add all changes

sign_language_backend/.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
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+lib/
+lib64/
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+var/
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+*.egg-info/
+.installed.cfg
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+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
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+# Unit test / coverage reports
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+.hypothesis/
+
+# Translations
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+
+# Django stuff:
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+
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+# However, in case of collaboration, if having platform-specific dependencies or using Docker, you probably want to ignore it.
+Pipfile.lock
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+__pypackages__/
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+
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+.python-version
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+pylint.log
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+# mkdocs documentation
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+dmypy.json
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+# Pyre type checker
+.pyre/
+

sign_language_backend/app.py

+from flask import Flask, request, jsonify
+import base64
+import cv2
+import numpy as np
+import mediapipe as mp
+import tensorflow as tf
+import logging
+
+app = Flask(__name__)
+
+# Load the model
+model = tf.keras.models.load_model('./finalModel.h5')
+
+mp_holistic = mp.solutions.holistic
+holistic = mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5)
+
+sequence = []
+sentence = []
+predictions = []
+threshold = 0.7
+actions = ["haloo", "dannwa", "mama", "obata", "puluwan", "suba", "udaasanak"]
+
+def mediapipe_detection(image, model):
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image.flags.writeable = False
+    results = model.process(image)
+    image.flags.writeable = True
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    return image, results
+
+def extract_keypoints(results):
+    pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)
+    face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*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, face, lh, rh])
+
+@app.route('/endpoint', methods=['POST'])
+def process_frame():
+    try:
+        data = request.json
+        frame_data = data['frame']
+        
+        # Fix incorrect padding for base64 string
+        frame_data += "=" * ((4 - len(frame_data) % 4) % 4)
+        
+        # Decode the base64 string to an image
+        decoded_data = base64.b64decode(frame_data)
+        np_data = np.frombuffer(decoded_data, dtype=np.uint8)
+        frame = cv2.imdecode(np_data, cv2.IMREAD_COLOR)
+        
+        if frame is None:
+            raise ValueError("Failed to decode image")
+        
+        # Process the frame with Mediapipe and your model
+        image, results = mediapipe_detection(frame, holistic)
+        keypoints = extract_keypoints(results)
+        sequence.append(keypoints)
+        sequence = sequence[-50:]
+        
+        if len(sequence) == 50:
+            res = model.predict(np.expand_dims(sequence, axis=0))[0]
+            predictions.append(np.argmax(res))
+        
+            # Implement your prediction logic
+            if np.unique(predictions[-25:])[0] == np.argmax(res):
+                if res[np.argmax(res)] > threshold:
+                    if len(sentence) > 0:
+                        if actions[np.argmax(res)] != sentence[-1]:
+                            sentence.append(actions[np.argmax(res)])
+                    else:
+                        sentence.append(actions[np.argmax(res)])
+        
+            if len(sentence) > 5:
+                sentence = sentence[-5:]
+
+        # Print predictions and sentence to the console
+        print(f"Predicted: {actions[np.argmax(res)]}")
+        print(f"Sentence: {sentence}")
+        
+        return jsonify({'message': 'Frame processed', 'sentence': sentence}), 200
+    
+    except Exception as e:
+        logging.exception("Exception in /endpoint")
+        return jsonify({'message': 'Error processing frame', 'error': str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(debug=True, port=8000)