calculator.py

import cvzone
import cv2
from cvzone.HandTrackingModule import HandDetector
import numpy as np
import google.generativeai as genai
from PIL import Image
import streamlit as st

st.set_page_config(layout="wide")
st.image('D:\\projects\\Guestures calculator\\MathGestures.png')

col1,col2=st.columns([2,1])
with col1:
    run =  st.checkbox('Run',value=True)
    FRAME_WINDOW=st.image([])

with col2:
    output_text_area = st.title("Answer")
    output_text_area = st.subheader("")


genai.configure(api_key="AIzaSyBUOFA_VaMYT9yfOW6WYVuPKFLwWH1KJFE")
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)
cap.set(4,720)


# Initialize the HandDetector class with the given parameters
detector = HandDetector(staticMode=False, maxHands=2, 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:
        # Information for the first hand detected
        hand = hands[0]  # Get the first hand detected
        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)
        print(fingers)
        return fingers,lmList
    else:
        return None
    
def draw (info,prev_pos,canvas):
    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 sendToAI(model,canvas,fingers):
    if fingers == [1,1,1,1,0]:
        pil_image = Image.fromarray(canvas)
        response = model.generate_content(["Solve this Math Problem",pil_image])
        return response.text

prev_pos = None
canvas = None
image_combined = None
output_text = ""

# Continuously get frames from the webcam
while True:
    # Capture each frame from the webcam
    # 'success' will be True if the frame is successfully captured, 'img' will contain the frame
    success, img = cap.read()
    img = cv2.flip(img,1)

    if canvas is None:
        canvas = np.zeros_like(img)


    info = getHandInfo(img)
    if info:
        fingers,lmlist = info
        # print(fingers)
        prev_pos,canvas = draw(info,prev_pos,canvas)
        output_text = sendToAI(model,canvas,fingers)

    image_combined = cv2.addWeighted(img,0.9,canvas,0.9,0)


    FRAME_WINDOW.image(image_combined,channels="BGR")

    if output_text:
        output_text_area.text(output_text)
    # Display the image in a window
    # cv2.imshow("Image", img)
    # cv2.imshow("canvas", canvas)
    # cv2.imshow("image_combined", image_combined)

    # Keep the window open and update it for each frame; wait for 1 millisecond between frames
    cv2.waitKey(1)