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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 | |
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, from i want you to provide a story that would fit well with the image: | |
'{clipi_result[0]}' | |
""" | |
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 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( | |
""" | |
<h1 style="text-align: center">Image to Story</h1> | |
<p style="text-align: center">Upload an image, get a story made by Llama2 !</p> | |
""" | |
) | |
with gr.Row(): | |
with gr.Column(): | |
image_in = gr.Image(label="Image input", type="filepath") | |
submit_btn = gr.Button('Tell me a story') | |
with gr.Column(): | |
#caption = gr.Textbox(label="Generated Caption") | |
story = gr.Textbox(label="generated Story") | |
submit_btn.click(fn=infer, inputs=[image_in], outputs=[story]) | |
demo.queue().launch() | |