Create app.py

app.py

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+import gradio as gr
+from PIL import Image, ImageDraw, ImageFont
+import random
+
+# Placeholder function for image generation (replace with actual model inference if desired)
+def generate_image(text_description):
+    # Create a blank image
+    img = Image.new('RGB', (512, 512), color=(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)))
+    draw = ImageDraw.Draw(img)
+    
+    # Add text to the image (simulating a generated image based on description)
+    try:
+        font = ImageFont.truetype("arial.ttf", 40)
+    except:
+        font = ImageFont.load_default()
+    
+    # Wrap text description if too long
+    text = text_description if len(text_description) < 30 else text_description[:27] + "..."
+    draw.text((20, 20), f"Generated: {text}", font=font, fill=(255, 255, 255))
+    
+    # Here you could integrate a real model like Stable Diffusion
+    # Example (commented out):
+    # from diffusers import StableDiffusionPipeline
+    # pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+    # img = pipe(text_description).images[0]
+    
+    return img
+
+# Gradio interface
+with gr.Blocks(title="Text-to-Image Generator") as demo:
+    gr.Markdown("# Text-to-Image Generator")
+    gr.Markdown("Enter a description below and generate an image!")
+    
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(label="Description", placeholder="Type your image description here...")
+            generate_btn = gr.Button("Generate Image")
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image")
+    
+    # Connect the button to the function
+    generate_btn.click(fn=generate_image, inputs=text_input, outputs=output_image)
+
+# Launch the app
+demo.launch()