Initial commit: Upload Gesture AI to Hugging Face

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model
+import mediapipe as mp
+
+model = load_model('gesture_model.h5')
+actions = ['I', 'help', 'need', 'sleep', 'angry', 'urgent']
+threshold = 0.8
+
+mp_holistic = mp.solutions.holistic
+
+def extract_keypoints(results):
+    pose = np.array([[res.x, res.y, res.z] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33 * 3)
+    lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21 * 3)
+    rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21 * 3)
+    return np.concatenate([pose, lh, rh])
+
+def predict_gesture(video_path):
+    cap = cv2.VideoCapture(video_path)
+    sequence = []
+    sentence = []
+
+    with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            results = holistic.process(image)
+            keypoints = extract_keypoints(results)
+            sequence.append(keypoints)
+            sequence = sequence[-30:]
+
+            if len(sequence) == 30:
+                res = model.predict(np.expand_dims(sequence, axis=0))[0]
+                if res[np.argmax(res)] > threshold:
+                    action = actions[np.argmax(res)]
+                    if not sentence or sentence[-1] != action:
+                        sentence.append(action)
+
+        cap.release()
+        return ' '.join(sentence)
+
+iface = gr.Interface(
+    fn=predict_gesture,
+    inputs=gr.Video(label="Upload your gesture video"),
+    outputs="text",
+    title="Gesture Recognition AI",
+    description="Upload a short gesture video (e.g., showing 'I need help') and get the recognized sentence."
+)
+
+iface.launch()

requirements.txt

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+tensorflow
+opencv-python
+mediapipe
+gradio
+numpy