44e5f7dc1f
Modularize Code: Break down the large sidebar_configuration function into smaller, more manageable functions to improve readability and maintainability. Error Handling: Implement error handling for critical operations, such as reading and writing files, to ensure the application handles exceptions gracefully. Docstrings and Comments: Add docstrings and comments to functions and critical code sections to improve code documentation and readability. Code Consistency: Ensure consistent use of naming conventions and code formatting.
215 lines
10 KiB
Python
215 lines
10 KiB
Python
import streamlit as st
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import os
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import json
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from dotenv import load_dotenv
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from datetime import datetime
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from lib.utils.config_manager import save_config
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from lib.utils.ui_setup import setup_ui, setup_tabs
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from lib.utils.api_key_manager import check_all_api_keys
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from lib.utils.file_processor import read_prompts, write_prompts
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# Constants for various options
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BLOG_TONE_OPTIONS = ["Casual", "Professional", "How-to", "Beginner", "Research", "Programming", "Social Media", "Customize"]
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BLOG_DEMOGRAPHIC_OPTIONS = ["Professional", "Gen-Z", "Tech-savvy", "Student", "Digital Marketing", "Customize"]
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BLOG_LANGUAGE_OPTIONS = ["English", "Spanish", "German", "Chinese", "Arabic", "Nepali", "Hindi", "Hindustani", "Customize"]
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CONTENT_TYPE_OPTIONS = ["Informational", "Commercial", "Company", "News", "Finance", "Competitor", "Programming", "Scholar"]
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OUTPUT_FORMAT_OPTIONS = ["markdown", "HTML", "plaintext"]
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IMAGE_MODEL_OPTIONS = ["stable-diffusion", "dalle2", "dalle3"]
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GPT_PROVIDER_OPTIONS = ["google", "openai", "minstral"]
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GEOGRAPHIC_LOCATION_OPTIONS = ["us", "in", "fr", "cn"]
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SEARCH_LANGUAGE_OPTIONS = ["en", "zn-cn", "de", "hi"]
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TIME_RANGE_OPTIONS = ["anytime", "past day", "past week", "past month", "past year"]
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def get_custom_input(label, options, help_text, placeholder=""):
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"""
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Get custom input from the user.
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Parameters:
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- label (str): The label for the input field.
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- options (list): List of options for the select box.
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- help_text (str): Help text for the select box.
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- placeholder (str): Placeholder text for the custom input field.
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Returns:
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- str: The selected or custom input value.
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"""
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selection = st.selectbox(f"**{label}**", options=options, help=help_text)
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if selection == "Customize":
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custom_input = st.text_input(f"Enter your {label.lower()}", help=f"Specify your {label.lower()}.", placeholder=placeholder)
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if custom_input:
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selection = custom_input
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else:
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st.warning(f"Please specify your {label.lower()}.")
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return selection
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def content_personalization(sb):
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"""
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Sidebar content personalization section.
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Parameters:
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- sb: The Streamlit sidebar object.
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Returns:
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- dict: Dictionary containing personalized content configuration.
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"""
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with sb.expander("**👷 Content Personalization**"):
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blog_length = sb.text_input("**Content Length (words)**", value="2000", help="Approximate word count for blogs.")
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blog_tone = get_custom_input("Content Tone", BLOG_TONE_OPTIONS, "Select the desired tone for the blog content.")
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blog_demographic = get_custom_input("Target Audience", BLOG_DEMOGRAPHIC_OPTIONS, "Select the primary audience for the blog content.", "Eg. Domain expert, Content creator, Financial expert etc.")
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blog_type = sb.selectbox("**Content Type**", options=CONTENT_TYPE_OPTIONS, help="Select the category that best describes the blog content.")
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blog_language = get_custom_input("Content Language", BLOG_LANGUAGE_OPTIONS, "Select the language in which the blog will be written.")
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blog_output_format = sb.selectbox("**Content Output Format**", options=OUTPUT_FORMAT_OPTIONS, help="Select the format for the blog output.")
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return {
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"Blog Length": blog_length,
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"Blog Tone": blog_tone,
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"Blog Demographic": blog_demographic,
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"Blog Type": blog_type,
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"Blog Language": blog_language,
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"Blog Output Format": blog_output_format
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}
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def images_personalization(sb):
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"""
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Sidebar images personalization section.
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Parameters:
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- sb: The Streamlit sidebar object.
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Returns:
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- dict: Dictionary containing personalized images configuration.
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"""
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with sb.expander("**🩻 Images Personalization**"):
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image_generation_model = sb.selectbox("**Image Generation Model**", options=IMAGE_MODEL_OPTIONS, help="Select the model to generate images for the blog.")
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number_of_blog_images = sb.number_input("**Number of Blog Images**", value=1, help="Specify the number of images to include in the blog.")
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return {
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"Image Generation Model": image_generation_model,
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"Number of Blog Images": number_of_blog_images
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}
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def llm_personalization(sb):
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"""
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Sidebar LLM personalization section.
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Parameters:
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- sb: The Streamlit sidebar object.
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Returns:
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- dict: Dictionary containing personalized LLM configuration.
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"""
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with sb.expander("**🤖 LLM Personalization**"):
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gpt_provider = sb.selectbox("**GPT Provider**", options=GPT_PROVIDER_OPTIONS, help="Select the provider for the GPT model.")
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model = sb.text_input("**Model**", value="gemini-1.5-flash-latest", help="Specify the model version to use from the selected provider.")
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temperature = sb.slider("Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.1, format="%.1f", help="Controls the 'creativity' or randomness of the text generated.")
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top_p = sb.slider("Top-p", min_value=0.0, max_value=1.0, value=0.9, step=0.1, format="%.1f", help="Controls the level of diversity in the generated text.")
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max_tokens = sb.selectbox("Max Tokens", options=[500, 1000, 2000, 4000, 16000, 32000, 64000], index=3, help="Maximum length of the output sequence generated by a model.")
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n = sb.number_input("N", value=1, min_value=1, max_value=10, help="Defines the number of words or characters grouped together in a sequence.")
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frequency_penalty = sb.slider("Frequency Penalty", min_value=0.0, max_value=2.0, value=1.0, step=0.1, format="%.1f", help="Promotes diversity with higher values.")
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presence_penalty = sb.slider("Presence Penalty", min_value=0.0, max_value=2.0, value=1.0, step=0.1, format="%.1f", help="Encourages the use of diverse words by discouraging repetition.")
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return {
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"GPT Provider": gpt_provider,
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"Model": model,
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"Temperature": temperature,
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"Top-p": top_p,
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"Max Tokens": max_tokens,
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"N": n,
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"Frequency Penalty": frequency_penalty,
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"Presence Penalty": presence_penalty
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}
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def search_engine_personalization(sb):
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"""
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Sidebar search engine personalization section.
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Parameters:
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- sb: The Streamlit sidebar object.
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Returns:
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- dict: Dictionary containing personalized search engine configuration.
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"""
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with sb.expander("**🕵️ Search Engine Personalization**"):
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geographic_location = sb.selectbox("**Geographic Location**", options=GEOGRAPHIC_LOCATION_OPTIONS, help="Select the geographic location for tailoring search results.")
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search_language = sb.selectbox("**Search Language**", options=SEARCH_LANGUAGE_OPTIONS, help="Select the language for the search results.")
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number_of_results = sb.number_input("**Number of Results**", value=10, min_value=1, max_value=20, help="Specify the number of search results to retrieve.")
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time_range = sb.selectbox("**Time Range**", options=TIME_RANGE_OPTIONS, help="Select the time range for filtering search results.")
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include_domains = sb.text_input("**Include Domains**", value="", help="List specific domains to include in search results.")
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similar_url = sb.text_input("**Similar URL**", value="", help="Provide a URL to find similar results.")
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return {
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"Geographic Location": geographic_location,
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"Search Language": search_language,
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"Number of Results": number_of_results,
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"Time Range": time_range,
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"Include Domains": include_domains,
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"Similar URL": similar_url
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}
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def sidebar_configuration():
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"""
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Sidebar configuration for personalization and settings.
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This function consolidates various personalization settings into the sidebar.
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"""
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sb = st.sidebar
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sb.title("🛠️ Personalization & Settings 🏗️")
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content_config = content_personalization(sb)
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images_config = images_personalization(sb)
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llm_config = llm_personalization(sb)
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search_config = search_engine_personalization(sb)
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config = {
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"Blog Content Characteristics": content_config,
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"Blog Images Details": images_config,
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"LLM Options": llm_config,
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"Search Engine Parameters": search_config
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}
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# Save the configuration to a file whenever a change is made
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save_config(config)
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def setup_environment_paths():
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"""
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Sets up environment paths for saving files and configurations.
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"""
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os.environ["SEARCH_SAVE_FILE"] = os.path.join(os.getcwd(), "lib", "workspace", "alwrity_web_research",
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f"web_research_report_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}")
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os.environ["IMG_SAVE_DIR"] = os.path.join(os.getcwd(), "lib", "workspace", "alwrity_content")
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os.environ["CONTENT_SAVE_DIR"] = os.path.join(os.getcwd(), "lib", "workspace", "alwrity_content")
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os.environ["PROMPTS_DIR"] = os.path.join(os.getcwd(), "lib", "workspace", "alwrity_prompts")
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os.environ["ALWRITY_CONFIG"] = os.path.join(os.getcwd(), "lib", "workspace", "alwrity_config", "main_config.json")
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def modify_prompts_sidebar():
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"""
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Provides a sidebar for modifying prompts.
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"""
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st.sidebar.title("📝 Modify Prompts")
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prompts = read_prompts()
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if prompts:
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edited_prompts = []
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for i, prompt in enumerate(prompts):
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edited_prompt = st.sidebar.text_area(f"Prompt {i+1}", prompt)
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edited_prompts.append(edited_prompt)
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if st.sidebar.button("Save Prompts"):
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write_prompts(edited_prompts)
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st.sidebar.success("Prompts saved successfully!")
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else:
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st.sidebar.warning("No prompts found in the file.")
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def main():
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"""
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Main function to run the Streamlit app.
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Initializes the environment, checks API keys, sets up paths, and configures the UI.
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"""
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load_dotenv()
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setup_ui()
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if check_all_api_keys():
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setup_environment_paths()
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sidebar_configuration()
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setup_tabs()
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modify_prompts_sidebar()
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if __name__ == "__main__":
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main()
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