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app.py

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+import gradio as gr
+import cv2, os
+import mediapipe as mp
+
+mp_hands = mp.solutions.hands
+hands = mp_hands.Hands(max_num_hands=1)
+
+# ensure slides folder exists
+if not os.path.exists("slides"):
+    os.makedirs("slides")
+
+slides = sorted([f"slides/{img}" for img in os.listdir("slides")])
+index = 0
+
+def detect(frame):
+    global index, slides
+    slides = sorted([f"slides/{img}" for img in os.listdir("slides")])
+    if not slides:
+        import numpy as np
+        blank = np.zeros((600,800,3),dtype=np.uint8)
+        cv2.putText(blank, "Upload slide images in /slides", (50,300),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)
+        return blank
+
+    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,
+                    inputs=gr.Image(source="webcam", streaming=True),
+                    outputs="image",
+                    live=True)
+
+demo.launch()

requirements.txt

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