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import streamlit as st
import requests
from dotenv import load_dotenv
import os
load_dotenv()
API_URL_SEMANTICS = "https://api-inference.huggingface.co/models/Salesforce/blip-image-captioning-large"
API_URL_CAPTION = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
headers = {"Authorization": f"Bearer {os.getenv('api_key')}"}
st.set_page_config(page_title="Instagram Post Improvement")
def generateSemantic(file):
response = requests.post(API_URL_SEMANTICS, headers=headers, data=file)
return response.json()[0]['generated_text']
def generateCaption(payload):
response = requests.post(API_URL_CAPTION , headers=headers, json=payload)
return response.json()[0]['generated_text']
st.title("Create an Eye-Catching Instagram Post 📸✨")
st.write(""" 🌟 This project utilizes two free pre-trained models from Hugging Face to enhance the engagement and attractiveness of your Instagram posts for your followers. It accomplishes this through two steps:
1-🚀 Capturing the semantics of an image.
2-🎀 Transforming the captured semantics into an appealing Instagram post. """)
st.sidebar.title('About app')
st.sidebar.info(
"This is a Streamlit application created by Gasbaoui Mohammed el Amin.\n"
"It demonstrates how to interact with pre-trained model hagging face."
)
file=st.file_uploader("upload an image",type=["jpg","jpeg","png"])
if file:
col1,col2=st.columns(2)
with col1:
st.image(file,use_column_width=True)
with col2:
with st.spinner("Generating semantics..."):
outputSemantic=generateSemantic(file)
st.subheader("Output Semantic")
st.markdown(
"""
<style>
/* Style for the container */
.fancy-text {
padding: 10px;
border-radius: 10px; /* Make edges curved */
box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1); /* Add box shadow */
border: 2px solid #ccc; /* Add a border */
}
</style>
""",
unsafe_allow_html=True
)
# Now use the styled container for your text
st.markdown(f'<div class="fancy-text">{outputSemantic}</div>',
unsafe_allow_html=True)
with st.spinner("Generating caption..."):
promptDictionary={
"inputs": f"convert the following image semantics '{outputSemantic}' "
f"to an instagram caption make sure to add hashtags and emojis.,"
f"Answer: ",
}
st.subheader("Caption")
outputCaption=generateCaption(promptDictionary)
result=outputCaption.split("Answer: ")[1]
st.markdown(f'<div class="fancy-text">{result}</div>',
unsafe_allow_html=True)
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