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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"}
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