app.py

import gradio as gr
from PIL import Image
from utils import generate_thumbnail

def process(image, text, font_size, position, text_color):
    result = generate_thumbnail(image, text, font_size=font_size, position=position, text_color=text_color)
    return result

demo = gr.Interface(
    fn=process,
    inputs=[
        gr.Image(type="pil", label="Upload Background Image"),
        gr.Textbox(label="Thumbnail Text"),
        gr.Slider(20, 100, step=5, value=60, label="Font Size"),
        gr.Radio(["top", "center", "bottom"], label="Text Position", value="bottom"),
        gr.ColorPicker(label="Text Color", value="#FFFFFF"),
    ],
    outputs=gr.Image(label="Generated Thumbnail"),
    title="🖼️ AI Thumbnail Generator",
    description="Upload an image and generate a custom thumbnail with your text.",
    allow_flagging="never"
)

if __name__ == "__main__":
    demo.launch()

    from diffusers import StableDiffusionPipeline
import torch
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Prompt(BaseModel):
    prompt: str

# Load once
pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float16,
    use_safetensors=True,
    revision="fp16"
).to("cuda" if torch.cuda.is_available() else "cpu")

@app.post("/generate")
def generate_image(data: Prompt):
    image = pipe(data.prompt).images[0]
    image.save("output.png")
    return {"message": "Image saved as output.png"}