Create app.py

app.py

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+import cv2
+import mediapipe as mp
+import gradio as gr
+import numpy as np
+
+# Initialize MediaPipe Hands
+mp_hands = mp.solutions.hands
+mp_drawing = mp.solutions.drawing_utils
+hands = mp_hands.Hands(max_num_hands=1, min_detection_confidence=0.7)
+
+# Simple gesture classifier
+def classify_gesture(landmarks):
+    if landmarks:
+        thumb_tip = landmarks[4]
+        index_tip = landmarks[8]
+        if thumb_tip.y < index_tip.y:
+            return "A"  # Thumb up
+    return "Unknown"
+
+def process_frame(frame):
+    frame = cv2.flip(frame, 1)
+    h, w, _ = frame.shape
+    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    result = hands.process(rgb)
+
+    gesture = "No hand detected"
+
+    if result.multi_hand_landmarks:
+        for hand_landmarks in result.multi_hand_landmarks:
+            mp_drawing.draw_landmarks(
+                frame, hand_landmarks, mp_hands.HAND_CONNECTIONS
+            )
+            gesture = classify_gesture(hand_landmarks.landmark)
+
+    cv2.putText(frame, f"Sign: {gesture}",
+                (10, 40), cv2.FONT_HERSHEY_SIMPLEX,
+                1, (0, 255, 0), 2)
+
+    cv2.putText(frame, "Made by Simar",
+                (10, h - 10), cv2.FONT_HERSHEY_SIMPLEX,
+                0.8, (255, 0, 255), 2)
+
+    return frame
+
+demo = gr.Interface(
+    fn=process_frame,
+    inputs=gr.Image(source="webcam", streaming=True),
+    outputs=gr.Image(),
+    live=True,
+    title="Sign Language Recognition",
+    description="Demo sign recognition using MediaPipe + OpenCV"
+)
+
+demo.launch()