Create app.py

app.py

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+# Import necessary libraries
+import gradio as gr    # For building the web interface
+from transformers import pipeline  # To use Hugging Face models
+
+# Load the DALL-E Mini model from Hugging Face
+# Using pipeline to handle the model. Note: 'dalle-mini' is lightweight and CPU-friendly
+generator = pipeline("text-to-image-generation", model="flax-community/dalle-mini")
+
+# Function to generate comic-style panels from a user's story description
+def generate_comic_panels(story_description):
+    # Break the story into key points (naive splitting; could use NLP techniques for better splitting)
+    scenes = story_description.split(". ")
+    
+    images = []
+    for scene in scenes:
+        # Generate an image for each scene using the loaded model
+        image = generator(scene)
+        images.append(image[0]["generated_image"])  # Get the generated image from the response
+    
+    return images
+
+# Set up the Gradio interface
+# User inputs their story description, and we generate images as a comic-style series
+demo = gr.Interface(
+    fn=generate_comic_panels,  # Function to be called when the user interacts
+    inputs=gr.Textbox(lines=5, placeholder="Enter your short story description here..."),  # User input
+    outputs=gr.Gallery(label="Generated Comic Panels").style(grid=[2]),  # Display images in a gallery format
+    title="GenArt Narrative",
+    description="Enter a short story description, and we'll transform it into a comic strip!"
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()
+