NutriProof Project

Features/Workflow

• Utilizes GPT-4o to extract factual claims from input text
• Fine-tunes GPT-4o's performance for identifying and judging nutrientional/health related claims.
• Reformats claims into queries optimized for Wolfram Alpha
• Processes claims through Wolfram Alpha for mathematical/factual accuracy
• Generates JSON records of claims, verification steps, and results
• Maintains a timestamped history of all fact-checking operations

Installation

Backend Setup

1. Clone the repository
2. Install dependencies:

pip install -r requirements.txt

Running the Application

python app.py

The server will start at http://localhost:5000.

API Usage

Fact-Check Endpoint

URL: /api/fact-check Method: POST Content-Type: application/json

Request Body:

{
  "text": "The radius of the Earth is 6,371 kilometers. The speed of light is approximately 300,000 kilometers per second."
}

Response:

[
  {
    "claim": "The radius of the Earth is 6,371 kilometers.",
    "wolfram_query": "What is the radius of the Earth in kilometers?",
    "wolfram_response": "Earth radius ≈ 6371 km",
    "verification": "True"
  },
  {
    "claim": "The speed of light is approximately 300,000 kilometers per second.",
    "wolfram_query": "What is the speed of light in kilometers per second?",
    "wolfram_response": "Speed of light = 299,792 km/s",
    "verification": "Approximately True"
  }
]

Example Usage

Using cURL

curl -X POST http://localhost:5000/api/fact-check \
  -H "Content-Type: application/json" \
  -d '{"text":"The radius of the Earth is 6,371 kilometers. The speed of light is approximately 300,000 kilometers per second."}'

Using Python Requests

import requests
import json

url = "http://localhost:5000/api/fact-check"
payload = {
    "text": "The radius of the Earth is 6,371 kilometers. The speed of light is approximately 300,000 kilometers per second."
}
headers = {"Content-Type": "application/json"}

response = requests.post(url, data=json.dumps(payload), headers=headers)
print(response.json())