Status: True
Assertion
The human body has roughly 2-5 million sweat glands.
Results
The average human body contains between approximately 2 to 5 million sweat glands. This assertion is based on scientific research and studies that have observed the number of sweat glands in various individuals, showing a wide range but generally falling within this estimate. Sweat glands are responsible for producing sweat, which helps regulate body temperature through evaporation from the skin surface. The majority of sweat glands (eccrine) are distributed across specific areas such as palms, soles, forehead, and underarms. Sweat is composed primarily of water, electrolytes like sodium, potassium, and chloride, as well as trace amounts of urea, lactic acid, and ammonia. External factors such as exercise or a hot environment can activate the sweat glands to help regulate body temperature by promoting evaporation from the skin surface.
– False: [No explanation needed]
– Debatable: [No explanation needed]
Assumptions
<|assistant|> – The average human body contains between approximately 2 to 5 million sweat glands, as stated in scientific research and studies on human anatomy.
– Sweat glands are responsible for producing sweat that helps regulate body temperature and maintain homeostasis.
– The majority of sweat glands are distributed across the palms of hands, soles of feet, forehead, and underarms.
– Sweat is composed primarily of water, electrolytes (sodium, potassayer, chloride), and trace amounts of urea, lactic acid, and ammonia.
– External factors such as exercise or a hot environment can trigger the activation of sweat glands to help regulate body temperature through evaporation on the skin surface.
– The number of sweat glands may vary among individuals due to genetic factors, age, or certain medical conditions like hyperhidrosis (excessive sweating) or focal hyperhidrosis.
Fact Checker Output
<|assistant|> – True: The average human body contains between approximately 2 to 5 million sweat glands. This assertion is based on scientific research and studies that have observed the number of sweat glands in various individuals, showing a wide range but generally falling within this estimate.
– True: Sweat glands are responsible for producing sweat, which helps regulate body temperature through evaporation from the skin surface. This mechanism is well established in human physiology and widely accepted by the scientific community.
– True: The majority of sweat glands (eccrine) are distributed across specific areas such as palms, soles, forehead, and underarms. These locations have a higher concentration of sweat glands compared to other parts of the body.
– True: Sweat is composed primarily of water, electrolytes like sodium, potassium, and chloride, as well as trace amounts of urea, lactic acid, and ammonia. This composition has been identified through biochemical analysis of sweat samples.
– True: External factors such as exercise or a hot environment can activate the sweat glands to help regulate body temperature by promoting ev
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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