Groq

from groq import Groq
import json

from honeyhive import HoneyHiveTracer, trace

HoneyHiveTracer.init(
    api_key="MY_HONEYHIVE_API_KEY",
    project="MY_HONEYHIVE_PROJECT_NAME",
)

client = Groq(
    api_key="MY_GROQ_API_KEY",
)

def evaluate_post(post: str) -> dict:
    evaluation_prompt = f"""
    Evaluate the following blog post based on these criteria (rate each from 1-5):
    1. Engagement: How well does it capture and maintain reader interest?
    2. Clarity: How clear and well-structured is the content?
    3. Value: How informative and valuable is the content?
    
    Blog post:
    {post}
    
    Respond in this exact JSON format:
    {{
        "engagement": <score>,
        "clarity": <score>,
        "value": <score>,
        "total": <sum of scores>
    }}
    """
    
    response = client.chat.completions.create(
        messages=[{"role": "user", "content": evaluation_prompt}],
        model="llama3-8b-8192",
        response_format={"type": "json_object"}
    )
    
    # Parse the response as a dictionary
    return json.loads(response.choices[0].message.content)

@trace
def generate_blog_post(topic: str) -> dict:
    prompt = f"Write a compelling blog post about {topic}. Make it engaging and informative."
    
    response = client.chat.completions.create(
        messages=[{"role": "user", "content": prompt}],
        model="llama3-8b-8192",
    )
    # Evaluate the generated post right away
    post = response.choices[0].message.content
    evaluation = evaluate_post(post)
    return {
        "content": post,
        "evaluation": evaluation
    }

def main():
    # Topics for blog posts
    topics = [
        "The Future of AI in Healthcare",
        "Sustainable Living in 2024",
        "Digital Privacy in the Modern Age",
        "The Rise of Remote Work",
        "Mindfulness and Technology Balance"
    ]
    
    # Generate blog posts
    print("Generating blog posts...")
    posts = [generate_blog_post(topic) for topic in topics]
    
    # Find the highest-rated post
    best_post_index = max(range(len(posts)), key=lambda i: posts[i]['evaluation']['total'])
    
    print("\nEvaluation Results:")
    for i, post in enumerate(posts):
        print(f"\nPost {i+1}: {topics[i]}")
        print(f"Engagement: {post['evaluation']['engagement']}")
        print(f"Clarity: {post['evaluation']['clarity']}")
        print(f"Value: {post['evaluation']['value']}")
        print(f"Total Score: {post['evaluation']['total']}")
    
    print("\n=== Best Rated Blog Post ===")
    print(f"Topic: {topics[best_post_index]}")
    print(posts[best_post_index]['content'])
    print(posts[best_post_index]['evaluation'])

main()