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app.py

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+import gradio as gr
+import cv2
+import mediapipe as mp
+import numpy as np
+import os
+
+mp_hands = mp.solutions.hands
+hands = mp_hands.Hands(max_num_hands=1)
+slides = sorted([f"slides/{img}" for img in os.listdir("slides")])
+index = 0
+
+def detect(frame):
+    global index
+    h, w, c = frame.shape
+    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    result = hands.process(frame_rgb)
+
+    if result.multi_hand_landmarks:
+        lm = result.multi_hand_landmarks[0]
+        x = lm.landmark[mp_hands.HandLandmark.WRIST].x * w
+        if x < w * 0.3:
+            index = max(0, index - 1)
+        elif x > w * 0.7:
+            index = min(len(slides)-1, index + 1)
+
+    slide = cv2.imread(slides[index])
+    slide = cv2.cvtColor(slide, cv2.COLOR_BGR2RGB)
+    return slide
+
+demo = gr.Interface(
+    fn=detect,
+    live=True,
+    inputs=gr.Image(source="webcam", streaming=True),
+    outputs="image"
+)
+
+demo.launch()

requirements.txt

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+gradio
+opencv-python-headless
+mediapipe
+numpy