Spaces:
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Complete Refactored Code
Browse filesImprovement:
Constants: Define constants at the top for model names and safety settings to improve readability and maintainability.
Function for Image Saving: While a small task, encapsulating temporary file saving could be a good practice for larger applications. For this case, it's fine as is.
Redundant pathlib for temp_image_path: You create a Path object but then pass it to read_bytes() which expects a string or Path-like object. It's okay, but str(temp_image_path) could be used for clarity if needed by other functions, although read_bytes() handles Path directly.
Improvement: Streamlit reruns the script from top to bottom whenever there's an interaction. Re-initializing the GenerativeModel every time is inefficient.
Change: Use
@st
.cache_resource to cache the model.
Improvement: The chat_session should ideally be managed within st.session_state to maintain a persistent conversation with the model across Streamlit reruns for a single user, if you intend to have a continued conversation. While your current use case is step-by-step generation, it's a good practice for chat applications.
@@ -2,13 +2,20 @@ import streamlit as st
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import pathlib
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from PIL import Image
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import google.generativeai as genai
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# Configure the API key directly in the script
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API_KEY = 'YOUR KEY'
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genai.configure(api_key=API_KEY)
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# Generation configuration
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"temperature": 1,
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"top_p": 0.95,
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"top_k": 64,
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"response_mime_type": "text/plain",
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}
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# Safety settings
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{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
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{"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
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{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
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{"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
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]
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# Model name
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MODEL_NAME = "gemini-1.5-pro-latest"
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#
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image_input = {
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'mime_type': 'image/jpeg',
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'data':
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}
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def main():
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st.
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uploaded_file = st.file_uploader("
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if uploaded_file is not None:
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try:
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# Load and display the image
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image = Image.open(uploaded_file)
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st.image(image, caption='Uploaded Image
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# Convert image to RGB mode if it has an alpha channel
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if image.mode == 'RGBA':
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image = image.convert('RGB')
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# Save the uploaded image temporarily
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temp_image_path = pathlib.Path("temp_image.jpg")
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image.save(temp_image_path, format="JPEG")
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#
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st.
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file.write(refined_html)
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st.success("HTML file 'index.html' has been created.")
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except Exception as e:
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st.error(f"An error occurred: {e}")
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if __name__ == "__main__":
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main()
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import pathlib
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from PIL import Image
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import google.generativeai as genai
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import os # Import os for environment variables
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# --- Configuration ---
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try:
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# Ensure GOOGLE_API_KEY is set in Streamlit secrets or as an environment variable
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API_KEY = st.secrets["GOOGLE_API_KEY"]
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except KeyError:
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st.error("Google API Key not found. Please set it in Streamlit secrets or as an environment variable (`GOOGLE_API_KEY`).")
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st.stop() # Stop the app if API key is missing
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genai.configure(api_key=API_KEY)
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# Generation configuration for the Gemini model
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GENERATION_CONFIG = {
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"temperature": 1,
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"top_p": 0.95,
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"top_k": 64,
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"response_mime_type": "text/plain",
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}
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# Safety settings for the Gemini model
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SAFETY_SETTINGS = [
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{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
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{"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
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{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
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{"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
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]
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# Model name constant
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MODEL_NAME = "gemini-1.5-pro-latest"
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# --- Model Initialization ---
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@st.cache_resource # Cache the model to avoid re-initializing on every rerun
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def load_gemini_model():
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"""Loads and caches the Google GenerativeModel."""
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return genai.GenerativeModel(
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model_name=MODEL_NAME,
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safety_settings=SAFETY_SETTINGS,
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generation_config=GENERATION_CONFIG,
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)
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model = load_gemini_model()
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# Initialize chat session in Streamlit's session state
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# This ensures the chat history persists across reruns for a single user
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if "chat_session" not in st.session_state:
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st.session_state.chat_session = model.start_chat(history=[])
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# --- Helper Function for Model Communication ---
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def send_message_to_model(message: str, image_path: pathlib.Path) -> str:
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"""Sends a message and an image to the Gemini model and returns the response."""
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image_input = {
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'mime_type': 'image/jpeg',
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'data': image_path.read_bytes()
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}
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try:
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response = st.session_state.chat_session.send_message([message, image_input])
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return response.text
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except Exception as e:
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st.error(f"Error communicating with the Gemini model: {e}")
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st.exception(e) # Display full traceback for debugging
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return "An error occurred during AI model communication. Please try again or check your API key."
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# --- Streamlit App ---
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def main():
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"""Main function to run the Streamlit application."""
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st.set_page_config(page_title="Gemini 1.5 Pro: Images to Code", layout="wide") # Set a wider layout
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st.title("Gemini 1.5 Pro: Images to Code 👨💻")
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st.markdown('Made with ❤️ by [KhulnaSoft](https://x.com/khulnasoft)')
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st.info("Upload an image of a UI design, and I'll generate the corresponding HTML and CSS code for you!")
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# Framework selection using a selectbox
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framework_options = {
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"Regular CSS (Flexbox/Grid)": "Regular CSS use flex grid etc",
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"Bootstrap": "Bootstrap",
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"Tailwind CSS": "Tailwind CSS",
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"Materialize CSS": "Materialize CSS"
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}
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selected_framework_name = st.selectbox(
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"Choose your preferred CSS framework:",
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options=list(framework_options.keys()),
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help="This will influence the CSS generated within your HTML file."
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)
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framework = framework_options[selected_framework_name]
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uploaded_file = st.file_uploader("Upload a UI image (JPG, JPEG, PNG):", type=["jpg", "jpeg", "png"])
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# temp_image_path is declared outside the try block to ensure it's accessible for cleanup
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temp_image_path = pathlib.Path("temp_image.jpg")
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if uploaded_file is not None:
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try:
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# Load and display the image
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image = Image.open(uploaded_file)
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st.image(image, caption='Uploaded UI Image', use_column_width=True)
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# Convert image to RGB mode if it has an alpha channel
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if image.mode == 'RGBA':
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image = image.convert('RGB')
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# Save the uploaded image temporarily
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image.save(temp_image_path, format="JPEG")
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st.markdown("---") # Visual separator
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# Button to trigger the generation process
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if st.button("Generate UI Code", help="Click to initiate the multi-step code generation."):
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st.subheader("Code Generation Process:")
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# Step 1: Generate initial UI description
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with st.spinner("Step 1/4: Describing your UI elements and colors..."):
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prompt = "Describe this UI in accurate details. When you reference a UI element put its name and bounding box in the format: [object name (y_min, x_min, y_max, x_max)]. Also Describe the color of the elements."
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description = send_message_to_model(prompt, temp_image_path)
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st.success("UI Description Generated!")
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with st.expander("See Initial UI Description"):
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st.text(description)
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# Step 2: Refine the description
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with st.spinner("Step 2/4: Refining description with visual comparison..."):
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refine_prompt = f"Compare the described UI elements with the provided image and identify any missing elements or inaccuracies. Also Describe the color of the elements. Provide a refined and accurate description of the UI elements based on this comparison. Here is the initial description: {description}"
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refined_description = send_message_to_model(refine_prompt, temp_image_path)
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st.success("UI Description Refined!")
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with st.expander("See Refined UI Description"):
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st.text(refined_description)
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# Step 3: Generate initial HTML
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with st.spinner("Step 3/4: Generating initial HTML with CSS..."):
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html_prompt = (
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f"Create an HTML file based on the following UI description, using the UI elements described in the previous response. "
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f"Include {framework} CSS within the HTML file to style the elements. "
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f"Make sure the colors used are the same as the original UI. "
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f"The UI needs to be responsive and mobile-first, matching the original UI as closely as possible. "
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f"Do not include any explanations or comments. Avoid using ```html and ``` at the end. "
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f"ONLY return the HTML code with inline CSS. Here is the refined description: {refined_description}"
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)
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initial_html = send_message_to_model(html_prompt, temp_image_path)
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st.success("Initial HTML Generated!")
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st.subheader("Initial Generated HTML:")
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st.code(initial_html, language='html')
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# Step 4: Refine HTML
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with st.spinner("Step 4/4: Refining the generated HTML code..."):
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refine_html_prompt = (
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f"Validate the following HTML code based on the UI description and image and provide a refined version of the HTML code with {framework} CSS that improves accuracy, responsiveness, and adherence to the original design. "
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f"ONLY return the refined HTML code with inline CSS. Avoid using ```html and ``` at the end. "
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f"Here is the initial HTML: {initial_html}"
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)
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refined_html = send_message_to_model(refine_html_prompt, temp_image_path)
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st.success("HTML Refined Successfully!")
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st.subheader("Refined Generated HTML:")
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st.code(refined_html, language='html')
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st.markdown("---") # Final separator
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st.success("All steps completed! Your `index.html` file is ready for download.")
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# Save the refined HTML to a file and provide download link
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with open("index.html", "w", encoding="utf-8") as file: # Specify encoding
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file.write(refined_html)
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st.download_button(
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label="Download index.html",
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data=refined_html,
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file_name="index.html",
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mime="text/html"
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)
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st.info("You can open the downloaded `index.html` file in your web browser to view the generated UI.")
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except Exception as e:
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st.error(f"An unexpected error occurred: {e}")
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st.exception(e) # Displays the full traceback
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finally:
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# Clean up the temporary image file whether an error occurred or not
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if temp_image_path.exists():
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os.remove(temp_image_path)
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# st.success("Temporary image file removed.") # Can uncomment for debugging
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else:
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st.write("Please upload an image to start generating UI code.")
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if __name__ == "__main__":
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main()
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