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| import gradio as gr | |
| import os | |
| hf_token = os.environ.get('HF_TOKEN') | |
| from gradio_client import Client | |
| client = Client("https://fffiloni-test-llama-api.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): | |
| 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 | |
| 6, # 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, from i want you to provide a story that would fit well with the image: | |
| '{clipi_result}' | |
| """ | |
| result = client.predict( | |
| llama_q, # str in 'Message' Textbox component | |
| api_name="/predict" | |
| ) | |
| print(f"Llama2 result: {result}") | |
| result = get_text_after_colon(result) | |
| return clipi_result, result | |
| css=""" | |
| #col-container {max-width: 910px; margin-left: auto; margin-right: auto;} | |
| a {text-decoration-line: underline; font-weight: 600;} | |
| """ | |
| 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 ! | |
| """ | |
| ) | |
| image_in = gr.Image(label="Image input", type="filepath") | |
| submit_btn = gr.Button('Sumbit') | |
| caption = gr.Textbox(label="Generated Caption") | |
| story = gr.Textbox(label="generated Story") | |
| submit_btn.click(fn=infer, inputs=[image_in], outputs=[caption, story]) | |
| demo.queue().launch() | |