Michael Stattelman mstatt

Multi-Modal Lie Detection System using an Agentic ReAct Approach: Step-by-Step Tutorial

Author: rUv
Created by: rUv, cause he could

🤯 Zoom calls will never be the same. I think I might have just created the world’s most powerful lie detector tutorial using deep research.

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	<title>FALCONLENS // Image Forensics v1.2</title>
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	@import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;700&family=Roboto+Mono:wght@300;400&display=swap');

	:root {

	# pip install streamlit pytube youtube-transcript-api transformers
	# streamlit run app.py

	import streamlit as st
	from youtube_transcript_api import YouTubeTranscriptApi
	import re
	import pandas as pd
	from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline
	import nltk

	import os
	import cv2
	import torch
	from PIL import Image
	from transformers import AutoProcessor ,AutoImageProcessor
	from transformers import AutoModelForCausalLM


	## -------------------------------------------------------------------------------------------------------------------

	Prompt engineering refers to the process of designing and refining prompts for language models or AI systems. It involves crafting specific instructions or queries to elicit desired responses from the model.

	Prompt engineering is crucial because language models like ChatGPT, as powerful as they are, require clear and well-defined input to generate accurate and relevant outputs. By carefully constructing prompts, developers can guide the model's behavior, improve its performance, and make it more reliable for specific tasks or applications.

	Effective prompt engineering involves several strategies:

	1. Specifying the format: Designing prompts that specify the desired format of the response. For example, using placeholders like "[Title]" or "[Person]" to indicate where specific information should be inserted.

	2. Providing context: Giving the model relevant background information or context to improve its understanding of the task or question at hand.

	# USAGE
	# python basic-ocr-with-spellcheck.py --image <imagename>

	# import the necessary packages
	from textblob import TextBlob
	import pytesseract
	import argparse
	import cv2

	# construct the argument parser and parse the arguments