0]
+    if len(bg_prompt) !=0:
+      image_bytes = query({
+        "inputs": bg_prompt,
+      })
+    else:
+      image_bytes = query({
+        "inputs": "orange background image",
+      })
+    bg_image = im.open(io.BytesIO(image_bytes))
+
+    return [cart_image,bg_image]
+  else:
+    gr.Warning("Please upload an Input Image!")
+    return [input_img,input_img]
+
+
+with gr.Blocks(theme = gr.themes.Citrus()) as cart:
+  gr.HTML("""<h1 align="center">Cartoonize your Image with best backgrounds!</h1>""")
+  with gr.Tab("Cartoonize"):
+    with gr.Row():
+      image_input = gr.Image()
+      image_output = gr.Image()
+      text_img_output = gr.Image()
+
+    txt_label = gr.Label("Enter your photo frame description:")
+    txt_input = gr.Textbox()
+    image_btn = gr.Button("Convert")
+
+  image_btn.click(cartoonizer,inputs = [image_input,txt_input],outputs=[image_output,text_img_output])
+
+
+cart.launch()