first commit

CaptionGenerator.py

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+'''
+This script contains the CaptionGenerator class,
+which is used to generate Instagram captions for images
+using BLIP and Gemini models.
+'''
+import google.generativeai as genai
+import streamlit as st
+import app_utils as utils
+from transformers import AutoProcessor, Blip2ForConditionalGeneration
+
+genai.configure(api_key=utils.get_gemini_api_key())
+
+class CaptionGenerator:
+    """
+    Class for generating Instagram captions for images using BLIP and Gemini models.
+    The model from Hugging Face is used to generate the initial caption for the image,
+    which is then used as a prompt for the Gemini model to generate five distinct and
+    engaging captions for the image.
+
+    Attributes:
+    - google_api_key (str): Google API key for accessing the Generative AI API.
+    - gemini_model (GenerativeModel): Gemini model for generating Instagram captions.
+    - blip_processor (AutoProcessor): BLIP model processor for image captioning.
+    - blip_model (BlipForConditionalGeneration): BLIP model for image captioning.
+
+    Methods:
+    - process_image(image_data): Resize and prepare the image for caption generation.
+    - predict(image_data): Generate five Instagram captions for the provided image.
+    """
+    def __init__(self):
+        self.gemini = genai.GenerativeModel('gemini-pro')
+        self.processor = None
+        self.model = None
+
+    def image_2_text(self, image_data):
+        """
+        Generate a caption for the provided image using the BLIP-2 model.
+        :param image_data: PIL.Image - The image for which the caption is to be generated.
+        :return: description - The description generated for the image.
+        """
+        try:
+            self.processor, self.model, _ = utils.init_model(init_model_required=True)
+            inputs = self.processor(images=image_data, return_tensors="pt")
+            generated_ids = self.model.generate(**inputs, max_length=100)
+            description = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
+            return description
+
+        except Exception as e:
+            st.error(f"Error occurred during image captioning: {e}")
+
+
+    def text_2_caption(self, image_description):
+        """
+        Generate five Instagram captions for the provided image.
+        The image is first processed before generating captions.
+
+        :param image_description: str - The description of the image for which captions are to be generated.
+        :return: str - Five Instagram captions formatted as specified.
+        """
+        prompt = (
+            f"This caption was generated with a deep learning model."
+            f"Your task is to enhance the caption to make it more engaging:"
+            f"Given this provided photo description, generate five distinct "
+            f"fun and engaging Instagram captions. Each caption must include "
+            f"at least one emoji and one hashtag. The captions should be "
+            f"formatted with a preceding 'Caption #', followed by the "
+            f"caption text. Ensure each caption is separated by a blank "
+            f"line for readability."
+            f"Original Caption: {image_description}"
+            f"Please format your response as follows: \n"
+            f"**Caption 1**: [caption text]\n"
+            f"**Caption 2**: [caption text]\n"
+            f"**Caption 3**: [caption text]\n"
+            f"**Caption 4**: [caption text]\n"
+            f"**Caption 5**: [caption text]\n"
+        )
+
+        try:
+            response = self.gemini.generate_content(prompt)
+            caption = response.parts[0].text
+            caption_list = response.parts[0].text.split("\n")
+            return caption, caption_list, image_description
+
+        except Exception as e:
+            st.error(f"Unable to connect to Gemini API: {e}")
+
+
+        # try:
+        #     response = gemini_model.generate_content(prompt)
+        #     caption = response.parts[0].text
+        #     caption_list = response.parts[0].text.split("\n")
+        #     return caption, caption_list, image_description
+        #
+        # except Exception as e:
+        #     st.error(f"Unable to generate captions from description: {e}")
+
+    def caption_2_hashtag(self, caption):
+        """
+        Generate additional hashtags based on the content of the caption.
+
+        :param caption: str - The caption for which hashtags are to be generated.
+        :return: str - Additional hashtags based on the content of the caption.
+        """
+        # Generate hashtags based on the content of the caption
+        prompt = (f"Given the provided caption, generate relevant hashtags to increase engagement,"
+                  f"and are related to the caption content. Original Image Description: {caption},"
+                  f"Please format your response as follows:\n"
+                  f'[hashtags separated by commas]'
+                  f" \n")
+
+        try:
+            response = self.gemini.generate_content(prompt)
+            hashtags = response.parts[0].text
+            return hashtags
+
+        except Exception as e:
+            st.error(f"Error occurred with Gemini API: {e}")
+
+
+@st.cache_resource()
+def load_model():
+    """
+    Loads the BLIP-2 model for image captioning. This function is cached to avoid
+    re-loading the model on every call.
+    :param CaptionGenerator:  The class for generating captions for images.
+    :return: Instance of the CaptionGenerator class.
+    """
+    return CaptionGenerator()
+
+
+# Example usage of the CaptionGenerator class
+# caption_generator = load_model()
+# image = Image.open("example.jpg")
+# desc = caption_generator.image_2_text(image)
+# captions, caption_list, img_description = caption_generator.text_2_caption(desc)
+# hashtags = caption_generator.caption_2_hashtag(img_description)
+# print(captions)
+# print(hashtags)

app_utils.py

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+"""
+Utility functions for the Instagram Caption Generator app.
+"""
+import streamlit as st
+from dotenv import load_dotenv
+from transformers import AutoProcessor, Blip2ForConditionalGeneration
+import os
+from pathlib import Path
+import pandas as pd
+
+
+def get_gemini_api_key():
+    """
+    Retrieves the Google API key for accessing the Generative AI API.
+    :return: str - The Google API key.
+    """
+    load_dotenv()
+    google_api_key = os.getenv("GOOGLE_API_KEY")
+    return google_api_key
+
+
+@st.cache_resource()
+def init_model(init_model_required):
+    """
+    Initializes the BLIP-2 model and processor for image captioning.
+    This helper function allows for lazy loading of the model and processor.
+    The streamlit app can call this function to load the model and processor
+    only when needed.
+    :param init_model_required: bo
+    ol - Flag to indicate if the model needs to be initialized.
+    :returns: AutoProcessor, Blip2ForConditionalGeneration, bool - Model processor, BLIP-2 model, and flag.
+    """
+    if init_model_required:
+        try:
+            processor = AutoProcessor.from_pretrained('./data/pretrained/blip2-opt-2.7b')
+            blip2_model = Blip2ForConditionalGeneration.from_pretrained('./data/pretrained/blip2-opt-2.7b')
+            init_model_required = False
+            return processor, blip2_model, init_model_required
+        except Exception as e:
+            st.error(f"Error occurred during model initialization: {e}")
+
+
+# Function to store the user data to a CSV file
+def save_user_data(first_name, last_name, email, phone):
+    """
+    Function to store the user data to a CSV file
+
+    :param first_name: str - First name of the user
+    :param last_name: str - Last name of the user
+    :param email: str - Email of the user
+    :param phone: str - Phone number of the user
+    :return: None
+    """
+    csv_file = Path("./user_data/user_data.csv")
+    # Check if the file exists and create a DataFrame accordingly
+    if csv_file.exists():
+        df = pd.read_csv(csv_file)
+    else:
+        df = pd.DataFrame(columns=["First Name", "Last Name", "Email", "Phone Number"])
+
+    # Add and save new user data (not for production).
+    new_data = pd.DataFrame({"First Name": [first_name],
+                             "Last Name": [last_name],
+                             "Email": [email],
+                             "Phone Number": [phone]})
+    df = pd.concat([df, new_data], ignore_index=True)
+    df.to_csv(csv_file, index=False)
+    return None
+
+
+def get_gif(path):
+    """Function to get the GIF image"""
+    with open(path, "rb") as file:
+        gif = file.read()
+    return gif
+
+
+# Blip-2 does most of the standard image processing needed for image captioning.
+def process_image(image_data, processor):
+    pass

pages/📸_InstaMuse.py

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+import os
+import streamlit as st
+from PIL import Image
+import pyperclip
+import app_utils as utils
+from CaptionGenerator import CaptionGenerator, load_model
+
+# Define Streamlit configurations
+st.set_page_config(
+    page_title="Instamuse", 
+    page_icon=":camera:", 
+    layout='wide', 
+    menu_items={
+        'Get Help': 'https://www.streamlit.io',
+        'Report a bug': "https://github.com/aditya-67/Instagram_Caption_Generator/issues",
+        'About': "# This is a Streamlit app that uses AI to generate captions for images."
+    }
+)
+
+# Initialize the caption generator
+caption_generator = load_model()
+
+# Define aesthetic enhancements, including CSS for the spinner
+st.markdown(
+    """
+    <style>
+    .big-font {
+        font-size:30px !important;
+        font-weight: bold;
+    }
+    .image-shadow {
+        box-shadow: 8px 8px 20px grey;
+    }
+    /* Centering the spinner */
+    .st-bq {
+        position: fixed;
+        top: 50%;
+        left: 50%;
+        transform: translate(-50%, -50%);
+        font-size: 20px;  /* Make text larger */
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Sidebar configuration
+with st.sidebar:
+    st.image(os.path.join('images', 'insta.png'), width=300)
+    st.title("Welcome to InstaMuse!")
+    st.subheader("Turn your snapshots into social media sensations.")
+    st.write("Start turning heads with your posts. Use InstaMuse now and watch your likes soar!")
+
+# Main page content
+st.markdown('<p class="big-font">InstaMuse Photo Caption Generator</p>', unsafe_allow_html=True)
+st.write("### Upload your photo below and spark some caption magic! ଘ(੭ˊᵕˋ)੭* ੈ✩‧₊")
+
+# Upload image file and process image
+file = st.file_uploader(
+    "Upload your image here:",
+    type=["jpg", "png"],
+    help="Only jpg and png images are supported"
+)
+
+if file:
+    image = Image.open(file)
+    image.thumbnail((600, 600), Image.Resampling.LANCZOS)
+
+    try:
+        desc = caption_generator.image_2_text(image)
+        captions, caption_list, img_description = caption_generator.text_2_caption(desc)
+        st.session_state['captions'] = captions
+        st.session_state['caption_list'] = caption_list
+        st.session_state['img_description'] = img_description
+    except Exception as e:
+        st.error(f"Error generating captions: {e}")
+        captions = None
+
+    with st.spinner(r'#### :sparkles: :sparkles: Generating... please wait :hourglass_flowing_sand:'):
+        st.session_state['file'] = file
+
+        with st.container():
+            col1, col2 = st.columns(2)
+
+            with col1:
+                st.markdown("## 📸 Your Image:")
+                st.image(image, caption='Uploaded Image', use_column_width=True)
+
+            with col2:
+                if captions:
+                    st.markdown("## 📝 Generated Captions:")
+                    for caption in caption_list:
+                        if caption.strip():
+                            st.info(f"##### {caption}")
+
+    # Action buttons with functionality
+    if 'captions' in st.session_state and st.session_state['captions']:
+        col1, col2, col3, col4 = st.columns(4)
+        if col1.button("📋 Copy"):
+            pyperclip.copy(st.session_state['captions'])
+            st.success("Caption copied to clipboard!")
+
+        if col2.button("🔄 Regenerate"):
+            with st.spinner('Regenerating captions...'):
+                st.rerun()
+
+        if col3.button("✨ More Hashtags"):
+            with st.spinner('Generating hashtags...'):
+                try:
+                    hashtags = caption_generator.caption_2_hashtag(st.session_state['img_description'])
+                except Exception as e:
+                    st.error(f"Error generating hashtags: {e}")
+                    hashtags = None
+                st.write("### Generated Hashtags:")
+                st.write(f"**{hashtags}**")
+
+        if col4.button(":window: Clear Screen"):
+            st.rerun()
+
+with st.expander("Need help?"):
+    st.write("Please contact us at [email](mailto:jess@llmjessica.com)")
+
+# Footer
+st.markdown("---")
+st.caption("Thank you for using InstaMuse! Feel free to contact us for any suggestions or issues.")

test_app.py

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+import unittest
+from PIL import Image
+from app import import_and_predict
+
+
+class TestApp(unittest.TestCase):
+
+    def test_import_and_predict(self):
+        image_data = Image.new('RGB', (100, 100))  # Mock image data
+        caption = import_and_predict(image_data)  # Call the function
+        self.assertIsInstance(caption, str)  # Check if caption is a string
+        self.assertTrue(len(caption) > 0)  # Check if caption is not empty
+
+
+if __name__ == '__main__':
+    unittest.main()

user_data/user_data.csv

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+First Name,Last Name,Email,Phone Number
+Jane,Doe,jd@gmail.com,99999999
+j`,as,22,22
+z,asds,4,3
+test,test,test,test

✨_Home.py

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+import os
+import streamlit as st
+from app_utils import get_gif, save_user_data
+
+
+def main():
+    # Define Streamlit configurations
+    st.set_page_config(page_title="Instamuse", page_icon=":camera:", initial_sidebar_state='auto')
+
+    # Sidebar configuration
+    with st.sidebar:
+        st.image(os.path.abspath('images/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!")
+
+    # Title and Style
+    st.markdown("""
+    <style>
+    .title-font {
+        font-size: 50px;
+        font-weight: bold;
+        font-family: 'Helvetica', sans-serif;
+        color: #FF573D;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+    st.markdown('<h1 class="title-font">The Worlds Best Instagram Caption Generator</h1>', unsafe_allow_html=True)
+
+    # Subheader
+    st.write(
+        "### Transform your online presence—join InstaMuse to create "
+        "innovative captions that increase likes and enhance your visibility."
+    )
+
+    # Gif display
+    st.image(get_gif(os.path.abspath('images/insta.png')))
+
+    # Why us?
+    st.subheader('Why InstaMuse?')
+    st.write("""
+    - 🎨 **Creativity at your fingertips:** Generate unique captions that stand out.
+    - ⏳ **Save time:** Stop spending hours trying to write the perfect caption.
+    - 🚀 **Boost engagement:** Captions crafted to keep your audience engaged.
+    - 💸 **Free to use:** Get access to our technology preview at no cost.
+    """)
+
+    # Success Stories
+    st.subheader('Success Stories')
+    cols = st.columns(2)
+    with cols[0]:
+        st.image(os.path.abspath('images/insta_post.png'))
+    with cols[1]:
+        st.write("")
+        st.write('"InstaMuse helped us reach a wider audience. Our engagement rates have never been higher!" - CR')
+        st.write('"Thanks to InstaMuse, our posts now resonate better with our audience, bringing more likes and comments than ever before." - LJ')
+        st.write('"The creative captions we developed with InstaMuse have not only increased likes but also built lasting connections with our followers." - MK')
+    
+    # Sign up section
+    st.subheader('Sign up here for priority access')
+    st.write('Enter your details below to get early access to our innovative caption generator.')
+
+    # Form to collect user details
+    with st.form(key='user_details_form', clear_on_submit=True):
+        cols = st.columns(2)
+        with cols[0]:
+            first_name = st.text_input("First Name")
+            email = st.text_input("Email", help="We'll never share your email with anyone else.")
+        with cols[1]:
+            last_name = st.text_input("Last Name")
+            phone = st.text_input("Phone Number", help="We'll never share your number with anyone else.")
+        submit_button = st.form_submit_button("Start Using InstaMuse")
+        
+        if submit_button:
+            if first_name and last_name and email and phone:
+                save_user_data(first_name, last_name, email, phone)
+                st.success("Thank you for signing up! We will be in touch soon.")
+            else:
+                st.error("Please fill in all the fields.")
+
+
+if __name__ == "__main__":
+    main()