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| import streamlit as st | |
| from crewai import Agent, Task, Crew, Process | |
| from crewai_tools import WebsiteSearchTool | |
| from dotenv import load_dotenv | |
| import os | |
| from fpdf import FPDF | |
| from io import BytesIO | |
| import textwrap | |
| # Load environment variables | |
| load_dotenv() | |
| # Streamlit App | |
| st.title("Blog Generator with AI") | |
| st.sidebar.header("Input") | |
| # OpenAI API key input | |
| user_api_key = st.sidebar.text_input("Enter your OpenAI API Key", type="password") | |
| news_link = st.sidebar.text_input("Enter News Link", "") | |
| generate_blog = st.sidebar.button("Generate Blog") | |
| if generate_blog: | |
| if user_api_key: | |
| # Set the OpenAI API key dynamically | |
| os.environ["OPENAI_API_KEY"] = user_api_key | |
| if news_link: | |
| st.info("Fetching and processing the news...") | |
| # Define tools | |
| web_tool = WebsiteSearchTool() | |
| # Create agents | |
| blog_researcher = Agent( | |
| role='AI Blog Researcher from News Website', | |
| goal='Get the relevant latest AI related news from News Website', | |
| verbose=True, | |
| memory=True, | |
| backstory=("Expert in understanding videos in AI, Data Science, Machine Learning, and GEN AI."), | |
| tools=[web_tool], | |
| allow_delegation=True | |
| ) | |
| blog_writer = Agent( | |
| role='Blog Writer', | |
| goal='Narrate compelling tech stories about the News Article and add the reference links at the end of the blog.', | |
| verbose=True, | |
| memory=True, | |
| backstory=( | |
| "With a flair for simplifying complex topics, you craft engaging narratives that captivate and educate," | |
| "bringing new discoveries to light in an accessible manner." | |
| ), | |
| tools=[web_tool], | |
| allow_delegation=False | |
| ) | |
| # Define tasks | |
| research_task = Task( | |
| description=( | |
| "Research Task: " | |
| "1. Visit the provided news link and analyze the content thoroughly\n" | |
| "2. Extract key information about the AI-related news or development\n" | |
| "3. Identify main points, technological aspects, and potential impact\n" | |
| "4. Gather any relevant background information or context\n" | |
| "5. Note any quotes, statistics, or specific technical details" | |
| ), | |
| expected_output=( | |
| "A detailed research report containing:\n" | |
| "- Main story/development summary\n" | |
| "- Technical aspects and innovations\n" | |
| "- Impact and implications\n" | |
| "- Supporting facts and context\n" | |
| "- Notable quotes or statistics" | |
| ), | |
| agent=blog_researcher | |
| ) | |
| write_task = Task( | |
| description=( | |
| "Writing Task:\n" | |
| "1. Use the research findings to craft an engaging blog post\n" | |
| "2. Structure the content with clear introduction, body, and conclusion\n" | |
| "3. Explain technical concepts in an accessible manner\n" | |
| "4. Include relevant examples and real-world applications\n" | |
| "5. Add reference links at the end" | |
| ), | |
| expected_output=( | |
| "A well-structured blog post that includes:\n" | |
| "- Engaging headline and introduction\n" | |
| "- Clear explanation of the AI development\n" | |
| "- Technical details explained simply\n" | |
| "- Impact and future implications\n" | |
| "- Reference links" | |
| ), | |
| agent=blog_writer | |
| ) | |
| # Create crew - Fixed verbose parameter to be boolean | |
| crew = Crew( | |
| agents=[blog_researcher, blog_writer], | |
| tasks=[research_task, write_task], | |
| process=Process.sequential, | |
| verbose=True # Changed from 2 to True | |
| ) | |
| try: | |
| # Kickoff the process with the news link as input | |
| result = crew.kickoff(inputs={'topic': news_link}) | |
| # Extract blog content from the result | |
| if hasattr(result, 'tasks_output'): | |
| blog_content = result.tasks_output[-1].raw # Get the last task's output | |
| else: | |
| # If tasks_output is not available, try to get the raw result | |
| blog_content = str(result) | |
| # Display the blog content | |
| st.subheader("Generated Blog") | |
| st.text_area("Blog Content", value=blog_content, height=400) | |
| # Actions to save or reject the blog | |
| st.subheader("Actions") | |
| save_blog = st.button("Save Blog as PDF") | |
| reject_blog = st.button("Reject Blog") | |
| if save_blog: | |
| try: | |
| # Create PDF with proper text wrapping and formatting | |
| pdf = FPDF() | |
| pdf.add_page() | |
| pdf.set_auto_page_break(auto=True, margin=15) | |
| # Add title | |
| pdf.set_font("Arial", "B", 16) | |
| pdf.cell(0, 10, "Generated Blog", ln=True, align='C') | |
| pdf.ln(10) | |
| # Add content with proper formatting | |
| pdf.set_font("Arial", size=12) | |
| # Process and wrap text | |
| effective_page_width = pdf.w - 2 * pdf.l_margin | |
| # Split content into paragraphs | |
| paragraphs = blog_content.split('\n') | |
| for paragraph in paragraphs: | |
| if paragraph.strip(): # Only process non-empty paragraphs | |
| # Wrap text to fit page width | |
| wrapped_text = textwrap.fill(paragraph, width=95) | |
| # Split wrapped text into lines and write them | |
| lines = wrapped_text.split('\n') | |
| for line in lines: | |
| pdf.multi_cell(0, 10, line) | |
| pdf.ln(5) # Add small space between paragraphs | |
| # Generate PDF bytes | |
| pdf_buffer = BytesIO() | |
| pdf.output(pdf_buffer) | |
| pdf_buffer.seek(0) | |
| # Create download button | |
| st.download_button( | |
| label="Download Blog as PDF", | |
| data=pdf_buffer, | |
| file_name="generated_blog.pdf", | |
| mime="application/pdf" | |
| ) | |
| st.success("Blog saved as PDF and ready for download!") | |
| except Exception as e: | |
| st.error(f"Failed to save blog as PDF: {str(e)}") | |
| st.error("PDF Generation Error Details:", exc_info=True) | |
| if reject_blog: | |
| st.warning("Blog rejected. No file was saved.") | |
| except Exception as e: | |
| st.error(f"An error occurred during the process: {str(e)}") | |
| else: | |
| st.error("Please enter a valid news link.") | |
| else: | |
| st.error("Please provide your OpenAI API Key.") |