Fact Check: The Sahara is the largest hot desert in the world.

Status: True

Assertion

The Sahara is the largest hot desert in the world.

Results

The definition of a “hot desert” being used as a criterion for measuring and comparing different deserts allows for a standardized approach to classifying and evaluating various arid regions based on their temperature profiles. This helps in understanding the unique characteristics of hot deserts compared to other types, like cold or semi-arid deserts.:

– True: Having accurate and comprehensive geographical data covering Earth’s surface areas is essential for any comparative analysis, including those involving different ecosystems such as deserts. This enables researchers and scientists to study the distribution of land types globally with precision.:

– True: Knowledge about the specific characteristics that define the Sahara Desert—such as its climate (hot temperatures throughout the year), vegetation (limited plant life due to aridity), and topography (largely flat or undulating surfaces)—is crucial for distinguishing it from other desert regions. This knowledge helps in understanding why the Sahara is one of the most well-known deserts in the world.:

– True: Recognizing the countries located within North Africa that host the Sahara Desert (e.g., Algeria, Ch

Assumptions

Output: – The definition of a “hot desert” is used as a criterion for measuring and comparing different deserts.
– Accurate and comprehensive geographical data on Earth’s surface area, including land areas covered by various types of ecosystems such as deserts.
– Knowledge about the specific characteristics that define the Sahara Desert, distinguishing it from other desert regions around the world (e.g., climate, vegetation, and topography).
– Recognition of the countries in North Africa where the Sahara Desert is located.
– Availability of reliable sources or data to determine the area of the Sahara Desert accurately (approximately 9.2 million square kilometers).
– Acceptance that there are different types of deserts, and hot deserts can be identified based on specific temperature criteria.
– The information provided is up-to-date and accurate as of the time it was gathered or published.

Fact Checker Output

<|assistant|> – True: The definition of a “hot desert” being used as a criterion for measuring and comparing different deserts allows for a standardized approach to classifying and evaluating various arid regions based on their temperature profiles. This helps in understanding the unique characteristics of hot deserts compared to other types, like cold or semi-arid deserts.

– True: Having accurate and comprehensive geographical data covering Earth’s surface areas is essential for any comparative analysis, including those involving different ecosystems such as deserts. This enables researchers and scientists to study the distribution of land types globally with precision.

– True: Knowledge about the specific characteristics that define the Sahara Desert—such as its climate (hot temperatures throughout the year), vegetation (limited plant life due to aridity), and topography (largely flat or undulating surfaces)—is crucial for distinguishing it from other desert regions. This knowledge helps in understanding why the Sahara is one of the most well-known deserts in the world.

– True: Recognizing the countries located within North Africa that host the Sahara Desert (e.g., Algeria, Ch

Model Used

microsoft/Phi-3-mini-4k-instruct-gguf

Script Name

fact_checker_mongodb.py

Script Code

import os
import sys
import requests
from langchain_openai.llms import OpenAI
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
from statements import get_random_statement
from mongodb_helper import insert_record  # Import MongoDB helper functions
import html

# Load environment variables from .env file
load_dotenv()

# Define category IDs
CATEGORY_IDS = {
    "True": 2,
    "False": 3,
    "Debatable": 4,
    "Unknown": 6
}

def create_wordpress_post(title, content, category):
    data = {
        "title": title,
        "content": content,
        "status": "publish",
        "categories": [CATEGORY_IDS[category]]
    }

    response = requests.post(
        os.getenv("WORDPRESS_POSTS_URL"),
        json=data,
        auth=(os.getenv("WORDPRESS_USERNAME"), os.getenv("WORDPRESS_PASSWORD"))
    )

    if response.status_code == 201:
        print("Blog post created successfully.")
    else:
        print(f"Failed to create blog post: {response.status_code} - {response.text}")

def fact_check(assertion):
    llm = OpenAI(temperature=0.7, model=os.getenv("MODEL_NAME"))

    # Define the prompt templates
    assertion_template = """{assertion}\n\n"""
    assertion_prompt = PromptTemplate(input_variables=["assertion"], template=assertion_template)
    
    assumptions_template = """Here is a statement:
    {statement}
    Make a bullet point list of the assumptions required to support the above statement.\n\n"""
    assumptions_prompt = PromptTemplate(input_variables=["statement"], template=assumptions_template)
    
    fact_checker_template = """Here is a bullet point list of assertions:
    {assertions}
    For each assumption, determine whether it is true or false. Explain your reasoning.\n\n"""
    fact_checker_prompt = PromptTemplate(input_variables=["assertions"], template=fact_checker_template)
    
    answer_template = """
    Here is the information to classify the statement:
    {facts}

    Based on the above information, how would you classify the statement? Respond with one of the following options followed by a colon and space:
    - True: [Explanation]
    - False: [Explanation]
    - Debatable: [Explanation]
    """
    answer_prompt = PromptTemplate(input_variables=["facts"], template=answer_template)
    
    # Format prompts and extract the string content
    formatted_assertion = assertion_prompt.format_prompt(assertion=assertion).text
    assertion_output = llm.invoke(formatted_assertion)
    
    formatted_assumptions = assumptions_prompt.format_prompt(statement=assertion_output).text
    assumptions_output = llm.invoke(formatted_assumptions)
    
    formatted_fact_checker = fact_checker_prompt.format_prompt(assertions=assumptions_output).text
    fact_checker_output = llm.invoke(formatted_fact_checker)
    
    formatted_answer = answer_prompt.format_prompt(facts=fact_checker_output).text
    final_output = llm.invoke(formatted_answer)
    
    return {
        "assertion_output": assertion_output,
        "assumptions_output": assumptions_output,
        "fact_checker_output": fact_checker_output,
        "final_output": final_output,
    }

def extract_status_and_reasoning(final_output):
    final_output = final_output.strip()
    if "True:" in final_output:
        status_start = final_output.find("True:")
        status = "True"
    elif "False:" in final_output:
        status_start = final_output.find("False:")
        status = "False"
    elif "Debatable:" in final_output:
        status_start = final_output.find("Debatable:")
        status = "Debatable"
    else:
        return "Unknown", final_output

    reasoning = final_output[status_start + len(status) + 1:].strip()
    return status, reasoning

if __name__ == "__main__":
    if len(sys.argv) > 1:
        assertion = sys.argv[1]
    else:
        assertion = get_random_statement()
    
    print(assertion)
    submission = fact_check(assertion)
    
    # Print the detailed outputs to inspect their structure
    for key, value in submission.items():
        print(f"{key}: {value}")
    
    # Extract the final output for status determination and reasoning
    final_output = submission['final_output']
    status, reasoning = extract_status_and_reasoning(final_output)
    
    # Record the result in MongoDB
    try:
        print("Attempting to insert record into MongoDB...")
        insert_record(
            script_name="fact_checker_mongodb.py",
            script_code=html.escape(open(__file__).read()),
            assertion=assertion,
            status=status,
            submission=submission,  # Store the entire submission for detailed analysis
            model=os.getenv("MODEL_NAME")
        )
        print("Record inserted into MongoDB successfully.")
    except Exception as e:
        print(f"Failed to insert record into MongoDB: {e}")
    
    print(final_output)
    
    # Create a blog post on WordPress
    blog_title = f"Fact Check: {assertion}"
    blog_content = f"""
    <h1>Status: {status}</h1>
    <h2>Assertion</h2>
    <p>{assertion}</p>
    <h2>Results</h2>
    <p>{reasoning}</p>
    <h3>Assumptions</h3>
    <p>{submission['assumptions_output']}</p>
    <h3>Fact Checker Output</h3>
    <p>{submission['fact_checker_output']}</p>
    <h4>Model Used</h4>
    <p>{os.getenv("MODEL_NAME")}</p>
    <h4>Script Name</h4>
    <p>fact_checker_mongodb.py</p>
    <h4>Script Code</h4>
    <pre>{html.escape(open(__file__).read())}</pre>
    """
    create_wordpress_post(blog_title, blog_content, status)

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