Spaces:
Paused
Paused
File size: 2,306 Bytes
56458a8 24c50a4 7175dd2 dd17729 afc2656 56458a8 dd17729 522e040 dd17729 a5c9736 dd17729 522e040 dd17729 522e040 56458a8 7175dd2 fec39f8 3b617f3 7175dd2 afc2656 20c0b53 56458a8 e290e46 56458a8 8f36174 22fd767 1b02803 20c0b53 1b02803 20c0b53 56458a8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
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()
|