app.py

# import relevant libraries and modules

import warnings
from crewai import LLM, Task, Agent, Crew
import os
from dotenv import load_dotenv
from pathlib import Path
import gradio as gr
from tools.hybrid_retriever_tool import HybridRetrieverTool

# control warnings
warnings.filterwarnings("ignore")

load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

#Define LLMs
llm_planner = LLM(model="gpt-4o-mini", temperature=0.5)
llm_writer = LLM(model="gpt-5-mini", temperature=1.0)
llm_editor = LLM(model="gpt-4-turbo", temperature=0.3)
llm_fact=LLM(model="gpt-4o-mini", temperature=0.3)

#Define tools
hybrid_tool = HybridRetrieverTool(alpha=0.6) #including RAG in search

#Creating Agents

planner = Agent(
    role="Research Lead",
    goal="Plan engaging and factually accurate content on {topic}",
    backstory="You are working on planning a blog article about the topic {topic}."
    "Use the retriever tool to gather accurate, recent information before outlining." #RAG search
    "You collect relevant information that helps the audience learn something and make informed decisions. "
    "Your work is the basis for the Content Writer to write an article on this topic.",
    allow_delegation=False,
    verbose=True,
    tools = [hybrid_tool], #Rag search
    llm=llm_planner
)

writer = Agent(
    role="Technical Writer",
    goal="Write an insightful and factually accurate opinion piece about the topic: {topic}",
    backstory="You are working on writing a new opnion piece about the topic: {topic}. "
    "You base your writing on the work of the research lead, who provides an outline and relevant context about the topic. "
    "You follow the main objectives and direction of the outline, as provided by the Content Planner. "
    "You also provide objective and impartial insights, supported by relevant information that is provided by the Content Planner. "
    "You acknowledge in your opnion piece when your statements are opinions as opposed to objective staements.",
    allow_delegation=False,
    verbose=True,
    llm=llm_writer
)

fact_checker = Agent(
    role="Data Verifier",
    goal="Verify factual accuracy, detect unsupported claims and identify missing references or sources.",
    backstory="You are a meticulous research analyst who checks every claim against known facts and relaible sources"
    "Use the retriever tool to cross-check the Content Writer's statements against reliable, recent information.",
    allow_delegation=False,
    verbose=True,
    tools = [hybrid_tool], #Rag search
    llm=llm_fact
)

editor = Agent(
    role="Editor-in-Chief",
    goal="Edit a given blog post to align with the context and narrative style of the {topic} (e.g. academic, informal, satirical).",
    backstory="You are an editor who receives a blog post from the Content Writer. "
    "Your goal is to review the blog post to ensure that it follows journalistic best practices, "
    "provides balanced viewpoints, when providing opinions or assertions, "
    "and also avoids major controversial topics or opinions when possible. "
    "The blog post should align with the context and narrative style of the topic being discussed.",
    allow_delegation=False,
    verbose=True,
    llm=llm_editor
)

#Creating Tasks

plan = Task(
    name="content_planner_task",
    description=(
        "1. Prioritize the latest trends, key players, and groundbreaking news on {topic}.\n"
        "2. Identify the target audience, considering their interests and pain points.\n"
        "3. Develop a detailed content outline including and introduction, key points, and a call to action.\n"
        "4. Include SEO keywords and relevant data or sources"
    ),
    expected_output="A comprehensive content plan document with an outline, audience analysis, SEO keywords, and resources.",
    agent = planner
)

write = Task(
    name="blog_writer_task",
    description=(
        "1. Use the provided content plan to craft a compelling blog post on {topic}.\n"
        "2. Incorporate SEO keywords naturally.\n"
        "3. Sections/Subtitles are properly named in an engaging manner.\n"
        "4. Ensure the post structured with an engaging introduction, insightful body, and a summarizing conclusion.\n"
        "5. The narrative style and language used should be determined by the target audience and {tone}.\n"
        "6. Proofread for grammatical errors and alignment with the target audience.\n"
    ),
    expected_output="A well-written blog post in markdown format, ready for publication. "
    "Each section should have 2 or 3 paragraphs and the whole blog should be a maximum of 1000 words.",
    agent=writer, 
    context=[plan]
)

fact_check = Task(
    name="fact_checker_task",
    description=(
        "Carefully review the drafted blog post provided by the Content Writer. "
        "Check all factual statements for accuracy and ensure sources or references are mentioned. "
        "Identify any unsupported claims, logical inconsistencies, or outdated information. "
        "Provide corrections or highlight areas that require further verification."
    ),
    expected_output=(
        "A revised version of the blog post in markdown format, with annotations or corrections "
        "for any inaccurate or unsupported statements."
    ),
    agent=fact_checker,
    context=[write]
)

edit = Task(
    name="editor_task",
    description=("Edit and refine the fact-checked blog post to ensure clarity, flow and consistent {tone}.\n"
    "Polish grammar, readability, and overall style."
    ),
    expected_output="A well-written blog post in markdown format, ready for publication. "
    "Each section should have 2 or 3 paragraphs and the whole blog should be a maximum of 1000 words",
    agent=editor,
    context=[fact_check]
)

# Creating the Crew

crew = Crew(
    agents=[planner, writer, fact_checker, editor],
    tasks=[plan, write, fact_check, edit],
    process="sequential",
    verbose=True
)

# fetch context for RAG search
def fetch_context(topic):
    passages = hybrid_tool._run(topic)
    if isinstance(passages, str):
        summary = passages
    else:
        summary = hybrid_tool.summarize_passages(topic, passages)
    return summary

# Define Gradio Handler
def generate_blog(topic, tone, progress=gr.Progress(track_tqdm=True)):
    progress(0, desc="Starting collaborative report generation...")
    yield "### 🧭 Starting Collaborative Report\n"

    # Run the entire multi-agent workflow once (non-streaming)
    result = crew.kickoff(inputs={"topic": topic, "tone": tone})

    # Try to extract clean text output
    if hasattr(result, "output"):
        final_output = result.output
    elif hasattr(result, "final_output"):
        final_output = result.final_output
    else:
        final_output = str(result)

    progress(1.0, desc="Completed.")
    yield f"✅ **Collaborative Report Generated Successfully!**\n\n{final_output}"

# Build Gradio Interface
css = """
#output-box {
    background-color: #f8f9fa;
    border-radius: 12px;
    padding: 1.5rem;
    font-family: 'Inter', sans-serif;
    font-size: 1rem;
    line-height: 1.6;
    white-space: pre-wrap;
    overflow-y: auto;
    max-height: 70vh;
}

#context-box h1, #context-box h2, #context-box h3 {
    font-size: 1rem !important;
    font-weight: 600 !important;
}
#context-box {
    font-family: 'Inter', sans-serif;
    font-size: 1rem;
    line-height: 1.6;
    background-color: #ffffff;
    border: 1px solid #ddd;
    border-radius: 10px;
    padding: 1rem;
    margin-top: 0.5rem;
    max-height: 70vh;
    overflow-y: auto;
}
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown(
        """
        ## ✍️ AI Blog Writer Multi-Agent (Multi-Agent Crew: Sequential research → writing → verification → editing)
        Create a professional, fact-checked, and polished article using CrewAI agents.
        """
    )

    with gr.Row():
        topic = gr.Textbox(
            label="Blog Topic",
            placeholder="e.g. Sustainable Investing, Vegan cuisine, AI in Healthcare",
            value="Sustainable Investing"
        )
        tone=gr.Dropdown(
            ["professional", "playful", "academic", "casual", "satirical", "humorous"],
            label="Select Writing Tone",
            value="academic"
        )
    fetch_btn = gr.Button("🌐 Fetch & Summarize Context", variant="secondary") # Rag Search
    context_output = gr.Markdown(label="📚 Retrieved Context Summary") # Rag Search

    run_button = gr.Button("🚀 Generate Blog", variant="primary")
    final_output = gr.Textbox(label="📰 Generated Blog Post", elem_id="output-box", lines=30, interactive=False, show_label=True)

    fetch_btn.click(fetch_context, inputs=[topic], outputs=[context_output]) # Rag Search
    run_button.click(generate_blog, inputs=[topic, tone], outputs=[final_output])

#Launch app
if __name__=="__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)