main.py

import cvzone
import cv2
from cvzone.HandTrackingModule import HandDetector
import numpy as np
import google.generativeai as genai
import os
from dotenv import load_dotenv
from PIL import Image

load_dotenv()

genai.configure(api_key=os.environ.get('GENAI_API_KEY'))
model = genai.GenerativeModel("gemini-1.5-flash")

# Initialize the webcam to capture video
# The '2' indicates the third camera connected to your computer; '0' would usually refer to the built-in camera
cap = cv2.VideoCapture(0)
cap.set(3, 1280)  # Set the width of the frame
cap.set(4, 720)  # Set the height of the frame

# Initialize the HandDetector class with the given parameters
detector = HandDetector(staticMode=False, maxHands=1, modelComplexity=1, detectionCon=0.7, minTrackCon=0.5)

def getHandInfo(img):
    # Find hands in the current frame
    # The 'draw' parameter draws landmarks and hand outlines on the image if set to True
    # The 'flipType' parameter flips the image, making it easier for some detections
    hands, img = detector.findHands(img, draw=True, flipType=True)

    # Check if any hands are detected
    if hands:
       
        hand = hands[0]  
        lmList = hand["lmList"]  # List of 21 landmarks for the first hand

        # Count the number of fingers up for the first hand
        fingers = detector.fingersUp(hand)
        return fingers, lmList
    else:
        return None
    
def draw(info, prev_pos, canvas, img):
    fingers, lmList = info
    current_pos = None
    if fingers == [0, 1, 0, 0, 0]:
        current_pos = lmList[8][0:2]
        if prev_pos is None:
            prev_pos = current_pos
        cv2.line(canvas, current_pos, prev_pos, (255, 0, 255), 10)
    elif fingers == [1, 0, 0, 0, 0]:
        canvas = np.zeros_like(img)
        
    return current_pos, canvas

def sendToIA(canvas, fingers, model):
    if fingers == [1, 1, 1, 0, 1]:
        pil_image = Image.fromarray(canvas)
        response = model.generate_content(["Solve this math problem, and add a short explantion:", pil_image])
        return response.text
        
prev_pos = None   
canvas = None
image_combined = None