Upload app.py

app.py

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+import gradio as gr
+from PIL import Image, ImageDraw, ImageFont
+import numpy as np
+import torch
+from data import transform_img 
+from inference import load_model, predict
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+weights_path = "unet_model.pth"
+model = load_model(weights_path, device)
+
+def process_image(image, text, font_size):
+    image = image.convert("RGB")
+    print(f"image: {image}")
+    background_with_text = image.copy()
+    draw = ImageDraw.Draw(background_with_text)
+    font = ImageFont.truetype("/usr/share/fonts/truetype/freefont/FreeSansBold.ttf", font_size)
+    text_position = (50, 50)
+    text_color = (0, 0, 0)
+    draw.text(text_position, text, fill=text_color, font=font)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    weights_path = "unet_model.pth"
+    model = load_model(weights_path, device)
+    transform = transform_img()
+    image_tensor = transform(image).unsqueeze(0) 
+    mask = predict(model, image_tensor, device)
+    mask = mask.squeeze(0)
+    mask_binary = (mask > 0.5).astype(np.uint8) * 255
+    mask_img = Image.fromarray(mask_binary, mode="L")
+    mask_img = mask_img.resize(image.size, resample=Image.NEAREST)
+
+    original_rgba = image.convert("RGBA")
+
+    r, g, b, _ = original_rgba.split()
+    subject_img = Image.merge("RGBA", (r, g, b, mask_img))
+
+    background_with_text.paste(subject_img, (0, 0), subject_img)
+    return background_with_text
+
+interface = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Textbox(label="Enter Text"),
+        gr.Slider(10, 70, value=5, step=5, label="Font Size")
+    ],
+    outputs=gr.Image(type="pil", label="Output Image"),
+    title="Text Behind Image Generator",
+    description="Upload an image, enter text, and choose font size to generate the output image."
+)
+
+interface.launch()