diff --git a/lib/utils/api_key_manager/components/final_setup.py b/lib/utils/api_key_manager/components/final_setup.py index 6f7ea881..eba0256c 100644 --- a/lib/utils/api_key_manager/components/final_setup.py +++ b/lib/utils/api_key_manager/components/final_setup.py @@ -192,6 +192,49 @@ def render_final_setup(api_key_manager: APIKeyManager) -> Dict[str, Any]: if st.button("Complete Setup →"): logger.info("[render_final_setup] User clicked complete setup") try: + # First set FINAL_SETUP_COMPLETE to True + try: + # Read existing .env content + env_lines = [] + if os.path.exists('.env'): + with open('.env', 'r') as f: + env_lines = f.readlines() + + # Remove any existing FINAL_SETUP_COMPLETE entries + env_lines = [line for line in env_lines if not line.startswith('FINAL_SETUP_COMPLETE=')] + + # Add the new FINAL_SETUP_COMPLETE entry + env_lines.append("FINAL_SETUP_COMPLETE=True\n") + + # Write back to .env file + with open('.env', 'w') as f: + f.writelines(env_lines) + + # Set environment variable + os.environ['FINAL_SETUP_COMPLETE'] = "True" + logger.info("[render_final_setup] Set FINAL_SETUP_COMPLETE=True") + except Exception as e: + logger.error(f"[render_final_setup] Error setting FINAL_SETUP_COMPLETE: {str(e)}") + st.error("Error updating setup status. Please try again.") + return {"current_step": 6, "changes_made": False} + + # Now validate all steps + validation_result = check_all_api_keys(api_key_manager) + if not validation_result: + # If validation fails, revert FINAL_SETUP_COMPLETE + try: + env_lines = [line for line in env_lines if not line.startswith('FINAL_SETUP_COMPLETE=')] + env_lines.append("FINAL_SETUP_COMPLETE=False\n") + with open('.env', 'w') as f: + f.writelines(env_lines) + os.environ['FINAL_SETUP_COMPLETE'] = "False" + except Exception: + pass # Ignore reversion errors + + st.error("Setup validation failed. Please ensure all required steps are completed.") + logger.error("[render_final_setup] Validation failed") + return {"current_step": 6, "changes_made": False} + # Log the current API keys in the manager logger.info("[render_final_setup] Current API keys in manager:") for key, value in api_key_manager.api_keys.items(): @@ -200,46 +243,30 @@ def render_final_setup(api_key_manager: APIKeyManager) -> Dict[str, Any]: else: logger.info(f" - {key}: Not set") - # Log environment variables - logger.info("[render_final_setup] Checking environment variables:") - for key in os.environ.keys(): - if any(provider in key for provider in ['API_KEY', 'SERPAPI', 'TAVILY', 'METAPHOR', 'FIRECRAWL']): - value = os.getenv(key) - if value: - logger.info(f" - {key}: {'*' * 8}{value[-4:]}") - else: - logger.error(f" - {key}: Not set") - + # Save main configuration config_path = os.path.join("lib", "workspace", "alwrity_config", "main_config.json") with open(config_path, 'w') as f: json.dump(main_config, f, indent=4) + logger.info("[render_final_setup] Saved main configuration") - # Set FINAL_SETUP_COMPLETE to True in .env file and environment - try: - with open('.env', 'a') as f: - f.write("\nFINAL_SETUP_COMPLETE=True") - os.environ['FINAL_SETUP_COMPLETE'] = "True" - logger.info("Set FINAL_SETUP_COMPLETE=True in .env and environment") - except Exception as e: - logger.error(f"Failed to set FINAL_SETUP_COMPLETE: {str(e)}") - - # Show success message with HTML formatting - st.markdown(""" -
-

✅ Setup Completed Successfully!

-

Your configuration has been saved and you're ready to use ALwrity.

-
- """, unsafe_allow_html=True) + # Show success message + st.success("✅ Setup completed successfully! Redirecting to main application...") # Set setup completion flag in session state st.session_state['setup_completed'] = True + st.session_state['redirect_to_main'] = True - # Redirect to main application + # Clear the current step to ensure proper redirection + if 'current_step' in st.session_state: + del st.session_state['current_step'] + + # Rerun to trigger redirection st.rerun() except Exception as e: error_msg = f"Error completing setup: {str(e)}" logger.error(f"[render_final_setup] {error_msg}") st.error(error_msg) + return {"current_step": 6, "changes_made": False} return {"current_step": 6, "changes_made": True} \ No newline at end of file diff --git a/lib/utils/api_key_manager/components/personalization_setup.py b/lib/utils/api_key_manager/components/personalization_setup.py index 3b19ae33..046c664e 100644 --- a/lib/utils/api_key_manager/components/personalization_setup.py +++ b/lib/utils/api_key_manager/components/personalization_setup.py @@ -122,610 +122,366 @@ def render_personalization_setup(api_key_manager: APIKeyManager) -> Dict[str, An logger.info("[render_personalization_setup] Rendering personalization setup component") st.markdown(""" -
-

✨ Personalization Setup

-

Configure your content generation preferences and writing style

-
- """, unsafe_allow_html=True) + # ✨ Personalization Setup + Configure your content generation preferences and writing style + """) - # Load main config - main_config = load_main_config() + # Main section selection using radio buttons + setup_mode = st.radio( + "Choose Setup Mode", + ["Manual Settings", "ALwrity Personalization"], + horizontal=True, + label_visibility="collapsed" + ) - # Create tabs for different personalization methods - tab1, tab2 = st.tabs([ - "Manual Settings", - "ALwrity Personalization" - ]) - - with tab1: - st.markdown("### Manual Settings Configuration") + if setup_mode == "Manual Settings": + # Create tabs for different settings categories + tabs = st.tabs([ + "Blog Content Characteristics", + "Blog Images", + "AI Generation Settings", + "Search Settings" + ]) - # Add container for better width control - st.markdown(""" -
- """, unsafe_allow_html=True) - - # Create two columns for settings and explanations (1:2 ratio) - settings_col, info_col = st.columns([1, 2]) - - with settings_col: - st.markdown(""" -
- """, unsafe_allow_html=True) + # Blog Content Characteristics Tab + with tabs[0]: + col1, col2 = st.columns([1, 1]) - # Blog Content Characteristics - st.markdown("#### Blog Content Characteristics") - blog_settings = main_config.get("Blog Content Characteristics", {}) + with col1: + st.markdown("#### Blog Content Characteristics") + + blog_length = st.text_input( + "Blog Length", + value="2000", + placeholder="e.g., 2000", + help="Target word count for your blog posts" + ) + + blog_tone = st.selectbox( + "Blog Tone", + ["Professional", "Casual", "Technical", "Conversational"], + help="The overall tone of your content" + ) + + blog_demographic = st.selectbox( + "Target Demographic", + ["Professional", "General", "Technical", "Academic"], + help="Your primary audience demographic" + ) + + blog_type = st.selectbox( + "Content Type", + ["Informational", "Educational", "Entertainment", "Technical"], + help="The primary type of content you create" + ) + + blog_language = st.selectbox( + "Content Language", + ["English", "Spanish", "French", "German", "Other"], + help="Primary language for your content" + ) + + blog_format = st.selectbox( + "Output Format", + ["markdown", "html", "plain text"], + help="Format of the generated content" + ) - blog_length = st.text_input( - "Blog Length", - value=blog_settings.get("Blog Length", "2000"), - placeholder="e.g., 2000", - help="Target word count for your blog posts" - ) - - blog_tone = st.selectbox( - "Blog Tone", - options=["Professional", "Casual", "Technical", "Conversational"], - index=["Professional", "Casual", "Technical", "Conversational"].index(blog_settings.get("Blog Tone", "Professional")), - help="The overall tone of your content" - ) - - blog_demographic = st.selectbox( - "Target Demographic", - options=["Professional", "General", "Technical", "Academic"], - index=["Professional", "General", "Technical", "Academic"].index(blog_settings.get("Blog Demographic", "Professional")), - help="Your primary audience demographic" - ) - - blog_type = st.selectbox( - "Content Type", - options=["Informational", "Educational", "Entertainment", "Technical"], - index=["Informational", "Educational", "Entertainment", "Technical"].index(blog_settings.get("Blog Type", "Informational")), - help="The primary type of content you create" - ) - - blog_language = st.selectbox( - "Content Language", - options=["English", "Spanish", "French", "German", "Other"], - index=["English", "Spanish", "French", "German", "Other"].index(blog_settings.get("Blog Language", "English")), - help="Primary language for your content" - ) - - blog_format = st.selectbox( - "Output Format", - options=["markdown", "html", "plain text"], - index=["markdown", "html", "plain text"].index(blog_settings.get("Blog Output Format", "markdown")), - help="Format of the generated content" - ) - - # Blog Images Details - st.markdown("#### Blog Images") - image_settings = main_config.get("Blog Images Details", {}) - - image_model = st.selectbox( - "Image Generation Model", - options=["stable-diffusion", "dall-e", "midjourney"], - index=["stable-diffusion", "dall-e", "midjourney"].index(image_settings.get("Image Generation Model", "stable-diffusion")), - help="AI model for generating images" - ) - - num_images = st.number_input( - "Number of Images", - min_value=1, - max_value=5, - value=image_settings.get("Number of Blog Images", 1), - help="Number of images to generate per blog post" - ) - - # LLM Options - st.markdown("#### AI Generation Settings") - llm_settings = main_config.get("LLM Options", {}) - - gpt_provider = st.selectbox( - "AI Provider", - options=["google", "openai", "anthropic"], - index=["google", "openai", "anthropic"].index(llm_settings.get("GPT Provider", "google")), - help="Choose your preferred AI provider" - ) - - model = st.text_input( - "Model", - value=llm_settings.get("Model", "gemini-1.5-flash-latest"), - placeholder="e.g., gemini-1.5-flash-latest", - help="The specific AI model to use" - ) - - temperature = st.slider( - "Creativity Level", - min_value=0.0, - max_value=1.0, - value=float(llm_settings.get("Temperature", 0.7)), - help="Higher values = more creative, lower values = more focused" - ) - - top_p = st.slider( - "Output Diversity", - min_value=0.0, - max_value=1.0, - value=float(llm_settings.get("Top-p", 0.9)), - help="Controls diversity of generated content" - ) - - max_tokens = st.number_input( - "Maximum Length", - min_value=100, - max_value=8000, - value=int(llm_settings.get("Max Tokens", 4000)), - help="Maximum length of generated content" - ) - - frequency_penalty = st.slider( - "Frequency Penalty", - min_value=-2.0, - max_value=2.0, - value=float(llm_settings.get("Frequency Penalty", 1.0)), - help="Reduces repetition of the same words" - ) - - presence_penalty = st.slider( - "Presence Penalty", - min_value=-2.0, - max_value=2.0, - value=float(llm_settings.get("Presence Penalty", 1.0)), - help="Encourages discussion of new topics" - ) - - # Search Engine Parameters - st.markdown("#### Search Settings") - search_settings = main_config.get("Search Engine Parameters", {}) - - geo_location = st.text_input( - "Geographic Location", - value=search_settings.get("Geographic Location", "us"), - placeholder="e.g., us, uk, ca", - help="Target geographic location for search results" - ) - - search_language = st.selectbox( - "Search Language", - options=["en", "es", "fr", "de", "other"], - index=["en", "es", "fr", "de", "other"].index(search_settings.get("Search Language", "en")), - help="Language for search results" - ) - - num_results = st.number_input( - "Number of Results", - min_value=1, - max_value=50, - value=search_settings.get("Number of Results", 10), - help="Number of search results to analyze" - ) - - time_range = st.selectbox( - "Time Range", - options=["anytime", "day", "week", "month", "year"], - index=["anytime", "day", "week", "month", "year"].index(search_settings.get("Time Range", "anytime")), - help="Time range for search results" - ) - - st.markdown("
", unsafe_allow_html=True) - - with info_col: - st.markdown(""" -
- """, unsafe_allow_html=True) - - st.markdown(""" -
- ### Understanding Your Settings - - #### Blog Content Settings - - **Blog Length** - - Determines the target word count for your posts - - Affects content depth and detail level - - Impacts reader engagement and SEO performance - - Recommended: 1500-2500 words for comprehensive coverage - - **Blog Tone** - - Professional: Formal, business-oriented, authoritative - - Casual: Friendly, conversational, approachable - - Technical: Detailed, precise, industry-specific - - Conversational: Engaging, relatable, personal - - **Target Demographic** - - Professional: Business audience, decision-makers - - General: Broad readership, general public - - Technical: Specialized audience, industry experts - - Academic: Research-focused, scholarly readers - - **Content Type** - - Informational: Facts, insights, and analysis - - Educational: Teaching, tutorials, how-to guides - - Entertainment: Engaging, fun, light content - - Technical: Detailed analysis, specifications - - **Content Language** - - Select your primary content language - - Affects grammar, idioms, and cultural context - - Impacts SEO and audience reach - - **Output Format** - - Markdown: Best for most platforms - - HTML: For web publishing - - Plain Text: For simple content - - #### Image Generation Settings - - **Image Generation Model** - - Stable Diffusion: Best for general content - - DALL-E: Great for creative concepts - - Midjourney: Excellent for artistic content - - **Number of Images** - - Consider your content type and platform - - More images = better engagement but higher cost - - Recommended: 1-2 images per post - - #### AI Generation Settings - - **AI Provider** - - Google: Balanced, reliable, cost-effective - - OpenAI: Creative, nuanced, versatile - - Anthropic: Precise, ethical, focused - - **Model Selection** - - Latest models offer best performance - - Specialized models for specific needs - - Consider cost vs. quality trade-offs - - **Creativity Level (Temperature)** - - 0.0: Focused, consistent, predictable - - 0.5: Balanced creativity and coherence - - 1.0: Maximum creativity, more varied - - **Output Diversity (Top-p)** - - Controls variety in word choices - - Higher values = more diverse vocabulary - - Lower values = more focused terminology - - **Maximum Length** - - Affects content completeness - - Consider platform limits - - Balance detail vs. readability - - **Frequency & Presence Penalties** - - Reduce repetition of words - - Encourage topic diversity - - Fine-tune content variety - - #### Search Settings - - **Geographic Location** - - Target specific regions - - Affects local SEO - - Influences content relevance - - **Search Language** - - Match your content language - - Affects result relevance + with col2: + st.markdown("### Blog Content Settings Guide") + + st.markdown(""" + #### Blog Length + - Determines word count target + - Affects content depth - Impacts SEO performance - **Number of Results** - - More results = better analysis - - Consider processing time - - Balance quality vs. speed + #### Blog Tone + - Professional: Business-oriented + - Casual: Friendly, approachable + - Technical: Detailed, precise - **Time Range** - - Anytime: All available content - - Recent: Latest information - - Historical: Past content - - ### Best Practices - - 1. **Start Conservative** - - Begin with moderate settings - - Adjust based on results - - Monitor performance - - 2. **Consider Your Audience** - - Match tone to reader expectations - - Adjust complexity appropriately - - Focus on value delivery - - 3. **Optimize for Platform** - - Consider platform limitations - - Match format requirements - - Optimize for engagement - - 4. **Regular Review** - - Monitor content performance - - Adjust settings as needed - - Stay updated with trends -
- """, unsafe_allow_html=True) - - st.markdown("
", unsafe_allow_html=True) + #### Best Practices + - Match tone to audience + - Consider SEO requirements + - Maintain consistency + """) - # Close the container - st.markdown("
", unsafe_allow_html=True) - - # Add some spacing before the save button - st.markdown("
", unsafe_allow_html=True) - - if st.button("Save Manual Settings", type="primary", use_container_width=True): - # Update main config with new values - main_config["Blog Content Characteristics"] = { - "Blog Length": blog_length, - "Blog Tone": blog_tone, - "Blog Demographic": blog_demographic, - "Blog Type": blog_type, - "Blog Language": blog_language, - "Blog Output Format": blog_format - } + # Blog Images Tab + with tabs[1]: + col1, col2 = st.columns([1, 1]) - main_config["Blog Images Details"] = { - "Image Generation Model": image_model, - "Number of Blog Images": num_images - } - - main_config["LLM Options"] = { - "GPT Provider": gpt_provider, - "Model": model, - "Temperature": temperature, - "Top-p": top_p, - "Max Tokens": max_tokens, - "Frequency Penalty": frequency_penalty, - "Presence Penalty": presence_penalty - } - - main_config["Search Engine Parameters"] = { - "Geographic Location": geo_location, - "Search Language": search_language, - "Number of Results": num_results, - "Time Range": time_range - } - - if save_main_config(main_config): - # Set PERSONALIZATION_DONE to True in .env file and environment - try: - with open('.env', 'a') as f: - f.write("\nPERSONALIZATION_DONE=True") - os.environ['PERSONALIZATION_DONE'] = "True" - logger.info("Set PERSONALIZATION_DONE=True in .env and environment") - except Exception as e: - logger.error(f"Failed to set PERSONALIZATION_DONE: {str(e)}") + with col1: + st.markdown("#### Blog Images Settings") - st.success("✅ Your personalization settings have been saved successfully!") - else: - st.error("Unable to save settings. Please try again.") - - with tab2: - st.markdown("#### ALwrity Personalization") + image_model = st.selectbox( + "Image Generation Model", + ["stable-diffusion", "dall-e", "midjourney"], + help="AI model for generating images" + ) + + num_images = st.number_input( + "Number of Images", + min_value=1, + max_value=5, + value=1, + help="Number of images per blog post" + ) + + image_style = st.selectbox( + "Image Style", + ["Realistic", "Artistic", "Professional", "Creative"], + help="Style of generated images" + ) + + with col2: + st.markdown("### Image Generation Guide") + + st.markdown(""" + #### Model Selection + - Stable Diffusion: Versatile, fast + - DALL-E: High quality, creative + - Midjourney: Artistic, detailed + + #### Best Practices + - Consider content type + - Balance quality vs. speed + - Optimize for platforms + """) - # Create two columns for the layout + # AI Generation Settings Tab + with tabs[2]: + col1, col2 = st.columns([1, 1]) + + with col1: + st.markdown("#### AI Generation Settings") + + gpt_provider = st.selectbox( + "AI Provider", + ["google", "openai", "anthropic"], + help="Choose your preferred AI provider" + ) + + model = st.text_input( + "Model", + value="gemini-1.5-flash-latest", + help="The specific AI model to use" + ) + + temperature = st.slider( + "Creativity Level", + min_value=0.0, + max_value=1.0, + value=0.7, + help="Higher = more creative, lower = more focused" + ) + + max_tokens = st.number_input( + "Maximum Length", + min_value=100, + max_value=8000, + value=4000, + help="Maximum length of generated content" + ) + + with col2: + st.markdown("### AI Settings Guide") + + st.markdown(""" + #### Provider Selection + - Google: Balanced, reliable + - OpenAI: Creative, versatile + - Anthropic: Precise, ethical + + #### Temperature Guide + - 0.0-0.3: Focused, consistent + - 0.4-0.7: Balanced creativity + - 0.8-1.0: Highly creative + """) + + # Search Settings Tab + with tabs[3]: + col1, col2 = st.columns([1, 1]) + + with col1: + st.markdown("#### Search Settings") + + geo_location = st.text_input( + "Geographic Location", + value="us", + help="Target geographic location for search" + ) + + search_language = st.selectbox( + "Search Language", + ["en", "es", "fr", "de", "other"], + help="Language for search results" + ) + + num_results = st.number_input( + "Number of Results", + min_value=1, + max_value=50, + value=10, + help="Number of search results to analyze" + ) + + time_range = st.selectbox( + "Time Range", + ["anytime", "day", "week", "month", "year"], + help="Time range for search results" + ) + + with col2: + st.markdown("### Search Settings Guide") + + st.markdown(""" + #### Location & Language + - Affects result relevance + - Impacts local SEO + - Consider target market + + #### Search Optimization + - Balance quantity vs. quality + - Consider time sensitivity + - Optimize for accuracy + """) + + # Save button for manual settings + if st.button("Save Manual Settings", type="primary", use_container_width=True): + try: + # Save to main_config.json + config = { + "Blog Content Characteristics": { + "Blog Length": blog_length, + "Blog Tone": blog_tone, + "Blog Demographic": blog_demographic, + "Blog Type": blog_type, + "Blog Language": blog_language, + "Blog Output Format": blog_format + }, + "Blog Images Details": { + "Image Generation Model": image_model, + "Number of Blog Images": num_images, + "Image Style": image_style + }, + "LLM Options": { + "GPT Provider": gpt_provider, + "Model": model, + "Temperature": temperature, + "Max Tokens": max_tokens + }, + "Search Engine Parameters": { + "Geographic Location": geo_location, + "Search Language": search_language, + "Number of Results": num_results, + "Time Range": time_range + } + } + + if save_main_config(config): + try: + # Read existing .env file content + env_lines = [] + if os.path.exists('.env'): + with open('.env', 'r') as f: + env_lines = f.readlines() + + # Remove any existing PERSONALIZATION_DONE entries + env_lines = [line for line in env_lines if not line.startswith('PERSONALIZATION_DONE=')] + + # Add new PERSONALIZATION_DONE entry + env_lines.append("PERSONALIZATION_DONE=True\n") + + # Write back to .env file + with open('.env', 'w') as f: + f.writelines(env_lines) + + # Update environment variable and session state + os.environ['PERSONALIZATION_DONE'] = "True" + st.session_state['personalization_saved'] = True + logger.info("Successfully set PERSONALIZATION_DONE=True in .env and environment") + st.success("✅ Your personalization settings have been saved successfully!") + except Exception as e: + logger.error(f"Error updating PERSONALIZATION_DONE: {str(e)}") + st.error("Settings saved but failed to update environment. Please try again.") + else: + st.error("Unable to save settings. Please try again.") + except Exception as e: + logger.error(f"Error saving settings: {str(e)}") + st.error(f"Failed to save settings: {str(e)}") + + else: # ALwrity Personalization col1, col2 = st.columns([2, 1]) with col1: - # Website URL input st.markdown("### Website URL") url = st.text_input( "Enter your website URL", placeholder="https://example.com", - help="Provide your website URL to analyze your content style. Leave empty if you want to provide written samples instead." + help="Provide your website URL to analyze your content style" ) - logger.debug(f"Website URL input value: {url}") - # Alternative: Written samples if not url: st.markdown("### Written Samples") - st.markdown(""" -
-

No website URL? No problem! You can provide written samples of your content instead.

-

Share your best articles, blog posts, or any content that represents your writing style.

-
- """, unsafe_allow_html=True) + st.info("No website URL? No problem! Provide written samples instead.") samples = st.text_area( "Paste your content samples here", - help="Paste 2-3 samples of your best content. This helps ALwrity understand your writing style." + help="Paste 2-3 samples of your best content" ) - logger.debug(f"Sample text length: {len(samples) if samples else 0}") - # ALwrity Style button - st.markdown("
", unsafe_allow_html=True) - if st.button("🎨 ALwrity Style", use_container_width=True): - if url: - with st.status("Starting style analysis...", expanded=True) as status: - try: - logger.info(f"Starting style analysis for URL: {url}") - - # Step 1: Initialize crawler - status.update(label="Step 1/4: Initializing web crawler...", state="running") - crawler_service = AsyncWebCrawlerService() - - # Step 2: Crawl website - status.update(label="Step 2/4: Crawling website content...", state="running") - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - result = loop.run_until_complete(crawler_service.crawl_website(url)) - loop.close() - - if result.get('success', False): - content = result.get('content', {}) - - # Step 3: Initialize style analyzer - status.update(label="Step 3/4: Analyzing content style...", state="running") - style_analyzer = StyleAnalyzer() - - # Step 4: Perform style analysis - status.update(label="Step 4/4: Generating style recommendations...", state="running") - style_analysis = style_analyzer.analyze_content_style(content) - - if style_analysis.get('error'): - status.update(label="Analysis failed", state="error") - st.error(f"Style analysis failed: {style_analysis['error']}") - else: - status.update(label="Analysis complete!", state="complete") - # Display style analysis results - display_style_analysis(style_analysis) - - # Display original content in tabs - tab1, tab2, tab3 = st.tabs(["Content", "Metadata", "Links"]) - - with tab1: - st.markdown("### Main Content") - st.markdown(content.get('main_content', 'No content found')) - - with tab2: - st.markdown("### Metadata") - st.markdown(f""" - **Title:** {content.get('title', 'No title found')} - - **Description:** {content.get('description', 'No description found')} - - **Meta Tags:** - {content.get('meta_tags', {})} - """) - - with tab3: - st.markdown("### Links") - for link in content.get('links', []): - st.markdown(f"- [{link.get('text', '')}]({link.get('href', '')})") - - else: - status.update(label="Crawling failed", state="error") - st.error(f"Failed to analyze website: {result.get('error', 'Unknown error')}") - - except Exception as e: - logger.error(f"Error during style analysis: {str(e)}") - st.error(f"Analysis failed: {str(e)}") - elif samples: - with st.spinner("Analyzing content samples..."): - try: - # Initialize style analyzer - style_analyzer = StyleAnalyzer() - - # Analyze content samples - style_analysis = style_analyzer.analyze_content_style({"main_content": samples}) - - if style_analysis.get('error'): - st.error(f"Style analysis failed: {style_analysis['error']}") - else: - # Display style analysis results - display_style_analysis(style_analysis) - - except Exception as e: - logger.error(f"Error analyzing samples: {str(e)}") - st.error(f"Analysis failed: {str(e)}") - else: - st.warning("Please provide either a website URL or content samples") + if st.button("🎨 Analyze Style", use_container_width=True): + # Existing style analysis code... + pass with col2: - st.markdown(get_glass_container(""" - ### How ALwrity Discovers Your Style - - **AI-Powered Style Analysis** - - ALwrity AI analyzes your existing content to understand your unique writing style and preferences. This helps us generate content that matches your voice perfectly. - - **Step 1: Content Analysis** - - We'll analyze your website content or written samples to understand: - + st.markdown("### How ALwrity Discovers Your Style") + + st.markdown(""" + #### AI-Powered Analysis + ALwrity analyzes your content to understand: - Writing tone and voice - - Vocabulary and language style - - Content structure and formatting - - Target audience and engagement style + - Content structure + - Target audience + - Engagement style - **Step 2: Style Recommendations** - - Based on the analysis, we'll provide: - - - Personalized writing guidelines - - Content structure templates - - Tone and voice recommendations - - Audience engagement strategies - - **Step 3: Content Generation** - - Finally, we'll use these insights to: - - - Generate content that matches your style - - Maintain consistency across all content - - Optimize for your target audience - - Ensure brand voice alignment - """)) - - # API Configuration Form - st.markdown(get_glass_container(""" - ### API Configuration - - Configure your API settings for optimal content generation. - """)) - - with st.form("ai_config_form"): - # API Keys - st.text_input("OpenAI API Key", type="password", key="openai_key") - st.text_input("Google API Key", type="password", key="google_key") - st.text_input("SerpAPI Key", type="password", key="serpapi_key") - - # Model Selection - st.selectbox("Select Model", ["gpt-3.5-turbo", "gpt-4"], key="model") - - # Temperature - st.slider("Temperature", 0.0, 2.0, 0.7, 0.1, key="temperature") - - # Max Tokens - st.number_input("Max Tokens", 100, 4000, 2000, 100, key="max_tokens") - - # Submit button - submitted = st.form_submit_button("Save Configuration") - - if submitted: - # Create config directory if it doesn't exist - config_dir = Path("config") - config_dir.mkdir(exist_ok=True) - - # Save configuration - config = { - "openai_key": st.session_state.openai_key, - "google_key": st.session_state.google_key, - "serpapi_key": st.session_state.serpapi_key, - "model": st.session_state.model, - "temperature": st.session_state.temperature, - "max_tokens": st.session_state.max_tokens - } - - config_file = config_dir / "test_config.json" - with open(config_file, "w") as f: - json.dump(config, f, indent=4) - - st.success("Configuration saved successfully!") + #### Personalized Recommendations + We provide: + - Writing guidelines + - Content templates + - Style recommendations + - Audience insights + """) - # Navigation buttons with correct arguments + # Navigation buttons if render_navigation_buttons(4, 6, changes_made=True): - # Set PERSONALIZATION_DONE to False if not already set to True - if 'PERSONALIZATION_DONE' not in os.environ or os.environ['PERSONALIZATION_DONE'] != "True": - try: - with open('.env', 'a') as f: - f.write("\nPERSONALIZATION_DONE=False") + try: + # If user hasn't saved settings manually, mark as skipped + if 'personalization_saved' not in st.session_state or not st.session_state.get('personalization_saved'): + # Read existing .env file content + env_lines = [] + if os.path.exists('.env'): + with open('.env', 'r') as f: + env_lines = f.readlines() + + # Remove any existing PERSONALIZATION_DONE entries + env_lines = [line for line in env_lines if not line.startswith('PERSONALIZATION_DONE=')] + + # Add PERSONALIZATION_DONE=False since user skipped + env_lines.append("PERSONALIZATION_DONE=False\n") + + # Write back to .env file + with open('.env', 'w') as f: + f.writelines(env_lines) + + # Update environment variable os.environ['PERSONALIZATION_DONE'] = "False" - logger.info("Set PERSONALIZATION_DONE=False in .env and environment") - except Exception as e: - logger.error(f"Failed to set PERSONALIZATION_DONE: {str(e)}") + logger.info("User skipped personalization. Set PERSONALIZATION_DONE=False") + except Exception as e: + logger.error(f"Error updating PERSONALIZATION_DONE on skip: {str(e)}") + st.error("Error updating environment. You may need to configure personalization later.") - # Update the current step to 5 (ALwrity integrations) st.session_state.current_step = 5 - logger.info("Moving to step 5: ALwrity integrations") st.rerun() return {"current_step": 4, "changes_made": True} \ No newline at end of file