app.py

import streamlit as st
from PIL import Image
from torchvision import transforms
from transformers import AutoProcessor, BlipForConditionalGeneration
import google.generativeai as genai

# Define model

# Initialize the image to caption model - BLIP
blip_processor = AutoProcessor.\
    from_pretrained("Salesforce/blip-image-captioning-base")
blip_model = BlipForConditionalGeneration.\
    from_pretrained("Salesforce/blip-image-captioning-base")

# Initialize the caption to instagram post model - gemini
GOOGLE_API_KEY = 'AIzaSyDMeFOnOflzYJ-cjedJ8ky9AE-yThcNXFk'
genai.configure(api_key=GOOGLE_API_KEY)
gemini_model = genai.GenerativeModel('gemini-pro')


def import_and_predict(image_data):
    """Creates five possible Instagram captions for an image.
    Args:
    - image_data: Image data in RGB format.
    Returns:
    - response.text: The five Instagram captions in text format.
    """
    transform = transforms.Compose([
        transforms.Resize((1080, 1080))
    ])
    uploaded_image = transform(image_data)

    # BLIP Description Generation
    inputs = blip_processor(images=uploaded_image, return_tensors="pt")
    generated_ids = blip_model.generate(**inputs,
                                        max_new_tokens=100,
                                        max_length=100)
    caption = blip_processor.\
        batch_decode(generated_ids, skip_special_tokens=True)[0].strip()

    # Gemini Caption Generation
    prompt = f"""Given the provided photo caption, generate five distinct \
                and engaging Instagram captions. Each caption must include \
                at least one emoji and one hashtag. The captions should be \
                formatted with a preceding "Caption #", followed by the \
                caption text. Ensure each caption is seperated by a blank \
                line for readability. \

                Original Caption: {caption}

                Please format your response as follows:
                **Caption 1**: [caption text] \n
                **Caption 2**: [caption text] \n
                **Caption 3**: [caption text] \n
                **Caption 4**: [caption text] \n
                **Caption 5**: [caption text] \n
                """
    response = gemini_model.generate_content(prompt)
    return response.text


# Define streamlit configurations

# Set pre-defined page configurations
st.set_page_config(page_title="Instamuse",
                   page_icon=":camera:",
                   initial_sidebar_state='auto')

# Sidebar (left side of the page)
with st.sidebar:
    st.image('insta.png')
    st.title("InstaMuse")
    st.subheader("Welcome to InstaMuse, the ultimate tool for turning your \
                 snapshots into social media sensations!")
    st.write("Start turning heads with your posts. Use InstaMuse now and \
             watch your likes soar! ")

# Main page text
st.write("""
         # InstaMuse  🌟📸

        Struggling to find the perfect words to match your pictures? Let \
        InstaMuse do the heavy lifting! With just a simple upload, our app \
        uses cutting-edge technology to analyze your image and generate a \
        witty, engaging, or inspiring caption that captures the essence of \
        your moment. \n \

        Whether you’re a selfie savant, a nature explorer, or a foodie fanatic\
        , InstaMuse is here to amplify your Instagram presence. Jazz up your \
        feed with tailored captions that resonate with your followers and \
        attract new eyes to your profile. It’s quick, easy, and fun!

        **Drag your photo here and spark some caption magic!** ✨
         """
         )

# Upload image file and process image
file = st.file_uploader("", type=["jpg", "png"])

if file is not None:
    # Create two columns for the image and the captions
    col1, col2 = st.columns(2)
    
    with col1:
        # Image column, left screen.
        st.markdown("#### Photo:")
        image = Image.open(file).convert('RGB')
        image.thumbnail((400, 400))
        st.image(image, caption='Uploaded Image')

    with col2:
        # Caption column, right screen.
        predictions = import_and_predict(image)
        st.markdown("#### Captions:")
        st.write(predictions)
else:
    st.text("Please upload an image to generate captions.")