diff --git a/capture_img.py b/capture_img.py
new file mode 100644
index 0000000..bb666d6
--- /dev/null
+++ b/capture_img.py
@@ -0,0 +1,113 @@
+"""
+    This is a python script to capture images from the camera
+    and save them to the local directory in an interval of every 5 seconds.
+"""
+
+import cv2
+import time
+from datetime import datetime
+import os
+import logging
+
+
+def create_dir(**kwargs) -> None:
+    """
+    This function creates a new directory if it does not exist
+    the name of directory is images
+    """
+    import os
+
+    if kwargs is not None and "path" in kwargs:
+        path = kwargs["path"]
+    else:
+        path = "images"
+
+    format = "%(asctime)s [%(levelname)s] %(message)s"
+    logging.basicConfig(
+        level=logging.INFO,
+        format=format,
+        handlers=[logging.FileHandler("pipeline-1-capture-img.log")],
+    )
+
+    if not os.path.exists(path):
+        try:
+            os.mkdir(path)
+            logging.log(level=logging.INFO, msg=f"Directory {path} Created")
+        except Exception as e:
+            logging.log(level=logging.ERROR, msg=e)
+            logging.log(level=logging.ERROR, msg=f"Unable to create directory {path}")
+    else:
+        logging.log(level=logging.INFO, msg=f"{path} directory already exists")
+
+
+def capture_img(**kwargs) -> None:
+    """
+    This function captures images from the camera and saves them to the local directory
+    in an interval of every 5 seconds.
+    """
+
+    cap = cv2.VideoCapture(0)
+
+    if not cap.isOpened():
+        logging.log(level=logging.ERROR, msg="Unable to read camera feed")
+
+    # Default resolutions of the frame are obtained.The default resolutions are system dependent.
+    # We convert the resolutions from float to integer.
+
+    if not os.path.exists(os.path.join(os.getcwd(), "images")):
+        logging.log(
+            level=logging.FATAL,
+            msg="images directory not found, restart the script to create the directory",
+        )
+        exit(1)
+    else:
+        logging.log(level=logging.INFO, msg="images directory found")
+
+    queue = None
+    if kwargs is not None and "queue" in kwargs:
+        queue = kwargs["queue"]
+
+    while True:
+        ret, frame = cap.read()
+
+        if ret == True:
+            timestamp = datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
+            filename = f"{os.getcwd()}/images/img_{timestamp}.jpg"
+
+            cv2.imwrite(filename, frame)
+            time.sleep(0.2)
+
+            if queue is not None:
+                queue.put(filename)
+
+            logging.log(
+                level=logging.INFO,
+                msg=f"Image saved named img_{timestamp}.jpg in the directory images",
+            )
+
+            # Press Q on keyboard to stop recording
+            if cv2.waitKey(1) & 0xFF == ord("q"):
+                break
+
+            time.sleep(5)
+
+        else:
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    format = "%(asctime)s [%(levelname)s] %(message)s"
+    logging.basicConfig(
+        level=logging.INFO,
+        format=format,
+        handlers=[logging.FileHandler("pipeline-1.log")],
+    )
+
+    logging.log(level=logging.INFO, msg="Starting the pipeline")
+    pipeline = (create_dir, capture_img)
+
+    for func in pipeline:
+        func()
diff --git a/classifier/img.png b/classifier/img.png
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index 0000000..7e6cfaf
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diff --git a/classifier/model.h5 b/classifier/model.h5
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index 0000000..52d7ec4
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diff --git a/classifier/model.py b/classifier/model.py
new file mode 100644
index 0000000..09fb244
--- /dev/null
+++ b/classifier/model.py
@@ -0,0 +1,32 @@
+import tensorflow as tf
+import cv2
+import numpy as np
+from time import perf_counter
+
+
+def predict_monitor(model, img):
+    start = perf_counter()
+    img = np.array(img)
+
+    img = img.astype('float32') / 255.0
+    img = cv2.resize(img, (224, 224))
+    x = np.expand_dims(img, axis=0)
+    preds = model.predict(x)
+    k = np.argmax(preds)
+    print(perf_counter() - start)
+    if k == 0:
+        return "1"
+    elif k == 1:
+        return "2"
+    elif k == 2:
+        return "3"
+    elif k == 3:
+        return "4"
+
+if __name__ == "__main__":
+    model = tf.keras.models.load_model('model.h5')
+
+    img = cv2.imread('img.png')
+
+    print(predict_monitor(model, img))
+
diff --git a/model_yolo/best.pt b/model_yolo/best.pt
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diff --git a/model_yolo/medicakolkata_rcu_mon--8_2022_5_18_17_5_38.jpeg b/model_yolo/medicakolkata_rcu_mon--8_2022_5_18_17_5_38.jpeg
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