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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")
gr.Examples(examples=[["./examples/crabby.png"],["./examples/hopper.jpeg"]],
fn=infer,
inputs=[image_in],
outputs=[story],
cache_examples=True
)
submit_btn.click(fn=infer, inputs=[image_in], outputs=[story])
demo.queue(max_size=12).launch()
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