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AI Image and Audio Generation Improvements.

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AI Video Generation Pre-Flight Checklist. Cost Estimate Improvements.
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ToBeMigrated/ai_writers/ai_blog_faqs_writer/README.md
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# AI-Powered FAQ Generator
A sophisticated FAQ generation system that creates comprehensive, well-researched FAQs from various content sources. This tool leverages AI to analyze content, conduct web research, and generate detailed FAQs with customizable options.
## Features
### Content Processing
- **Multiple Input Sources**
- Direct text input
- File uploads (DOCX, TXT)
- URL content extraction
- Support for any content type (general, technical, educational, etc.)
### Research Capabilities
- **Multi-level Search Depth**
- **Basic**: Google Search for quick, general information
- **Comprehensive**: Tavily AI for detailed, in-depth research
- **Expert**: Metaphor AI for specialized, expert-level content
### Customization Options
- **Target Audience**
- Beginner
- Intermediate
- Expert
- **FAQ Style**
- Technical
- Conversational
- Professional
- **Advanced Features**
- Emoji inclusion
- Code example generation
- Reference integration
- Customizable time range for research
- Multi-language support
### Output Formats
- Interactive preview
- Markdown
- HTML
- JSON
## Installation
1. Clone the repository
2. Install dependencies:
```bash
pip install -r requirements.txt
```
## Usage
### Basic Usage
```python
from lib.ai_writers.ai_blog_faqs_writer.faqs_generator_blog import FAQGenerator, FAQConfig
# Initialize with default configuration
generator = FAQGenerator()
# Generate FAQs from content
faqs = await generator.generate_faqs("Your content here")
```
### Advanced Configuration
```python
from lib.ai_writers.ai_blog_faqs_writer.faqs_generator_blog import (
FAQGenerator, FAQConfig, TargetAudience, FAQStyle, SearchDepth
)
# Custom configuration
config = FAQConfig(
num_faqs=10,
target_audience=TargetAudience.INTERMEDIATE,
faq_style=FAQStyle.TECHNICAL,
include_emojis=True,
include_code_examples=True,
include_references=True,
search_depth=SearchDepth.COMPREHENSIVE,
time_range="last_6_months",
language="English"
)
generator = FAQGenerator(config)
```
### Web Interface
Run the Streamlit interface:
```bash
streamlit run lib/ai_writers/ai_blog_faqs_writer/faqs_ui.py
```
## Research Process
1. **Content Analysis**
- Identifies key topics and concepts
- Extracts potential questions
- Determines research requirements
2. **Web Research**
- Selects appropriate search function based on depth
- Gathers relevant information
- Validates and cross-references data
3. **FAQ Generation**
- Creates comprehensive questions
- Provides detailed answers
- Includes code examples (if applicable)
- Adds references and citations
## Output Structure
Each FAQ item includes:
- Question
- Detailed answer
- Category
- Code example (if applicable)
- References
- Confidence score
- Last updated timestamp
## Configuration Options
### FAQConfig Parameters
- `num_faqs`: Number of FAQs to generate (default: 5)
- `target_audience`: Target audience level (default: INTERMEDIATE)
- `faq_style`: Writing style (default: PROFESSIONAL)
- `include_emojis`: Whether to include emojis (default: True)
- `include_code_examples`: Whether to include code examples (default: True)
- `include_references`: Whether to include references (default: True)
- `search_depth`: Research depth level (default: COMPREHENSIVE)
- `time_range`: Time range for research (default: "last_6_months")
- `language`: Output language (default: "English")
## Research Depth Options
### Basic (Google Search)
- Quick, general information
- Broad coverage
- Suitable for basic topics
### Comprehensive (Tavily AI)
- Detailed, in-depth research
- Multiple source integration
- Best for most use cases
### Expert (Metaphor AI)
- Specialized, expert-level content
- Advanced topic coverage
- Technical and academic focus
## Best Practices
1. **Content Preparation**
- Provide clear, well-structured content
- Include key terms and concepts
- Specify target audience and style
2. **Research Selection**
- Use Basic for general topics
- Choose Comprehensive for detailed analysis
- Select Expert for technical subjects
3. **Output Review**
- Verify accuracy of information
- Check code examples
- Validate references
## Contributing
1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Support
For support, please open an issue in the repository or contact the maintainers.
## Acknowledgments
- OpenAI for GPT integration
- Google Search API
- Tavily AI
- Metaphor AI
- BeautifulSoup for web scraping
- Streamlit for UI

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ToBeMigrated/ai_writers/ai_blog_faqs_writer/faqs_generator_blog.py
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"""
Enhanced FAQ Generator
This module provides a comprehensive FAQ generation system that can create detailed,
well-researched FAQs from various content sources with customizable options.
"""
import sys
import json
import re
from typing import Dict, List, Optional, Union
from pathlib import Path
from enum import Enum
from dataclasses import dataclass
from loguru import logger
from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
from lib.ai_web_researcher.google_serp_search import google_search
from lib.ai_web_researcher.tavily_ai_search import do_tavily_ai_search
from lib.ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles
logger.remove()
logger.add(sys.stdout,
colorize=True,
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}")
class TargetAudience(Enum):
BEGINNER = "beginner"
INTERMEDIATE = "intermediate"
EXPERT = "expert"
class FAQStyle(Enum):
TECHNICAL = "technical"
CONVERSATIONAL = "conversational"
PROFESSIONAL = "professional"
class SearchDepth(Enum):
BASIC = "basic"
COMPREHENSIVE = "comprehensive"
EXPERT = "expert"
@dataclass
class FAQConfig:
"""Configuration for FAQ generation."""
num_faqs: int = 5
target_audience: TargetAudience = TargetAudience.INTERMEDIATE
faq_style: FAQStyle = FAQStyle.PROFESSIONAL
include_emojis: bool = True
include_code_examples: bool = True
include_references: bool = True
search_depth: SearchDepth = SearchDepth.COMPREHENSIVE
time_range: str = "last_6_months"
exclude_domains: List[str] = None
language: str = "English"
selected_search_queries: List[str] = None
@dataclass
class FAQItem:
"""Individual FAQ item with metadata."""
question: str
answer: str
category: str
code_example: Optional[str] = None
references: List[Dict[str, str]] = None
confidence_score: float = 0.0
last_updated: str = None
class FAQGenerator:
"""Enhanced FAQ Generator with research capabilities."""
def __init__(self, config: Optional[FAQConfig] = None):
"""Initialize the FAQ generator with optional configuration."""
self.config = config or FAQConfig()
self.faqs: List[FAQItem] = []
self.research_results = {}
self.search_queries = []
def generate_search_queries(self, content: str) -> List[str]:
"""Generate search queries based on the content."""
try:
prompt = f"""Based on the following content, generate 5 specific search queries that would help create comprehensive FAQs.
Content: {content}
Guidelines for search queries:
1. Focus on key concepts and terms
2. Include common questions users might have
3. Cover technical aspects that need clarification
4. Include best practices and recommendations
5. Make queries specific and focused
Please provide exactly 5 search queries, one per line.
Do not include numbers or bullet points in the queries.
"""
response = llm_text_gen(prompt)
# Clean up the queries by removing numbers and extra spaces
queries = []
for line in response.split('\n'):
# Remove any leading numbers, dots, or spaces
cleaned = re.sub(r'^\d+\.\s*', '', line.strip())
if cleaned:
queries.append(cleaned)
self.search_queries = queries[:5] # Ensure we only get 5 queries
return self.search_queries
except Exception as err:
logger.error(f"Failed to generate search queries: {err}")
return []
def _clean_search_query(self, query: str) -> str:
"""Clean up a search query by removing numbers and extra formatting."""
# Remove any leading numbers, dots, or spaces
cleaned = re.sub(r'^\d+\.\s*', '', query.strip())
# Remove any quotes
cleaned = cleaned.replace('"', '').replace("'", '')
# Remove any extra spaces
cleaned = ' '.join(cleaned.split())
return cleaned
def generate_faqs(self, content: str, content_type: str = "general") -> List[FAQItem]:
"""Generate FAQs from the given content with research integration."""
try:
if not self.config.selected_search_queries:
raise ValueError("No search queries selected. Please select queries to proceed.")
# Clean up selected queries
cleaned_queries = [self._clean_search_query(q) for q in self.config.selected_search_queries]
self.config.selected_search_queries = cleaned_queries
# Step 1: Research the topic using selected queries
research_results = self._conduct_research(content)
# Step 2: Generate initial FAQs
initial_faqs = self._generate_initial_faqs(content, research_results)
# Step 3: Enhance FAQs with research
enhanced_faqs = self._enhance_faqs_with_research(initial_faqs, research_results)
# Step 4: Add code examples if requested
if self.config.include_code_examples:
enhanced_faqs = self._add_code_examples(enhanced_faqs)
# Step 5: Add references if requested
if self.config.include_references:
enhanced_faqs = self._add_references(enhanced_faqs, research_results)
self.faqs = enhanced_faqs
return enhanced_faqs
except Exception as err:
logger.error(f"Failed to generate FAQs: {err}")
raise
def _conduct_research(self, content: str) -> Dict:
"""Conduct online research based on the selected search queries."""
try:
research_results = {}
for query in self.config.selected_search_queries:
try:
# Clean the query before searching
cleaned_query = self._clean_search_query(query)
logger.info(f"Researching query: {cleaned_query}")
# Select search function based on search depth
if self.config.search_depth == SearchDepth.BASIC:
results = google_search(cleaned_query)
elif self.config.search_depth == SearchDepth.COMPREHENSIVE:
results = do_tavily_ai_search(cleaned_query)
elif self.config.search_depth == SearchDepth.EXPERT:
results = metaphor_search_articles(cleaned_query)
else:
logger.warning(f"Unknown search depth: {self.config.search_depth}, defaulting to Google search")
results = google_search(cleaned_query)
research_results[query] = results
logger.info(f"Research completed for query: {query}")
except Exception as err:
logger.error(f"Failed to research query '{query}': {err}")
continue
return research_results
except Exception as err:
logger.error(f"Failed to conduct research: {err}")
return {}
def _generate_initial_faqs(self, content: str, research_results: Dict) -> List[FAQItem]:
"""Generate initial FAQs using LLM."""
try:
system_prompt = f"""You are an expert FAQ generator with deep knowledge in content creation and technical writing.
Your task is to create comprehensive FAQs based on the given content and research.
Guidelines:
1. Target Audience: {self.config.target_audience.value}
2. Style: {self.config.faq_style.value}
3. Include emojis: {self.config.include_emojis}
4. Language: {self.config.language}
5. Number of FAQs: {self.config.num_faqs}
Create FAQs that are:
- Clear and concise
- Well-structured
- Technically accurate
- Engaging and informative
- Based on the provided research
- Relevant to the target audience
- Written in the specified style
Format each FAQ exactly as follows:
Q: [Your question here]
A: [Your detailed answer here]
Category: [Category name]
Confidence: [Score between 0 and 1]
---
"""
prompt = f"""Content to generate FAQs from:
{content}
Research Results:
{json.dumps(research_results, indent=2)}
Please generate {self.config.num_faqs} FAQs following the guidelines above.
Each FAQ must be separated by '---' and include all required fields.
"""
response = llm_text_gen(prompt, system_prompt=system_prompt)
logger.info(f"LLM Response: {response}")
# Parse the response into FAQItem objects
faqs = []
current_faq = None
for line in response.split('\n'):
line = line.strip()
if not line or line == '---':
if current_faq and current_faq.question and current_faq.answer:
faqs.append(current_faq)
current_faq = None
continue
if line.startswith('Q:'):
if current_faq and current_faq.question and current_faq.answer:
faqs.append(current_faq)
current_faq = FAQItem(question=line[2:].strip(), answer="", category="")
elif line.startswith('A:'):
if current_faq:
current_faq.answer = line[2:].strip()
elif line.startswith('Category:'):
if current_faq:
current_faq.category = line[9:].strip()
elif line.startswith('Confidence:'):
if current_faq:
try:
current_faq.confidence_score = float(line[11:].strip())
except ValueError:
current_faq.confidence_score = 0.5
# Add the last FAQ if it exists and is complete
if current_faq and current_faq.question and current_faq.answer:
faqs.append(current_faq)
logger.info(f"Generated {len(faqs)} FAQs")
return faqs
except Exception as err:
logger.error(f"Failed to generate initial FAQs: {err}")
raise
def _enhance_faqs_with_research(self, faqs: List[FAQItem], research_results: Dict) -> List[FAQItem]:
"""Enhance FAQs with research findings."""
try:
enhanced_faqs = []
for faq in faqs:
# Find relevant research for this FAQ
relevant_research = self._find_relevant_research(faq, research_results)
if relevant_research:
# Enhance the answer with research findings
enhancement_prompt = f"""Enhance the following FAQ answer with the provided research:
Question: {faq.question}
Current Answer: {faq.answer}
Research:
{json.dumps(relevant_research, indent=2)}
Please enhance the answer while:
1. Maintaining the original style and tone
2. Adding relevant information from the research
3. Ensuring technical accuracy
4. Keeping the answer concise and clear
"""
enhanced_answer = llm_text_gen(enhancement_prompt)
faq.answer = enhanced_answer
enhanced_faqs.append(faq)
return enhanced_faqs
except Exception as err:
logger.error(f"Failed to enhance FAQs with research: {err}")
return faqs
def _add_code_examples(self, faqs: List[FAQItem]) -> List[FAQItem]:
"""Add code examples to FAQs where applicable."""
try:
for faq in faqs:
if self._is_technical_question(faq.question):
code_prompt = f"""Generate a code example for the following FAQ:
Question: {faq.question}
Answer: {faq.answer}
Please provide a relevant code example that demonstrates the concept.
Include comments and explanations where necessary.
"""
code_example = llm_text_gen(code_prompt)
faq.code_example = code_example
return faqs
except Exception as err:
logger.error(f"Failed to add code examples: {err}")
return faqs
def _add_references(self, faqs: List[FAQItem], research_results: Dict) -> List[FAQItem]:
"""Add references to FAQs based on research results."""
try:
for faq in faqs:
relevant_research = self._find_relevant_research(faq, research_results)
if relevant_research:
references = []
for source, content in relevant_research.items():
references.append({
"source": source,
"content": content
})
faq.references = references
return faqs
except Exception as err:
logger.error(f"Failed to add references: {err}")
return faqs
def _find_relevant_research(self, faq: FAQItem, research_results: Dict) -> Dict:
"""Find research results relevant to a specific FAQ."""
relevant_research = {}
for topic, results in research_results.items():
if any(keyword in faq.question.lower() for keyword in topic.lower().split()):
relevant_research[topic] = results
return relevant_research
def _is_technical_question(self, question: str) -> bool:
"""Determine if a question is technical and might benefit from a code example."""
technical_keywords = ["code", "program", "function", "method", "class", "api", "syntax", "error", "debug"]
return any(keyword in question.lower() for keyword in technical_keywords)
def to_markdown(self) -> str:
"""Convert FAQs to markdown format."""
markdown = "# Frequently Asked Questions\n\n"
for faq in self.faqs:
markdown += f"## {faq.question}\n\n"
markdown += f"{faq.answer}\n\n"
if faq.code_example:
markdown += "```\n"
markdown += f"{faq.code_example}\n"
markdown += "```\n\n"
if faq.references:
markdown += "### References\n"
for ref in faq.references:
markdown += f"- {ref['source']}\n"
markdown += "\n"
return markdown
def to_html(self) -> str:
"""Convert FAQs to HTML format."""
html = """
<!DOCTYPE html>
<html>
<head>
<title>Frequently Asked Questions</title>
<style>
body { font-family: Arial, sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; }
.faq { margin-bottom: 30px; }
.question { font-weight: bold; font-size: 1.2em; color: #2c3e50; }
.answer { margin: 10px 0; }
.code-example { background: #f8f9fa; padding: 15px; border-radius: 4px; }
.references { margin-top: 15px; font-size: 0.9em; }
</style>
</head>
<body>
<h1>Frequently Asked Questions</h1>
"""
for faq in self.faqs:
html += f"""
<div class="faq">
<div class="question">{faq.question}</div>
<div class="answer">{faq.answer}</div>
"""
if faq.code_example:
html += f"""
<div class="code-example">
<pre><code>{faq.code_example}</code></pre>
</div>
"""
if faq.references:
html += """
<div class="references">
<h3>References</h3>
<ul>
"""
for ref in faq.references:
html += f"""
<li>{ref['source']}</li>
"""
html += """
</ul>
</div>
"""
html += """
</div>
"""
html += """
</body>
</html>
"""
return html

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ToBeMigrated/ai_writers/ai_blog_faqs_writer/faqs_ui.py
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"""
Streamlit UI for FAQ Generator
This module provides a user-friendly interface for generating FAQs from various content sources.
"""
import streamlit as st
from pathlib import Path
from typing import Optional
import json
import requests
from bs4 import BeautifulSoup
import logging
import pyperclip
from .faqs_generator_blog import FAQGenerator, FAQConfig, TargetAudience, FAQStyle, SearchDepth
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def copy_to_clipboard(text: str) -> None:
"""Copy text to clipboard and show success message."""
try:
pyperclip.copy(text)
st.success("Copied to clipboard!")
except Exception as e:
st.error(f"Failed to copy to clipboard: {str(e)}")
def fetch_url_content(url):
"""Fetch and extract content from a URL."""
try:
response = requests.get(url)
response.raise_for_status()
soup = BeautifulSoup(response.text, 'html.parser')
# Remove script and style elements
for script in soup(["script", "style"]):
script.decompose()
# Get text
text = soup.get_text()
# Break into lines and remove leading and trailing space
lines = (line.strip() for line in text.splitlines())
# Break multi-headlines into a line each
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
# Drop blank lines
text = '\n'.join(chunk for chunk in chunks if chunk)
return text
except Exception as e:
st.error(f"Error fetching URL content: {str(e)}")
return None
def main():
st.title("FAQ Generator")
st.markdown("Generate comprehensive FAQs from your content with research integration.")
# Initialize session state variables if they don't exist
if 'search_queries' not in st.session_state:
st.session_state.search_queries = []
if 'selected_queries' not in st.session_state:
st.session_state.selected_queries = []
if 'research_completed' not in st.session_state:
st.session_state.research_completed = False
if 'research_results' not in st.session_state:
st.session_state.research_results = {}
if 'faq_config' not in st.session_state:
st.session_state.faq_config = None
if 'generator' not in st.session_state:
st.session_state.generator = FAQGenerator()
if 'generated_faqs' not in st.session_state:
st.session_state.generated_faqs = None
if 'output_format' not in st.session_state:
st.session_state.output_format = "Preview"
# Sidebar for configuration
with st.sidebar:
st.header("Configuration")
# Basic settings
num_faqs = st.slider("Number of FAQs", 1, 20, 5)
target_audience = st.selectbox(
"Target Audience",
[audience.value for audience in TargetAudience]
)
faq_style = st.selectbox(
"FAQ Style",
[style.value for style in FAQStyle]
)
# Advanced settings
with st.expander("Advanced Settings"):
include_emojis = st.checkbox("Include Emojis", value=True)
include_code_examples = st.checkbox("Include Code Examples", value=True)
include_references = st.checkbox("Include References", value=True)
search_depth = st.selectbox(
"Search Depth",
[depth.value for depth in SearchDepth]
)
time_range = st.selectbox(
"Time Range",
["last_month", "last_6_months", "last_year", "all_time"]
)
language = st.text_input("Language", value="English")
# Main content area
content_type = st.radio(
"Content Source",
["Direct Input", "File Upload", "URL"]
)
content = ""
if content_type == "Direct Input":
content = st.text_area("Enter your content", height=300)
elif content_type == "URL":
url = st.text_input("Enter URL")
if url:
content = fetch_url_content(url)
if content:
st.text_area("Extracted Content", content, height=300)
# Step 1: Generate search queries
if content and not st.session_state.search_queries:
if st.button("Generate Search Queries"):
with st.spinner("Generating search queries..."):
search_queries = st.session_state.generator.generate_search_queries(content)
if search_queries:
st.session_state.search_queries = search_queries
st.session_state.selected_queries = [] # Reset selected queries
st.session_state.research_completed = False # Reset research status
st.session_state.research_results = {} # Reset research results
st.session_state.faq_config = None # Reset config
st.session_state.generated_faqs = None # Reset generated FAQs
st.success("Search queries generated successfully!")
# Step 2: Display and select search queries
if st.session_state.search_queries:
st.subheader("Select Search Queries")
st.info("Select the queries you want to use for web research. You can select multiple queries.")
# Create checkboxes for each search query
selected_queries = []
for query in st.session_state.search_queries:
if st.checkbox(query, key=f"query_{query}", value=query in st.session_state.selected_queries):
selected_queries.append(query)
# Update selected queries in session state
st.session_state.selected_queries = selected_queries
# Step 3: Do web research
if st.session_state.selected_queries and not st.session_state.research_completed:
if st.button("Do Web Research"):
try:
# Create config with selected queries
config = FAQConfig(
num_faqs=num_faqs,
target_audience=TargetAudience(target_audience),
faq_style=FAQStyle(faq_style),
include_emojis=include_emojis,
include_code_examples=include_code_examples,
include_references=include_references,
search_depth=SearchDepth(search_depth),
time_range=time_range,
language=language,
selected_search_queries=selected_queries
)
# Store config in session state
st.session_state.faq_config = config
# Update generator with config
st.session_state.generator.config = config
# Do research
with st.spinner("Conducting web research..."):
research_results = st.session_state.generator._conduct_research(content)
st.session_state.research_completed = True
st.session_state.research_results = research_results
st.success("Web research completed successfully!")
# Display research results
st.subheader("Research Results")
for query, results in research_results.items():
with st.expander(f"Results for: {query}"):
if isinstance(results, dict):
st.json(results)
else:
st.text(results)
except Exception as e:
st.error(f"Error during web research: {str(e)}")
st.error("Please try again with different search queries or adjust the search depth.")
# Step 4: Generate FAQs
if st.session_state.research_completed and st.session_state.research_results and st.session_state.faq_config:
if st.button("Generate FAQs"):
try:
# Update generator with stored config
st.session_state.generator.config = st.session_state.faq_config
# Generate FAQs
with st.spinner("Generating FAQs..."):
logger.info("Starting FAQ generation...")
faqs = st.session_state.generator.generate_faqs(content)
logger.info(f"Generated {len(faqs) if faqs else 0} FAQs")
if not faqs:
st.error("No FAQs were generated. Please try again.")
return
st.session_state.generated_faqs = faqs
st.success("FAQs generated successfully!")
except Exception as e:
logger.error(f"Error generating FAQs: {str(e)}")
st.error(f"Error generating FAQs: {str(e)}")
st.error("Please try again or adjust your settings.")
# Display generated FAQs if they exist
if st.session_state.generated_faqs:
st.subheader("Generated FAQs")
# Output format selection
output_format = st.radio(
"Output Format",
["Preview", "Markdown", "HTML", "JSON"],
key="output_format"
)
# Create columns for copy and download buttons
col1, col2 = st.columns(2)
if output_format == "Preview":
# Create a formatted text for copying
preview_text = ""
for i, faq in enumerate(st.session_state.generated_faqs, 1):
preview_text += f"{i}. {faq.question}\n"
preview_text += f"{faq.answer}\n\n"
if faq.code_example:
preview_text += f"Code Example:\n{faq.code_example}\n\n"
if faq.references:
preview_text += "References:\n"
for ref in faq.references:
preview_text += f"- {ref['source']}\n"
preview_text += "\n"
with col1:
if st.button("Copy to Clipboard", key="copy_preview"):
copy_to_clipboard(preview_text)
# Display the FAQs
for i, faq in enumerate(st.session_state.generated_faqs, 1):
with st.expander(f"{i}. {faq.question}"):
st.markdown(faq.answer)
if faq.code_example:
st.code(faq.code_example)
if faq.references:
st.markdown("**References:**")
for ref in faq.references:
st.markdown(f"- {ref['source']}")
elif output_format == "Markdown":
markdown_output = st.session_state.generator.to_markdown()
st.code(markdown_output, language="markdown")
with col1:
if st.button("Copy to Clipboard", key="copy_markdown"):
copy_to_clipboard(markdown_output)
with col2:
st.download_button(
"Download Markdown",
markdown_output,
file_name="faqs.md",
mime="text/markdown"
)
elif output_format == "HTML":
html_output = st.session_state.generator.to_html()
st.code(html_output, language="html")
with col1:
if st.button("Copy to Clipboard", key="copy_html"):
copy_to_clipboard(html_output)
with col2:
st.download_button(
"Download HTML",
html_output,
file_name="faqs.html",
mime="text/html"
)
elif output_format == "JSON":
json_output = json.dumps([faq.__dict__ for faq in st.session_state.generated_faqs], indent=2)
st.code(json_output, language="json")
with col1:
if st.button("Copy to Clipboard", key="copy_json"):
copy_to_clipboard(json_output)
with col2:
st.download_button(
"Download JSON",
json_output,
file_name="faqs.json",
mime="application/json"
)
if __name__ == "__main__":
main()