Create app.py

app.py

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+import gradio as gr
+import requests
+from PIL import Image
+import io
+
+# 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):
+    headers = {"Authorization": "Bearer YOUR_HUGGINGFACE_API_TOKEN"}
+    
+    # Sending request to the model with user prompt
+    response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
+    
+    # Checking for successful response
+    if response.status_code != 200:
+        return f"Error: {response.status_code}, {response.text}"
+    
+    # Extracting generated images
+    images = response.json()["generated_images"]
+    
+    # Convert image bytes into PIL Image format
+    pil_images = [Image.open(io.BytesIO(img)) for img in images]
+    
+    return pil_images  # Return the list of PIL images
+
+# Gradio interface setup
+def gradio_interface():
+    # Set up input and output elements
+    with gr.Blocks() as demo:
+        gr.Markdown("## GenArt Narrative - Turn Your Story into Comic Panels!")
+        
+        with gr.Row():
+            prompt = gr.Textbox(label="Enter your short story description", placeholder="Once upon a time...")
+        
+        output_gallery = gr.Gallery(label="Generated Comic Panels").style(grid=[3], height="300px")
+        
+        submit_button = gr.Button("Generate Comic")
+        
+        # Set up button functionality
+        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()
+