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main.py

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+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('img.png')
+
+col1, col2 = st.columns([3,2])
+with col1:
+    run = st.checkbox('Run', value=True)
+    FRAME_WINDOW = st.image([])
+ 
+with col2:
+    st.title("Answer")
+    output_text_area = st.subheader("")
+
+genai.configure(api_key="AIzaSyAu7w2tMO4kIAiB-RDMh8vywmF8OqBjpQk")
+model = genai.GenerativeModel('gemini-1.5-flash')
+
+# Initialize the webcam to capture video
+cap = cv2.VideoCapture(0)  # Try using 0 for built-in camera
+cap.set(3,1280)
+cap.set(4,720)
+
+detector = HandDetector(staticMode=False, maxHands=1, modelComplexity=1, detectionCon=0.7, minTrackCon=0.5)
+
+def getHandInfo(img):
+    if img is None or not img.any():
+        return None
+    hands, img = detector.findHands(img, draw=False, flipType=True)
+    if hands:
+        hand = hands[0]
+        lmList = hand["lmList"]
+        fingers = detector.fingersUp(hand)
+        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
+output_text = ""
+
+while True:
+    success, img = cap.read()
+    if not success or img is None:
+        continue  # Skip this iteration if the frame is not captured properly
+    img = cv2.flip(img, 1)
+
+    if canvas is None:
+        canvas = np.zeros_like(img)
+
+    info = getHandInfo(img)
+    if info:
+        fingers, lmList = info
+        prev_pos, canvas = draw(info, prev_pos, canvas)
+        output_text = sendToAI(model, canvas, fingers)
+
+    image_combined = cv2.addWeighted(img, 0.7, canvas, 0.3, 0)
+    FRAME_WINDOW.image(image_combined, channels="BGR")
+
+    if output_text:
+        output_text_area.text(output_text)
+
+    cv2.waitKey(1)

requirements.txt

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+cvzone
+opencv-python
+numpy
+google-generativeai
+Pillow
+streamlit