File size: 3,928 Bytes
7807c8e dfe1acb 7807c8e dfe1acb 7807c8e dfe1acb 7807c8e 95849dc 5bc2c51 7807c8e | 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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | import streamlit as st
import requests
import base64
from typing import Iterator
import os
from text_generation import Client
from deep_translator import GoogleTranslator
model_id = os.environ.get("CODE", None)
API_URL = "https://api-inference.huggingface.co/models/" + model_id
HF_TOKEN = os.environ.get("HF_TOKEN", None)
client = Client(
API_URL,
headers={"Authorization": f"Bearer {HF_TOKEN}"},
)
EOS_STRING = "</s>"
EOT_STRING = "<EOT>"
translator_to_en = GoogleTranslator(source='arabic', target='english')
translator_to_ar = GoogleTranslator(source='english', target='arabic')
def get_prompt(message: str, chat_history: list[tuple[str, str]],
system_prompt: str) -> str:
texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
do_strip = False
for user_input, response in chat_history:
user_input = user_input.strip() if do_strip else user_input
do_strip = True
texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
message = message.strip() if do_strip else message
texts.append(f'{message} [/INST]')
return ''.join(texts)
def run(message: str,
chat_history: list[tuple[str, str]],
system_prompt: str,
max_new_tokens: int = 1024,
temperature: float = 0.1,
top_p: float = 0.9,
top_k: int = 50) -> Iterator[str]:
prompt = get_prompt(message, chat_history, system_prompt)
generate_kwargs = dict(
max_new_tokens=max_new_tokens,
do_sample=True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
)
stream = client.generate_stream(prompt, **generate_kwargs)
output = ""
for response in stream:
if any([end_token in response.token.text for end_token in [EOS_STRING, EOT_STRING]]):
translated_output = translator_to_ar.translate(output)
yield translated_output
output = ""
else:
output += response.token.text
def generate_image_caption(image_data):
image_base64 = base64.b64encode(image_data).decode('utf-8')
payload = {"data": ["data:image/jpeg;base64," + image_base64]}
response = requests.post("https://ashrafb-salesforce-blip-image-captioning-base.hf.space/run/predict", json=payload)
if response.status_code == 200:
caption = response.json()["data"][0]
return caption
else:
return "Error: Unable to generate caption"
def main():
st.markdown('<p style="color:crimson;text-align:center;font-size:30px;">Aiconvert.online img2story</p>', unsafe_allow_html=True)
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
image_data = uploaded_file.read()
st.image(image_data, caption="Uploaded Image.", use_column_width=True)
if st.button("Generate Story"):
system_prompt = "write attractive story in 300 words about"
if uploaded_file is not None:
caption = generate_image_caption(image_data)
if caption.startswith("Error"):
st.error(caption)
return
with st.spinner("Generating story..."): # Adding a spinner while generating the story
ai_response = next(run(caption, [], system_prompt))
# Display the generated story
st.subheader("Generated Story:")
st.write(ai_response, unsafe_allow_html=True)
else:
st.warning("Please upload an image.")
return
if __name__ == "__main__":
main()
|