app.py

import gradio as gr
from transformers import pipeline
from diffusers import StableDiffusionPipeline
import torch
import random

# Load your Stable Diffusion model (you can change the model if you want)
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")

# Text generation for "Rewrite" button
text_generator = pipeline("text-generation", model="gpt2")

# Function to generate image
def generate_image(prompt):
    image = pipe(prompt).images[0]
    return image

# Rewrite prompt
def rewrite_prompt(prompt):
    outputs = text_generator(f"Rephrase this: {prompt}", max_length=50, num_return_sequences=1)
    return outputs[0]['generated_text'].replace("Rephrase this: ", "")

# Enhance prompt
def enhance_prompt(prompt):
    return prompt + ", ultra detailed, 4K, high quality, cinematic lighting"

# Gradio Interface
with gr.Blocks(css="footer {display: none !important}") as demo:
    gr.Markdown("## 🎨 Text to Image Generator")
    with gr.Row():
        with gr.Column(scale=3):
            prompt = gr.Textbox(label="Enter your prompt here")
            with gr.Row():
                btn_generate = gr.Button("🎨 Generate")
                btn_rewrite = gr.Button("✍️ Rewrite")
                btn_enhance = gr.Button("✨ Enhance")
            output_image = gr.Image(label="Generated Image")

        with gr.Column(scale=1):
            gr.Markdown("### Prompt Editing")
            prompt_display = gr.Textbox(label="Modified Prompt", interactive=False)

    btn_generate.click(fn=generate_image, inputs=prompt, outputs=output_image)
    btn_rewrite.click(fn=rewrite_prompt, inputs=prompt, outputs=prompt_display)
    btn_enhance.click(fn=enhance_prompt, inputs=prompt, outputs=prompt_display)

demo.launch()