Status: True
Assertion
Hair and fingernails continue to grow after death.
Results
The given statement can be accurately classified as true based on scientific evidence. Cellular activities do indeed cease upon death due to the cessation of metabolic processes within body cells, and hair and nails grow from active biological processes involving cell division and proliferation in specific regions (hair follicles for hair and matrix at base of fingernail for fingernails). Furthermore, the growth cycle of hair involves anagen, catagen, and telogen phases that require nutrient supply and hormonal signals to initiate.
Here is a breakdown of each point:
1. Cellular activities cease upon death due to cessation of metabolic processes – True: As mentioned earlier, the lack of oxygen and nutrients after death halts cellular respiration and consequently all metabolic functions leading to cell death.
2. Hair and nails grow from active biological processes – True: The growth of hair and fingernails are facilitated by cells actively dividing in their respective regions (hair follicles for hair, matrix at the base of fingernail).
3
Assumptions
* Cellular activities cease upon death due to the cessation of metabolic processes within body cells
* Hair and nails grow from active biological processes, specifically hair follicles (for hair) and matrix at the base of fingernail (for fingernails)
* The growth cycle of hair involves anagen, catagen, and telogen phases that require nutrient supply and hormonal signals to initiate.
* Dead cells cannot carry out these active biological processes or respond to external stimuli like a living organism can.
* The statement is intended as a clarification against the common misconception that hair and fingernails continue growing after death.
This list of assumptions helps establish the scientific basis for understanding why hair and fingernails do not grow postmortem. It relies on established knowledge about cellular activity, growth cycles, and biological processes related to hair and nail development.
Fact Checker Output
<|assistant|> 1. Cellular activities cease upon death due to the cessation of metabolic processes within body cells – True. When an organism dies, cellular respiration halts as there’s no longer a supply of oxygen and nutrients needed for energy production. This leads to a stop in metabolic functions and eventually results in cell death.
2. Hair and nails grow from active biological processes, specifically hair follicles (for hair) and matrix at the base of fingernail (for fingernails) – True. The growth of hair and fingernails is a result of cell division and proliferation in their respective regions, with cells actively dividing to form new tissues and structures.
3. The growth cycle of hair involves anagen, catagen, and telogen phases that require nutrient supply and hormonal signals to initiate – True. Hair goes through three distinct stages during its growth: the active growing phase (anagen), the regression phase (catagen), and the resting/shedding phase (telogen). Nutritional support, including vitamins, minerenas, and amino
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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