diff --git a/Camera-Detection/alarm.wav b/Camera-Detection/alarm.wav
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diff --git a/Camera-Detection/bestnew.pt b/Camera-Detection/bestnew.pt
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diff --git a/Camera-Detection/main.py b/Camera-Detection/main.py
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+++ b/Camera-Detection/main.py
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+import cv2
+import base64
+from ultralytics import YOLO
+import math
+import pymongo
+from datetime import datetime
+
+# MongoDB connection
+client = pymongo.MongoClient("mongodb://localhost:27017/")
+db = client["animal_detection"]
+collection = db["detections"]
+
+camera_ids = [0]
+
+captures = []
+
+# Open and configure the cameras
+for i in camera_ids:
+    cap = cv2.VideoCapture(i)
+    cap.set(3, 1200)
+    cap.set(4, 720)
+    captures.append(cap)
+
+model = YOLO('bestnew.pt')
+
+classNames = ['elephant']
+classColors = [(0, 255, 0)]
+
+# Convert alarm.wav to base64
+with open('alarm.wav', 'rb') as file:
+    sound_data = file.read()
+sound_base64 = base64.b64encode(sound_data).decode('utf-8')
+
+while True:
+    # Iterate through each camera and perform detection
+    for i, cap in enumerate(captures):
+        success, img = cap.read()
+        results = model(img, stream=True)
+
+        boxes_detected = False  # Flag to track if any boxes were detected
+
+        for r in results:
+            boxes = r.boxes
+            for box in boxes:
+                x1, y1, x2, y2 = box.xyxy[0]
+                x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+                w, h = x2 - x1, y2 - y1
+
+                # Get class index
+                cls = int(box.cls[0])
+
+                # Get class name and color
+                animal_name = classNames[cls]
+                color = classColors[cls]
+
+                # Calculate confidence
+                conf = math.ceil((box.conf[0] * 100)) / 100
+
+                # Check if confidence is above the threshold
+                if conf >= 0.6:
+                    # Draw bounding box with class color
+                    cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
+
+                    # Draw class name and confidence
+                    cv2.putText(img, f'{animal_name} {conf}', (max(0, x1), max(35, y1)), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
+
+                    if i == 0:
+                        areas = "area1"
+                    elif i == 1:
+                        areas = "area2"
+                    elif i == 2:
+                        areas = "area3"
+
+                    # Get the animal image within the bounding box
+                    animal_image = img[y1:y2, x1:x2]
+
+                    # Convert the image to base64 string
+                    _, image_buffer = cv2.imencode('.jpg', animal_image)
+                    image_base64 = base64.b64encode(image_buffer).decode('utf-8')
+
+                    # Insert into MongoDB
+                    current_time = datetime.now().strftime("%Y-%m-%d,%H:%M:%S")
+                    detection_data = {
+                        "documentno": "first",
+                        "area_name": areas,
+                        "animal_name": animal_name,
+                        "confidence": conf,
+                        "time": current_time,
+                        "animal_image": image_base64,
+                        "sound_file": sound_base64  # Add sound file as base64
+                    }
+                    collection.insert_one(detection_data)
+
+                    boxes_detected = True  # Set flag indicating boxes were detected
+
+        if not boxes_detected:
+            # Draw "No Detection" message
+            cv2.putText(img, 'No Detection', (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 2)
+            detection_data = {
+                "documentno":"second",
+                "no_detection": "No Animal Present Near Railway Track"
+            }
+            collection.insert_one(detection_data)
+        cv2.imshow(f"Camera {i}", img)
+
+    # exit if 'q' is pressed
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# Release all the captures and close windows
+for cap in captures:
+    cap.release()
+
+cv2.destroyAllWindows()
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