import streamlit as st
from transformers import pipeline
from crewai import Crew, Task

# Initialize the summarization pipeline
summarizer = pipeline("summarization")

# Define functions for tasks
def summarize_topic(topic):
    summary = summarizer(f"Summarize the topic: {topic}", max_length=100, min_length=30, do_sample=False)
    return summary[0]['summary_text']

def create_blog_post(summary):
    blog_post = f"""
    ### Blog Post: The Impact of AI in Modern Education

    {summary}

    In conclusion, AI is transforming education, making it more personalized and accessible.
    """
    return blog_post

# Function to run the Crew and get the blog post
def get_blog_post(topic):
    # Define Task functions
    def research_task_func():
        return {"summary": summarize_topic(topic)}

    def blog_task_func(summary):
        return {"blog_post": create_blog_post(summary)}

    # Create Task objects
    research_task = Task(
        name="Research Task",
        description="Summarizes the given topic",
        func=research_task_func
    )
    
    # Initialize Crew
    blog_creation_crew = Crew(
        tasks=[research_task],
        verbose=True
    )

    # Execute Crew
    results = blog_creation_crew.kickoff()
    
    # Retrieve summary
    summary = results["summary"]
    
    # Generate blog post
    blog_post = blog_task_func(summary)
    
    return blog_post["blog_post"]

# Streamlit UI
st.title("AI Blog Post Generator")

topic = st.text_input("Enter a topic for the blog post:", "AI in Education")

if st.button("Generate Blog Post"):
    if topic:
        blog_post = get_blog_post(topic)
        st.subheader("Generated Blog Post")
        st.write(blog_post)
    else:
        st.error("Please enter a topic.")