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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.") |