app.py

import gradio as gr
import torch
from transformers import AutoModelForSequenceToSequence, AutoTokenizer
from PIL import Image
from torchvision import transforms

# Load the Text-to-Image model
image_model = AutoModelForSequenceToSequence.from_pretrained("artificialguybr/CuteCartoonRedmond-V2")
image_tokenizer = AutoTokenizer.from_pretrained("artificialguybr/CuteCartoonRedmond-V2")

# Load the Text Generation model
text_model = AutoModelForSequenceToSequence.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
text_tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")

# Define a function to generate an image from text
def generate_image(text):
    inputs = image_tokenizer(text, return_tensors="pt")
    output = image_model.generate(inputs["input_ids"], attention_mask=inputs["attention_mask"])
    image = Image.fromarray(output[0].detach().numpy())
    return image

# Define a function to generate text from text
def generate_text(text):
    inputs = text_tokenizer(text, return_tensors="pt")
    output = text_model.generate(inputs["input_ids"], attention_mask=inputs["attention_mask"])
    return text_tokenizer.decode(output[0], skip_special_tokens=True)

# Create a Gradio interface
demo = gr.Interface(
    fn=lambda text: {"image": generate_image(text), "text": generate_text(text)},
    inputs="text",
    outputs=["image", "text"],
    title="Text-to-Image and Text Generation",
    description="Enter a prompt to generate both an image and text!"
)

# Launch the Gradio app
demo.launch()