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