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import gradio as gr
from inference_code import generate_images
def generate_image_predictions(prompt):
images = generate_images(prompt)
return images
demo = gr.Blocks()
with demo:
gr.Markdown(
"""
# 🌍 Map Diffuser
🌏 Generates images from a given text prompt. The prompts are in the format:
`{style} map of {city} with {features}` or
`satellite image of {city} with {features}` or
`satellite image with {features}` or
`satellite image of {city} with {features} and no {features}`
and so on...
So for example:
- "Satellite image of amsterdam with industrial area and highways"
- "Watercolor style map of Amsterdam with residential area and highways"
- "Toner style map of Amsterdam with residential area and highways"
- "Satellite image with forests and residential, no water"
Examples table:
| Prompt | Output |
| --- | --- |
| Satellite image of amsterdam with industrial area and highways | <img src="https://i.imgur.com/vrGpk45.png" width="300" /> |
| Watercolor style map of Amsterdam with residential area and highways | <img src="https://i.imgur.com/AQS34dk.png" width="300" /> |
| Toner style map of Amsterdam with residential area and highways | <img src="https://i.imgur.com/X8VcezT.png" width="300" /> |
| Satellite image with forests and residential, no water | <img src="https://i.imgur.com/MEccHdM.png" width="300" /> |
"""
)
input = gr.components.Textbox(label="Enter a text prompt here")
output = gr.components.Image(label="Output Image")
# button to submit the prompt
button = gr.components.Button(label="Generate")
# when the button is clicked, call the generate_image_predictions function
# and pass in the prompt as an argument
button.click(generate_image_predictions, inputs=input, outputs=output)
demo.launch()
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