Update alwrity.py - Cleaning up the mess.
This commit is contained in:
ي
316
alwrity.py
316
alwrity.py
@@ -15,18 +15,6 @@ from lib.utils.alwrity_utils import ai_agents_team, ai_social_writer
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from lib.utils.file_processor import load_image, read_prompts, write_prompts
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from lib.utils.voice_processing import record_voice
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# Constants for hardcoded values
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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_TYPE_OPTIONS = ["Informational", "Commercial", "Company", "News", "Finance", "Competitor", "Programming", "Scholar"]
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BLOG_LANGUAGE_OPTIONS = ["English", "Spanish", "German", "Chinese", "Arabic", "Nepali", "Hindi", "Hindustani", "Customize"]
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BLOG_OUTPUT_FORMAT_OPTIONS = ["markdown", "HTML", "plaintext"]
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IMAGE_GENERATION_MODEL_OPTIONS = ["stable-diffusion", "dalle2", "dalle3"]
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GPT_PROVIDER_OPTIONS = ["google", "openai", "minstral", "deepseek"]
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MAX_TOKENS_OPTIONS = [500, 1000, 2000, 4000, 16000, 32000, 64000]
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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 process_folder_for_rag(folder_path):
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@@ -45,153 +33,181 @@ def save_config(config):
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st.error(f"An error occurred while saving the configuration: {e}")
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def handle_custom_input(label, default_options, help_text):
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"""
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Handles custom user input for selectbox options.
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Args:
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label (str): The label for the selectbox.
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default_options (list): The default options for the selectbox.
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help_text (str): The help text for the selectbox.
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Returns:
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str: The selected or custom input value.
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"""
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selected_option = st.selectbox(f"**{label}**", options=default_options, help=help_text)
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if selected_option == "Customize":
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custom_option = st.text_input(f"Enter your {label.lower()}", help=f"Specify your {label.lower()}.")
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if custom_option:
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return custom_option
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else:
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st.warning(f"Please specify your {label.lower()}.")
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return selected_option
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def configure_content_personalization():
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"""
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Configures the content personalization settings in the sidebar.
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Returns:
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dict: A dictionary containing the blog content characteristics.
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"""
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st.sidebar.expander("**👷 Content Personalization**")
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blog_length = st.text_input("**Content Length (words)**", value="2000", help="Approximate word count for blogs. Note: Actual length may vary based on GPT provider and max token count.")
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blog_tone = handle_custom_input("Content Tone", BLOG_TONE_OPTIONS, "Select the desired tone for the blog content.")
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blog_demographic = handle_custom_input("Target Audience", BLOG_DEMOGRAPHIC_OPTIONS, "Select the primary audience for the blog content.")
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blog_type = st.selectbox("**Content Type**", options=BLOG_TYPE_OPTIONS, help="Select the category that best describes the blog content.")
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blog_language = handle_custom_input("Content Language", BLOG_LANGUAGE_OPTIONS, "Select the language in which the blog will be written.")
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blog_output_format = st.selectbox("**Content Output Format**", options=BLOG_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 configure_images_personalization():
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"""
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Configures the image personalization settings in the sidebar.
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Returns:
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dict: A dictionary containing the blog image details.
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"""
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st.sidebar.expander("**🩻 Images Personalization**")
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image_generation_model = st.selectbox("**Image Generation Model**", options=IMAGE_GENERATION_MODEL_OPTIONS, help="Select the model to generate images for the blog.")
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number_of_blog_images = st.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 configure_llm_personalization():
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"""
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Configures the LLM (Language Learning Model) personalization settings in the sidebar.
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Returns:
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dict: A dictionary containing the LLM options.
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"""
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st.sidebar.expander("**🤖 LLM Personalization**")
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gpt_provider = st.selectbox("**GPT Provider**", options=GPT_PROVIDER_OPTIONS, help="Select the provider for the GPT model.")
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model = st.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 = st.slider("Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.1, format="%.1f", help="Temperature controls the 'creativity' or randomness of the text generated by GPT.")
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top_p = st.slider("Top-p", min_value=0.0, max_value=1.0, value=0.9, step=0.1, format="%.1f", help="Top-p sampling controls the level of diversity in the generated text.")
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max_tokens = st.selectbox("Max Tokens", options=MAX_TOKENS_OPTIONS, index=MAX_TOKENS_OPTIONS.index(4000), help="Max tokens determine the maximum length of the output sequence generated by a model.")
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n = st.number_input("N", value=1, min_value=1, max_value=10, help="Defines the number of words or characters grouped together in a sequence when analyzing text.")
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frequency_penalty = st.slider("Frequency Penalty", min_value=0.0, max_value=2.0, value=1.0, step=0.1, format="%.1f", help="Influences word selection during text generation, promoting diversity with higher values.")
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presence_penalty = st.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 configure_search_engine_personalization():
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"""
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Configures the search engine personalization settings in the sidebar.
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Returns:
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dict: A dictionary containing the search engine parameters.
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"""
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st.sidebar.expander("**🕵️ Search Engine Personalization**")
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geographic_location = st.selectbox("**Geographic Location**", options=GEOGRAPHIC_LOCATION_OPTIONS, help="Select the geographic location for tailoring search results.")
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search_language = st.selectbox("**Search Language**", options=SEARCH_LANGUAGE_OPTIONS, help="Select the language for the search results.")
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number_of_results = st.number_input("**Number of Results**", value=10, max_value=20, min_value=1, help="Specify the number of search results to retrieve.")
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time_range = st.selectbox("**Time Range**", options=TIME_RANGE_OPTIONS, help="Select the time range for filtering search results.")
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include_domains = st.text_input("**Include Domains**", value="", help="List specific domains to include in search results. Leave blank to include all domains.")
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similar_url = st.text_input("**Similar URL**", value="", help="Provide a URL to find similar results. Leave blank if not needed.")
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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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# Sidebar configuration
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def sidebar_configuration():
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"""
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Configures the sidebar with various personalization and settings options for the AI Writer application.
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The function includes configurations for:
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- Content Personalization
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- Images Personalization
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- LLM Personalization
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- Search Engine Personalization
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The collected inputs are stored in a dictionary and saved to a configuration file.
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"""
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st.sidebar.title("🛠️ Personalization & Settings 🏗️")
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# Configure content personalization settings
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blog_content_config = configure_content_personalization()
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# Configure image personalization settings
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blog_images_config = configure_images_personalization()
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# Configure LLM personalization settings
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llm_config = configure_llm_personalization()
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# Configure search engine personalization settings
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search_engine_config = configure_search_engine_personalization()
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# Combine all configurations into a dictionary
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with st.sidebar.expander("**👷 Content Personalization**"):
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blog_length = st.text_input("**Content Length (words)**", value="2000",
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help="Approximate word count for blogs. Note: Actual length may vary based on GPT provider and max token count.")
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blog_tone_options = ["Casual", "Professional", "How-to", "Beginner", "Research", "Programming", "Social Media", "Customize"]
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blog_tone = st.selectbox("**Content Tone**",
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options=blog_tone_options,
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help="Select the desired tone for the blog content.")
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if blog_tone == "Customize":
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custom_tone = st.text_input("Enter the tone of your content", help="Specify the tone of your content.")
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if custom_tone:
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blog_tone = custom_tone
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else:
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st.warning("Please specify the tone of your content.")
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blog_demographic_options = ["Professional", "Gen-Z", "Tech-savvy", "Student", "Digital Marketing", "Customize"]
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blog_demographic = st.selectbox("**Target Audience**",
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options=blog_demographic_options,
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help="Select the primary audience for the blog content.")
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if blog_demographic == "Customize":
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custom_demographic = st.text_input("Enter your target audience",
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help="Specify your target audience.",
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placeholder="Eg. Domain expert, Content creator, Financial expert etc..")
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if custom_demographic:
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blog_demographic = custom_demographic
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else:
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st.warning("Please specify your target audience.")
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blog_type = st.selectbox("**Content Type**",
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options=["Informational", "Commercial", "Company", "News", "Finance", "Competitor", "Programming", "Scholar"],
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help="Select the category that best describes the blog content.")
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blog_language = st.selectbox("**Content Language**",
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options=["English", "Spanish", "German", "Chinese", "Arabic", "Nepali", "Hindi", "Hindustani", "Customize"],
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help="Select the language in which the blog will be written.")
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if blog_language == "Customize":
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custom_lang = st.text_input("Enter the language of your choice", help="Specify the content language.")
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if custom_lang:
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blog_language = custom_lang
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else:
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st.warning("Please specify the language of your content.")
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blog_output_format = st.selectbox("**Content Output Format**",
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options=["markdown", "HTML", "plaintext"],
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help="Select the format for the blog output.")
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with st.sidebar.expander("**🩻 Images Personalization**"):
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image_generation_model = st.selectbox("**Image Generation Model**",
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options=["stable-diffusion", "dalle2", "dalle3"],
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help="Select the model to generate images for the blog.")
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number_of_blog_images = st.number_input("**Number of Blog Images**", value=1, help="Specify the number of images to include in the blog.")
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with st.sidebar.expander("**🤖 LLM Personalization**"):
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gpt_provider = st.selectbox("**GPT Provider**",
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options=["google", "openai", "minstral"],
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help="Select the provider for the GPT model.")
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model = st.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 = st.slider(
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"Temperature",
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min_value=0.1,
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max_value=1.0,
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value=0.7,
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step=0.1,
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format="%.1f",
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help="""Temperature controls the 'creativity' or randomness of the text generated by GPT.
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Greater determinism with higher values indicating more randomness."""
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)
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top_p = st.slider(
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"Top-p",
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min_value=0.0,
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max_value=1.0,
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value=0.9,
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step=0.1,
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format="%.1f",
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help="Top-p sampling controls the level of diversity in the generated text."
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)
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# Selectbox for max tokens
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max_tokens_options = [500, 1000, 2000, 4000, 16000, 32000, 64000]
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max_tokens = st.selectbox(
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"Max Tokens",
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options=max_tokens_options,
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index=max_tokens_options.index(4000),
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help="Max tokens determine the maximum length of the output sequence generated by a model."
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)
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n = st.number_input("N",
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value=1,
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min_value=1,
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max_value=10,
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help="Defines the number of words or characters grouped together in a sequence when analyzing text.")
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frequency_penalty = st.slider(
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"Frequency Penalty",
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min_value=0.0,
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max_value=2.0,
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value=1.0,
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step=0.1,
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format="%.1f",
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help="Influences word selection during text generation, promoting diversity with higher values."
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)
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presence_penalty = st.slider(
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"Presence Penalty",
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min_value=0.0,
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max_value=2.0,
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value=1.0,
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step=0.1,
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format="%.1f",
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help="Encourages the use of diverse words by discouraging repetition."
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)
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with st.sidebar.expander("**🕵️ Search Engine Personalization**"):
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geographic_location = st.selectbox("**Geographic Location**",
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options=["us", "in", "fr", "cn"],
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help="Select the geographic location for tailoring search results.")
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search_language = st.selectbox("**Search Language**",
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options=["en", "zn-cn", "de", "hi"],
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help="Select the language for the search results.")
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number_of_results = st.number_input("**Number of Results**",
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value=10,
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max_value=20,
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min_value=1,
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help="Specify the number of search results to retrieve.")
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time_range = st.selectbox("**Time Range**",
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options=["anytime", "past day", "past week", "past month", "past year"],
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help="Select the time range for filtering search results.")
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include_domains = st.text_input("**Include Domains**", value="",
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help="List specific domains to include in search results. Leave blank to include all domains.")
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similar_url = st.text_input("**Similar URL**", value="", help="Provide a URL to find similar results. Leave blank if not needed.")
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# Storing collected inputs in a dictionary
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config = {
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"Blog Content Characteristics": blog_content_config,
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"Blog Images Details": blog_images_config,
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"LLM Options": llm_config,
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"Search Engine Parameters": search_engine_config
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"Blog Content Characteristics": {
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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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"Blog Images Details": {
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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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"LLM Options": {
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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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"Search Engine Parameters": {
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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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}
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# Save the configuration whenever a change is made
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# Writing the configuration to a file whenever a change is made
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save_config(config)
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def main():
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#load_environment
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load_dotenv()
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Reference in New Issue
Block a user