438 lines
20 KiB
Python
438 lines
20 KiB
Python
import streamlit as st
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from loguru import logger
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import asyncio
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from lib.web_crawlers.async_web_crawler import AsyncWebCrawlerService
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from lib.personalization.style_analyzer import StyleAnalyzer
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import sys
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# Configure logger
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logger.remove() # Remove default handler
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logger.add(
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"logs/settings_page.log",
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rotation="500 MB",
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retention="10 days",
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level="DEBUG",
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format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}",
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backtrace=True,
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diagnose=True
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)
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logger.add(
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sys.stdout,
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level="INFO",
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format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{message}</cyan>"
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)
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def display_style_analysis(analysis_results: dict):
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"""Display the style analysis results in a structured format."""
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try:
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# Writing Style Section
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st.markdown("### 🎨 Writing Style Analysis")
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writing_style = analysis_results.get("writing_style", {})
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writing_style_content = f"""
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<ul>
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<li><strong>Tone:</strong> {writing_style.get("tone", "N/A")}</li>
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<li><strong>Voice:</strong> {writing_style.get("voice", "N/A")}</li>
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<li><strong>Complexity:</strong> {writing_style.get("complexity", "N/A")}</li>
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<li><strong>Engagement Level:</strong> {writing_style.get("engagement_level", "N/A")}</li>
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</ul>
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"""
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st.markdown(writing_style_content, unsafe_allow_html=True)
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# Content Characteristics Section
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content_chars = analysis_results.get("content_characteristics", {})
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content_chars_content = f"""
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<ul>
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<li><strong>Sentence Structure:</strong> {content_chars.get("sentence_structure", "N/A")}</li>
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<li><strong>Vocabulary Level:</strong> {content_chars.get("vocabulary_level", "N/A")}</li>
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<li><strong>Paragraph Organization:</strong> {content_chars.get("paragraph_organization", "N/A")}</li>
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<li><strong>Content Flow:</strong> {content_chars.get("content_flow", "N/A")}</li>
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</ul>
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"""
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st.markdown(content_chars_content, unsafe_allow_html=True)
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# Target Audience Section
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target_audience = analysis_results.get("target_audience", {})
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target_audience_content = f"""
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<ul>
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<li><strong>Demographics:</strong> {', '.join(target_audience.get("demographics", ["N/A"]))}</li>
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<li><strong>Expertise Level:</strong> {target_audience.get("expertise_level", "N/A")}</li>
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<li><strong>Industry Focus:</strong> {target_audience.get("industry_focus", "N/A")}</li>
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<li><strong>Geographic Focus:</strong> {target_audience.get("geographic_focus", "N/A")}</li>
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</ul>
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"""
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st.markdown(target_audience_content, unsafe_allow_html=True)
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# Content Type Section
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content_type = analysis_results.get("content_type", {})
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content_type_content = f"""
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<ul>
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<li><strong>Primary Type:</strong> {content_type.get("primary_type", "N/A")}</li>
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<li><strong>Secondary Types:</strong> {', '.join(content_type.get("secondary_types", ["N/A"]))}</li>
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<li><strong>Purpose:</strong> {content_type.get("purpose", "N/A")}</li>
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<li><strong>Call to Action:</strong> {content_type.get("call_to_action", "N/A")}</li>
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</ul>
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"""
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st.markdown(content_type_content, unsafe_allow_html=True)
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# Recommended Settings Section
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recommended = analysis_results.get("recommended_settings", {})
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recommended_content = f"""
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<ul>
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<li><strong>Writing Tone:</strong> {recommended.get("writing_tone", "N/A")}</li>
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<li><strong>Target Audience:</strong> {recommended.get("target_audience", "N/A")}</li>
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<li><strong>Content Type:</strong> {recommended.get("content_type", "N/A")}</li>
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<li><strong>Creativity Level:</strong> {recommended.get("creativity_level", "N/A")}</li>
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<li><strong>Geographic Location:</strong> {recommended.get("geographic_location", "N/A")}</li>
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</ul>
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"""
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st.markdown(recommended_content, unsafe_allow_html=True)
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except Exception as e:
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logger.error(f"Error displaying style analysis: {str(e)}")
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st.error(f"Error displaying analysis results: {str(e)}")
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def render_settings_page():
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"""Renders the settings page with all configuration options in tabs"""
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st.title("🛠️ Settings & Configuration")
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# Create tabs for different settings categories
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tabs = st.tabs([
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"👷 Content",
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"🩻 Images",
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"🤖 LLM",
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"🕵️ Search",
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"🎨 AI Personalization"
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])
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# Content Settings Tab
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with tabs[0]:
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st.header("Content Personalization")
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blog_length = st.text_input(
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"**Content Length (words)**",
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value="2000",
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key="settings_blog_length",
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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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)
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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(
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"**Content Tone**",
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options=blog_tone_options,
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key="settings_blog_tone",
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help="Select the desired tone for the blog content."
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)
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if blog_tone == "Customize":
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custom_tone = st.text_input(
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"Enter the tone of your content",
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key="settings_custom_tone",
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help="Specify the tone of your content."
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)
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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(
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"**Target Audience**",
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options=blog_demographic_options,
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key="settings_blog_demographic",
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help="Select the primary audience for the blog content."
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)
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blog_type = st.selectbox(
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"**Content Type**",
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options=["Informational", "Commercial", "Company", "News", "Finance", "Competitor", "Programming", "Scholar"],
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key="settings_blog_type",
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help="Select the category that best describes the blog content."
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)
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blog_language = st.selectbox(
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"**Content Language**",
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options=["English", "Spanish", "German", "Chinese", "Arabic", "Nepali", "Hindi", "Hindustani", "Customize"],
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key="settings_blog_language",
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help="Select the language in which the blog will be written."
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)
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blog_output_format = st.selectbox(
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"**Content Output Format**",
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options=["markdown", "HTML", "plaintext"],
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key="settings_blog_output_format",
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help="Select the format for the blog output."
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)
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# Images Settings Tab
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with tabs[1]:
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st.header("Images Personalization")
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image_generation_model = st.selectbox(
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"**Image Generation Model**",
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options=["stable-diffusion", "dalle2", "dalle3"],
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key="settings_image_model",
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help="Select the model to generate images for the blog."
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)
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number_of_blog_images = st.number_input(
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"**Number of Blog Images**",
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value=1,
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min_value=1,
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max_value=10,
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key="settings_number_of_images",
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help="Specify the number of images to include in the blog."
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)
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# LLM Settings Tab
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with tabs[2]:
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st.header("LLM Personalization")
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gpt_provider = st.selectbox(
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"**GPT Provider**",
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options=["google", "openai", "minstral"],
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key="settings_gpt_provider",
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help="Select the provider for the GPT model."
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)
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model = st.text_input(
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"**Model**",
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value="gemini-1.5-flash-latest",
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key="settings_model",
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help="Specify the model version to use from the selected provider."
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)
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col1, col2 = st.columns(2)
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with col1:
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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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key="settings_temperature",
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help="Controls the creativity level of the generated text."
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)
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max_tokens = st.selectbox(
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"Max Tokens",
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options=[500, 1000, 2000, 4000, 16000, 32000, 64000],
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index=3,
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key="settings_max_tokens",
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help="Maximum length of the output sequence."
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)
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with col2:
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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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key="settings_top_p",
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help="Controls diversity in text generation."
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)
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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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key="settings_frequency_penalty",
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help="Reduces word repetition in output."
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)
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# Search Settings Tab
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with tabs[3]:
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st.header("Search Engine Personalization")
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geographic_location = st.selectbox(
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"**Geographic Location**",
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options=["us", "in", "fr", "cn"],
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key="settings_geographic_location",
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help="Select the geographic location for tailoring search results."
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)
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search_language = st.selectbox(
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"**Search Language**",
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options=["en", "zn-cn", "de", "hi"],
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key="settings_search_language",
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help="Select the language for the search results."
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)
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number_of_results = st.number_input(
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"**Number of Results**",
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value=10,
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min_value=1,
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max_value=20,
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key="settings_number_of_results",
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help="Specify the number of search results to retrieve."
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)
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time_range = st.selectbox(
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"**Time Range**",
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options=["anytime", "past day", "past week", "past month", "past year"],
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key="settings_time_range",
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help="Select the time range for filtering search results."
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)
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include_domains = st.text_input(
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"**Include Domains**",
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value="",
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key="settings_include_domains",
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help="List specific domains to include in search results (comma-separated)."
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)
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similar_url = st.text_input(
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"**Similar URL**",
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value="",
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key="settings_similar_url",
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help="Provide a URL to find similar results."
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)
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# AI Personalization Tab
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with tabs[4]:
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st.header("🎨 AI Style Analysis")
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st.markdown("""
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<div style='background-color: rgba(255, 255, 255, 0.1); padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
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<p>Enter a website URL or provide content samples to analyze your writing style and get personalized recommendations.</p>
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</div>
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""", unsafe_allow_html=True)
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# Create two columns for the layout
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col1, col2 = st.columns([2, 1])
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with col1:
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# Website URL input
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st.markdown("### Website URL")
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url = st.text_input(
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"Enter your website URL",
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placeholder="https://example.com",
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key="settings_website_url",
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help="Provide your website URL to analyze your content style. Leave empty if you want to provide written samples instead."
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)
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# Alternative: Written samples
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if not url:
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st.markdown("### Written Samples")
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st.markdown("""
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<div style='background-color: rgba(255, 255, 255, 0.1); padding: 20px; border-radius: 10px; margin-bottom: 20px;'>
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<p>No website URL? No problem! You can provide written samples of your content instead.</p>
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<p>Share your best articles, blog posts, or any content that represents your writing style.</p>
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</div>
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""", unsafe_allow_html=True)
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samples = st.text_area(
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"Paste your content samples here",
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key="settings_content_samples",
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help="Paste 2-3 samples of your best content. This helps ALwrity understand your writing style."
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)
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# ALwrity Style button
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st.markdown("<div style='height: 20px'></div>", unsafe_allow_html=True)
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if st.button("🎨 Analyze Style", use_container_width=True, key="settings_analyze_style"):
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if url:
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with st.status("Starting style analysis...", expanded=True) as status:
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try:
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# Step 1: Initialize crawler
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status.update(label="Step 1/4: Initializing web crawler...", state="running")
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crawler_service = AsyncWebCrawlerService()
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# Step 2: Crawl website
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status.update(label="Step 2/4: Crawling website content...", state="running")
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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result = loop.run_until_complete(crawler_service.crawl_website(url))
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loop.close()
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if result.get('success', False):
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content = result.get('content', {})
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# Step 3: Initialize style analyzer
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status.update(label="Step 3/4: Analyzing content style...", state="running")
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style_analyzer = StyleAnalyzer()
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# Step 4: Perform style analysis
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status.update(label="Step 4/4: Generating style recommendations...", state="running")
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style_analysis = style_analyzer.analyze_content_style(content)
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if style_analysis.get('error'):
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status.update(label="Analysis failed", state="error")
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st.error(f"Style analysis failed: {style_analysis['error']}")
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else:
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status.update(label="Analysis complete!", state="complete")
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# Display style analysis results
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display_style_analysis(style_analysis)
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# Display original content in tabs
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tab1, tab2, tab3 = st.tabs(["Content", "Metadata", "Links"])
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with tab1:
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st.markdown("### Main Content")
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st.markdown(content.get('main_content', 'No content found'))
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with tab2:
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st.markdown("### Metadata")
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st.markdown(f"""
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**Title:** {content.get('title', 'No title found')}
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**Description:** {content.get('description', 'No description found')}
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**Meta Tags:**
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{content.get('meta_tags', {})}
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""")
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with tab3:
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st.markdown("### Links")
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for link in content.get('links', []):
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st.markdown(f"- [{link.get('text', '')}]({link.get('href', '')})")
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else:
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status.update(label="Crawling failed", state="error")
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st.error("Failed to crawl the website. Please check the URL and try again.")
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except Exception as e:
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status.update(label="Analysis failed", state="error")
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st.error(f"An error occurred during analysis: {str(e)}")
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elif samples:
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with st.status("Starting style analysis...", expanded=True) as status:
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try:
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# Initialize style analyzer
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status.update(label="Analyzing content style...", state="running")
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style_analyzer = StyleAnalyzer()
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# Perform style analysis
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style_analysis = style_analyzer.analyze_content_style({"main_content": samples})
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if style_analysis.get('error'):
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status.update(label="Analysis failed", state="error")
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st.error(f"Style analysis failed: {style_analysis['error']}")
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else:
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status.update(label="Analysis complete!", state="complete")
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# Display style analysis results
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display_style_analysis(style_analysis)
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except Exception as e:
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status.update(label="Analysis failed", state="error")
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st.error(f"An error occurred during analysis: {str(e)}")
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else:
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st.warning("Please provide either a website URL or content samples to analyze.")
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# Save Settings Button
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if st.button("💾 Save Settings", type="primary", use_container_width=True, key="settings_save_button"):
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# Save all settings to session state
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st.session_state.update({
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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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'image_generation_model': image_generation_model,
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'number_of_blog_images': number_of_blog_images,
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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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'frequency_penalty': frequency_penalty,
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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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st.success("✅ Settings saved successfully!") |