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import gradio as gr
import requests
from PIL import Image
import io
import os
# Define the API URL for the Craiyon model (lightweight text-to-image generator)
API_URL = "https://api-inference.huggingface.co/models/dalle-mini"
# Function to call the model and generate images
def generate_comic(prompt):
api_token = os.environ.get("API_TOKEN") # Securely access the token
headers = {"Authorization": f"Bearer {api_token}"}
# Sending request to the model with user prompt
response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
# Check if the response is successful
if response.status_code != 200:
return f"Error: {response.status_code}, {response.text}"
# Extracting the response data
images = response.json().get("generated_images", [])
if not images:
return "No images were generated, please try again with a different prompt."
# Process the images (assuming they are base64 encoded or URL)
pil_images = []
for img in images:
image_data = base64.b64decode(img)
image = Image.open(io.BytesIO(image_data))
pil_images.append(image)
return pil_images # Return the list of PIL images
# Gradio interface setup
def gradio_interface():
with gr.Blocks() as demo:
gr.Markdown("## GenArt Narrative - Turn Your Story into Comic Panels!")
prompt = gr.Textbox(label="Enter your short story description", placeholder="Once upon a time...")
output_gallery = gr.Gallery(label="Generated Comic Panels", columns=3, height=300)
submit_button = gr.Button("Generate Comic")
submit_button.click(fn=generate_comic, inputs=prompt, outputs=output_gallery)
return demo
# Run the Gradio app
if __name__ == "__main__":
app = gradio_interface()
app.launch()
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