import gradio as gr from share_btn import community_icon_html, loading_icon_html, share_js import re import os hf_token = os.environ.get('HF_TOKEN') from gradio_client import Client client = Client("https://fffiloni-test-llama-api-debug.hf.space/", hf_token=hf_token) clipi_client = Client("https://fffiloni-clip-interrogator-2.hf.space/") def get_text_after_colon(input_text): # Find the first occurrence of ":" colon_index = input_text.find(":") # Check if ":" exists in the input_text if colon_index != -1: # Extract the text after the colon result_text = input_text[colon_index + 1:].strip() return result_text else: # Return the original text if ":" is not found return input_text def infer(image_input, audience): gr.Info('Calling CLIP Interrogator ...') clipi_result = clipi_client.predict( image_input, # str (filepath or URL to image) in 'parameter_3' Image component "best", # str in 'Select mode' Radio component 4, # int | float (numeric value between 2 and 24) in 'best mode max flavors' Slider component api_name="/clipi2" ) print(clipi_result) llama_q = f""" I'll give you a simple image caption, please provide a fictional story for a {audience} audience that would fit well with the image. Please be creative, do not worry and only generate a cool fictional story. Here's the image description: '{clipi_result[0]}' """ gr.Info('Calling Llama2 ...') result = client.predict( llama_q, # str in 'Message' Textbox component "I2S", api_name="/predict" ) print(f"Llama2 result: {result}") result = get_text_after_colon(result) # Split the text into paragraphs based on actual line breaks paragraphs = result.split('\n') # Join the paragraphs back with an extra empty line between each paragraph formatted_text = '\n\n'.join(paragraphs) return formatted_text, gr.Group.update(visible=True) css=""" #col-container {max-width: 910px; margin-left: auto; margin-right: auto;} a {text-decoration-line: underline; font-weight: 600;} a {text-decoration-line: underline; font-weight: 600;} .animate-spin { animation: spin 1s linear infinite; } @keyframes spin { from { transform: rotate(0deg); } to { transform: rotate(360deg); } } #share-btn-container { display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; } div#share-btn-container > div { flex-direction: row; background: black; align-items: center; } #share-btn-container:hover { background-color: #060606; } #share-btn { all: initial; color: #ffffff; font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important; right:0; } #share-btn * { all: unset; } #share-btn-container div:nth-child(-n+2){ width: auto !important; min-height: 0px !important; } #share-btn-container .wrap { display: none !important; } #share-btn-container.hidden { display: none!important; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown( """

Image to Story

Upload an image, get a story made by Llama2 !

""" ) with gr.Row(): with gr.Column(): image_in = gr.Image(label="Image input", type="filepath", elem_id="image-in", height=420) audience = gr.Radio(label="Target Audience", choices=["Children", "Adult"], value="Children") submit_btn = gr.Button('Tell me a story') with gr.Column(): #caption = gr.Textbox(label="Generated Caption") story = gr.Textbox(label="generated Story", elem_id="story") with gr.Group(elem_id="share-btn-container", visible=False) as share_group: community_icon = gr.HTML(community_icon_html) loading_icon = gr.HTML(loading_icon_html) share_button = gr.Button("Share to community", elem_id="share-btn") gr.Examples(examples=[["./examples/crabby.png", "Children"],["./examples/hopper.jpeg", "Adult"]], fn=infer, inputs=[image_in, audience], outputs=[story, share_group], cache_examples=True ) submit_btn.click(fn=infer, inputs=[image_in, audience], outputs=[story, share_group]) share_button.click(None, [], [], _js=share_js) demo.queue(max_size=12).launch()