Upload article4.py

article4.py

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+import os
+import streamlit as st
+from crewai import Agent, Task, Crew, LLM
+
+# Set your Gemini AI API key and model
+gemini_api_key = 'AIzaSyAC_i-I9uCP2UP14H89uigWP7MDM2xQno8'  # Replace with your actual Gemini API key
+os.environ["GEMINI_API_KEY"] = gemini_api_key
+
+# Initialize the LLM instance
+my_llm = LLM(
+    api_key=gemini_api_key,  # Replace with your actual API key
+    model="gemini/gemini-pro"
+)
+
+# Define your agents with roles, goals, and backstory
+planner = Agent(
+    role="Content Planner",
+    goal="Plan engaging and factually accurate content on {topic}",
+    backstory="You're working on planning a blog article "
+              "about the topic: {topic}. "
+              "You collect 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.",
+    llm=my_llm,  # Use the LLM instance here
+    allow_delegation=False,
+    verbose=True
+)
+
+writer = Agent(
+    role="Content Writer",
+    goal="Write insightful and factually accurate opinion piece about the topic: {topic}",
+    backstory="You're writing a new opinion piece about the topic: {topic}. "
+              "You base your writing on the work of the Content Planner, "
+              "who provides an outline and relevant context about the topic. "
+              "You follow the main objectives and direction of the outline provided by the Content Planner.",
+    llm=my_llm,  # Use the LLM instance here
+    allow_delegation=False,
+    verbose=True
+)
+
+editor = Agent(
+    role="Editor",
+    goal="Edit a given blog post to align with the writing style of the organization.",
+    backstory="You are an editor who reviews blog posts from the Content Writer to ensure they follow journalistic best practices, provide balanced viewpoints, "
+              "and avoid controversial topics when possible.",
+    llm=my_llm,  # Use the LLM instance here
+    allow_delegation=False,
+    verbose=True
+)
+
+# Define your tasks with descriptions, expected output, and the associated agent
+plan = Task(
+    description=(
+        "1. Prioritize the latest trends, key players, "
+        "and noteworthy news on {topic}.\n"
+        "2. Identify the target audience, considering "
+        "their interests and pain points.\n"
+        "3. Develop a detailed content outline including "
+        "an 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(
+    description=(
+        "1. Use the content plan to craft a compelling "
+        "blog post on {topic}.\n"
+        "2. Incorporate SEO keywords naturally.\n"
+        "3. Ensure the post is structured with an engaging introduction, insightful body, and a summarizing conclusion.\n"
+        "4. Proofread for grammatical errors and alignment with the brand's voice."
+    ),
+    expected_output="A well-written blog post in markdown format, "
+                    "ready for publication, each section should have 2 or 3 paragraphs.",
+    agent=writer,
+)
+
+edit = Task(
+    description=("Proofread the given blog post for "
+                 "grammatical errors and alignment with the brand's voice."),
+    expected_output="A well-written blog post in markdown format, "
+                    "ready for publication, each section should have 2 or 3 paragraphs.",
+    agent=editor
+)
+
+# Create the crew with your agents and tasks
+article_crew = Crew(
+    agents=[planner, writer, editor],
+    tasks=[plan, write, edit],
+    manager_llm=False,
+    verbose=True
+)
+
+# Streamlit interface for user input
+st.title('Content Creation for Blog Articles')
+st.write("Enter the topic for your blog article:")
+
+# Input for the topic from the user
+topic = st.text_input("Topic", "Artificial Intelligence")
+
+if st.button('Generate Article'):
+    # Run the crew with the topic
+    result = article_crew.kickoff(inputs={"topic": topic})
+
+    
+    # Display the results as markdown on the Streamlit page
+    st.markdown(result)
+