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import gradio as gr |
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import ai |
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import ai_tasks |
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import code_tasks |
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import custom_code |
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def open__get_text_from_url() -> str: |
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with open("code_tasks/text_in_url.py") as f: |
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return f.read() |
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def open__get_images_from_url() -> str: |
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with open("code_tasks/images_in_url.py") as f: |
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return f.read() |
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def open__get_image_infos() -> str: |
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with open("custom_code/image_analysis.py") as f: |
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return f.read() |
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def get_text_and_images_from_url(url): |
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return ( |
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code_tasks.text_in_url.get_text_from_url(url), |
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code_tasks.images_in_url.get_images_from_url(url), |
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) |
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def get_images_analysis(images): |
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return custom_code.image_analysis.analyze_images(eval(images)) |
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def summarize_text( |
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prompt, |
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url, |
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dimensions, |
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text, |
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images, |
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image_infos, |
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summary, |
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headline, |
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): |
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return ai_tasks.text_summary._summarize_text( |
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prompt, |
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url=url, |
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dimensions=dimensions, |
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text=text, |
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images=images, |
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image_infos=image_infos, |
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summary=summary, |
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headline=headline, |
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) |
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def get_headline_for_image( |
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prompt, |
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url, |
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dimensions, |
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text, |
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images, |
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image_infos, |
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summary, |
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headline, |
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): |
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import json |
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output = ai_tasks.headlines_for_images._get_headline_for_image( |
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prompt, |
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url=url, |
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dimensions=dimensions, |
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text=text, |
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images=images, |
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image_infos=image_infos, |
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summary=summary, |
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headline=headline, |
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) |
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return output, json.loads(output)["image_url"] |
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def get_headline_and_prompt( |
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prompt, |
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url, |
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dimensions, |
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text, |
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images, |
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image_infos, |
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summary, |
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headline, |
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): |
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import json |
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output = ai_tasks.headlines_for_ai_images._generate_headline_and_prompt( |
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prompt, |
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url=url, |
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dimensions=dimensions, |
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text=text, |
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images=images, |
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image_infos=image_infos, |
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summary=summary, |
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headline=headline, |
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) |
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output_dict = json.loads(output) |
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return ( |
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output, |
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output_dict["ai_prompt"], |
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output_dict["ai_prompt"], |
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output_dict["dimension_to_map"], |
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output_dict["dimension_to_map"], |
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) |
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def generate_image(prompt, dimensions): |
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return ai.image.urls(prompt, 1, dimensions)[0] |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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""" |
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## Scrape a website and get an ad |
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Enter an url and the dimensions for an image (eg, 300x600). |
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<br> A sequence of code and AI tasks will scrape the website and find an image that best fits those dimensions. They will also generate an AI image. |
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<br> It's your job to edit either of those images. |
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<br> A headline for your ad will also be generated. |
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<br> Play around with the AI tasks to get different results. Text in between {} are variables that you have access to. |
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""" |
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) |
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url = gr.Textbox(label="Input: {url}") |
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dimensions = gr.Textbox(label="Input: {dimensions}") |
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execute = gr.Button("Run") |
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with gr.Box(): |
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gr.Markdown("Code task") |
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with gr.Row(): |
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with gr.Column(): |
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gr.Textbox( |
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"write a python function that given an url returns all text in the website", |
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label="ChatGPT-4 prompt", |
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) |
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with gr.Accordion("Input: {url}", open=False): |
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gr.Code(open__get_text_from_url(), "python") |
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with gr.Column(): |
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text = gr.Textbox( |
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label="Output: {text}", lines=10, max_lines=10, interactive=False |
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) |
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with gr.Box(): |
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gr.Markdown("Code task") |
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with gr.Row(): |
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with gr.Column(): |
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gr.Textbox( |
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"write a python function that given an url returns all images in the website", |
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label="ChatGPT-4 prompt", |
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) |
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with gr.Accordion("Input: {url}", open=False): |
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gr.Code(open__get_images_from_url(), "python") |
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with gr.Column(): |
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images = gr.Textbox( |
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label="Output: {images}", lines=10, max_lines=10, interactive=False |
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) |
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with gr.Box(): |
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gr.Markdown("Custom code: analyze images with Google Vision") |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Accordion("Input: {images}", open=False): |
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gr.Code(open__get_image_infos(), "python") |
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with gr.Column(): |
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image_infos = gr.Textbox( |
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label="Output: {image_infos}", |
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lines=10, |
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max_lines=10, |
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interactive=False, |
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) |
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with gr.Box(): |
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gr.Markdown("AI task: summarize text") |
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with gr.Row(): |
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with gr.Column(): |
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summary_prompt = gr.Textbox( |
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ai_tasks.text_summary.PROMPT, |
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label="Instructions:", |
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interactive=True, |
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) |
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with gr.Column(): |
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summary = gr.Textbox( |
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label="Output: {summary}", lines=10, max_lines=10, interactive=False |
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) |
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with gr.Box(): |
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gr.Markdown("AI task: generate headline for image") |
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with gr.Row(): |
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with gr.Column(): |
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headline_prompt = gr.Textbox( |
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ai_tasks.headlines_for_images.PROMPT, |
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label="Instructions:", |
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interactive=True, |
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lines=20, |
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) |
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with gr.Column(): |
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headline = gr.Textbox( |
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label="Output: {headline}", |
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lines=10, |
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max_lines=10, |
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interactive=False, |
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) |
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headline_image = gr.Image(interactive=False) |
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with gr.Box(): |
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gr.Markdown("AI task: generate headline and prompt for image") |
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with gr.Row(): |
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with gr.Column(): |
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ai_prompt_prompt = gr.Textbox( |
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ai_tasks.headlines_for_ai_images.PROMPT, |
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label="Instructions:", |
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interactive=True, |
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) |
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with gr.Column(): |
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headline_and_prompt = gr.Textbox( |
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label="Output: {headline_prompt}", |
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lines=20, |
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max_lines=20, |
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interactive=False, |
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) |
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dimension_to_map = gr.Textbox( |
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label="Output: {dimension_to_map}", |
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interactive=False, |
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) |
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ai_prompt = gr.Textbox( |
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label="Output: {ai_prompt}", |
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interactive=False, |
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) |
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with gr.Box(): |
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gr.Markdown("AI task: generate image") |
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with gr.Row(): |
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with gr.Column(): |
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ai_image_prompt = gr.Textbox( |
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label="Instructions: {ai_prompt}", |
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interactive=False, |
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) |
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image_dimensions = gr.Textbox( |
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label="Input: {dimension_to_map}", |
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interactive=False, |
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) |
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with gr.Column(): |
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ai_image = gr.Image() |
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vars_ = [ |
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url, |
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dimensions, |
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text, |
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images, |
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image_infos, |
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summary, |
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headline, |
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] |
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execute.click( |
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get_text_and_images_from_url, inputs=[url], outputs=[text, images] |
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).success( |
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get_images_analysis, |
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inputs=[images], |
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outputs=[image_infos], |
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).success( |
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summarize_text, |
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inputs=[summary_prompt] + vars_, |
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outputs=[summary], |
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).success( |
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get_headline_for_image, |
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inputs=[headline_prompt] + vars_, |
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outputs=[headline, headline_image], |
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).success( |
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get_headline_and_prompt, |
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inputs=[ai_prompt_prompt] + vars_, |
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outputs=[ |
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headline_and_prompt, |
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ai_prompt, |
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ai_image_prompt, |
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dimension_to_map, |
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image_dimensions, |
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], |
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).success( |
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generate_image, inputs=[ai_image_prompt, image_dimensions], outputs=[ai_image] |
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) |
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demo.launch() |
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