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initial commit and working app
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‎.env.example

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GOOGLE_API_KEY=YOUR_GOOGLE_API_KEY

‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# pyenv
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.python-version
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pyvenv.cfg
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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project-env/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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# Mac OS
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.DS_Store

‎.streamlit/config.toml

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[theme]
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base="dark"

‎LICENSE

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MIT License
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Copyright (c) 2024 Sitam Meur
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md

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‎app.py

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# Necessary imports
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import os
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from PIL import Image
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import streamlit as st
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from streamlit_image_select import image_select
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from src.utils import input_image_details
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from src.llm_response import (
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generate_image_response,
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generate_text_response,
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generate_example_image_response,
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)
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# App title and description
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st.set_page_config(page_title="Streamlit App Builder", page_icon="🛠")
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st.title("🛠 Streamlit App Builder")
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st.header("Build Your Streamlit App with Ease")
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st.info(
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"Welcome to the **Streamlit App Builder**! In this app, you can either **Show** (by providing a preview image) or **Tell** (by providing a text prompt) how you want your Streamlit app to be built. Let's get started!"
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)
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# Tabs to switch between "Show" and "Tell"
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tabs = st.tabs(["Show", "Tell"])
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# Show how the app should be built or tell how the app should be built
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with tabs[0]:
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# Upload image option
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upload_img = st.toggle("Upload your own preview image")
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# If upload image option is selected
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if upload_img:
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st.subheader("Upload your own preview image")
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image_upload = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
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# Show uploaded image if any image is uploaded by the user
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if image_upload:
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image = Image.open(image_upload)
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st.image(image, caption="Uploaded Image", use_column_width=True)
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# Example images option
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example_img = st.toggle("Try example preview images")
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# If example image option is selected
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if example_img:
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st.subheader("Try these example preview images")
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# Select image from list of images in images folder
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img = image_select(
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label="Select a preview image",
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images=[
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"images/streamlit-app-preview-1.png",
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"images/streamlit-app-preview-2.png",
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],
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)
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# Start LLM process (only if image is uploaded or example image is selected)
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if (not upload_img) and (not example_img):
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start_button = st.button("Build", key="button_image_start", disabled=True)
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else:
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start_button = st.button("Build", key="button_image_start", disabled=False)
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# If image is uploaded or example image is selected
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if any([upload_img, example_img]) == True:
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if "img" in locals() or "img" in globals():
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if start_button:
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# Processing the image
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with st.spinner("Processing ..."):
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# When image preview 1 is selected
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if img == "images/streamlit-app-preview-1.png":
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st.subheader("Input")
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st.image("images/streamlit-app-preview-1.png")
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preview_image_1 = Image.open(
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"images/streamlit-app-preview-1.png"
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)
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st.subheader("Output")
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example_image_output_1 = generate_example_image_response(
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preview_image_1
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)
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st.write(example_image_output_1)
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# When image preview 2 is selected
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if img == "images/streamlit-app-preview-2.png":
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st.subheader("Input")
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st.image("images/streamlit-app-preview-2.png")
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preview_image_2 = Image.open(
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"images/streamlit-app-preview-2.png"
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)
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st.subheader("Output")
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example_image_output_2 = generate_example_image_response(
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preview_image_2
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)
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st.write(example_image_output_2)
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# Clear results if "Clear" button is clicked
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if st.button("Clear", key="button_image_clear"):
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os.remove(example_image_output_1)
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os.remove(example_image_output_2)
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# If image is uploaded and start button is clicked
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elif image_upload is not None and start_button:
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# Processing the image
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with st.spinner("Processing ..."):
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image_data = input_image_details(image_upload)
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try:
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# Get the generated output from the model based on the image uploaded
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image_data_output = generate_image_response(image_data)
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# Display the output
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st.subheader("Output")
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st.write(image_data_output)
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# Clear results if "Clear" button is clicked
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if st.button("Clear", key="button_image_clear"):
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os.remove(image_data_output)
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# Raise error if any error occurs during response generation
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except Exception as e:
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st.error(f"An error occurred: {e}")
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else:
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if not image_upload and start_button:
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# Warn user to upload image
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st.warning("Please upload your preview image.")
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# Tell how the app should be built
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with tabs[1]:
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# Text prompt to describe the app
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text_prompt = st.text_area(
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"Describe details on the functionalities of the Streamlit app that you want to build.",
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"",
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height=240,
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)
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# Start LLM process
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start_button = st.button("Build", key="button_text_start")
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# If text prompt is provided and start button is clicked
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if text_prompt is not None and start_button:
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# Processing the text prompt
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with st.spinner("Processing ..."):
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try:
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# Get the generated output from the model based on the text prompt
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text_data_output = generate_text_response(text_prompt)
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# Display the extracted content
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st.subheader("Output")
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st.write(text_data_output)
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# Clear results if "Clear" button is clicked
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if st.button("Clear", key="button_text_clear"):
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os.remove(text_data_output)
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# Raise error if any error occurs during response generation
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except Exception as e:
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st.error(f"An error occurred: {e}")
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else:
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if not text_prompt and start_button:
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# Warn user to provide text prompt
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st.warning("Please provide your text prompt.")

‎assets/example_prompt.txt

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I want to make a photo gallery app using Streamlit. Here are the details:
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1. **Photo Storage:**
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- The photos are stored locally on my computer in a folder named 'photos'.
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2. **Photo Display:**
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- Display the photos in a grid layout.
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- Each photo should be displayed as a thumbnail. When a thumbnail is clicked, the full-sized image should be displayed.
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3. **Additional Features:**
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- Users should be able to navigate through different folders within the 'photos' directory.
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- Each photo should have a caption displayed below it.
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- Provide an option for users to download the full-sized image.
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- Include a search functionality to filter photos by filename or caption.
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Please generate the Streamlit code to create this photo gallery app based on the above specifications.

‎images/streamlit-app-preview-1.png

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‎images/streamlit-app-preview-2.png

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‎requirements.txt

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streamlit==1.36.0
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Pillow==10.3.0
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streamlit-image-select==0.6.0
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google-generativeai==0.7.1
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python-dotenv==1.0.1

‎src/__init__.py

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‎src/config.py

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# Set up the model configuration for content generation
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generation_config = {
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"temperature": 0.3,
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"top_p": 0.95,
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"top_k": 40,
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"max_output_tokens": 1400,
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"response_mime_type": "text/plain",
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}
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# Define safety settings for content generation
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safety_settings = [
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{"category": f"HARM_CATEGORY_{category}", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}
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for category in [
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"HARASSMENT",
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"HATE_SPEECH",
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"SEXUALLY_EXPLICIT",
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"DANGEROUS_CONTENT",
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]
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]
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# Define the model name for content generation using the Gemini API
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model_name = "gemini-1.5-pro"

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