Update app.py

app.py

@@ -1,437 +1,440 @@
-# -*- coding: utf-8 -*-
-"""app.py
-Automated Fire and Accident Detection for CCTV with Freshdesk Ticket Creation
-"""
-
-import cv2
-import os
-import PIL.Image as Image
-import gradio as gr
-import numpy as np
-from ultralytics import YOLO
-import requests
-import json
-from datetime import datetime
-import tempfile
-import torch
-import uuid  # Added for unique filenames
-
-# Freshdesk Configuration
-FRESHDESK_DOMAIN = "7kctech-supportdesk.freshdesk.com"
-API_KEY = os.getenv("FRESHDESK_API_KEY", "JoJNI8nIY3hWQsk87e")  # Fallback for local testing
-
-# Base URL for Hugging Face Space
-BASE_URL = "https://huggingface.co/spaces/Zynaly/Surveillance-Intelligent-Camera/tree/main"
-
-# Directory for saving images
-MEDIA_DIR = "media"
-FIRE_DIR = os.path.join(MEDIA_DIR, "fire")
-ACCIDENT_DIR = os.path.join(MEDIA_DIR, "accidents")
-
-# Create directories if they don't exist
-os.makedirs(FIRE_DIR, exist_ok=True)
-os.makedirs(ACCIDENT_DIR, exist_ok=True)
-
-# Fixed thresholds for automated detection
-FIRE_CONF_THRESHOLD = 0.25
-FIRE_IOU_THRESHOLD = 0.45
-ACCIDENT_CONF_THRESHOLD = 0.3
-ACCIDENT_IOU_THRESHOLD = 0.55
-
-# Load models with explicit task definition
-fire_model = YOLO("fire.pt", task="detect")  # Fire detection model
-accident_model = YOLO("best.pt", task="detect")  # Accident detection model
-
-# Function to save image and return its URL
-def save_image(image, incident_type):
-    try:
-        # Generate unique filename
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        unique_id = str(uuid.uuid4())[:8]
-        filename = f"{incident_type}_{timestamp}_{unique_id}.jpg"
-        save_path = os.path.join(MEDIA_DIR, incident_type, filename)
-        
-        # Save the image
-        image.save(save_path)
-        
-        # Construct the URL
-        url = f"{BASE_URL}/{save_path}"
-        return url
-    except Exception as e:
-        print(f"Error saving image: {str(e)}")
-        return None
-
-# Function to create Freshdesk ticket
-# Function to create Freshdesk ticket with image attachment
-# Function to create Freshdesk ticket with image attachment
-def create_freshdesk_ticket(incident_type, confidence_score, img):
-    # Save the image to the appropriate directory and get its local path
-    image_url = None
-    image_path = None
-    if incident_type.lower() == "fire incident":
-        image_url = save_image(img, "fire")
-        image_path = os.path.join(MEDIA_DIR, "fire", os.path.basename(image_url.split("/")[-1]))
-    elif incident_type.lower() == "accident incident":
-        image_url = save_image(img, "accidents")
-        image_path = os.path.join(MEDIA_DIR, "accidents", os.path.basename(image_url.split("/")[-1]))
-    elif incident_type.lower() == "fire and accident incident":
-        fire_url = save_image(img, "fire")
-        accident_url = save_image(img, "accidents")
-        image_url = fire_url or accident_url
-        image_path = os.path.join(MEDIA_DIR, "fire" if fire_url else "accidents", os.path.basename(image_url.split("/")[-1]))
-
-    # Shortened subject
-    subject = f"{incident_type} Detected - Confidence: {confidence_score*100:.1f}%"
-
-    # Detailed description
-    description = f"""
-{incident_type} is critical.
-Details:
-1. Address: 123 Main Street, Lahore
-2. Phone: 923013225853
-3. Confidence Score: {confidence_score*100:.1f}%
-4. Image URL: {image_url or 'https://example.com/roboi.jpg'}
-5. Incident Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
-    """
-
-    ticket_data = {
-        "email": "safe.city@example.com",
-        "subject": subject,
-        "description": description,
-        "priority": 4,  # Urgent
-        "status": 2     # Open
-    }
-
-    # Create ticket
-    url = f"https://{FRESHDESK_DOMAIN}/api/v2/tickets"
-    headers = {"Content-Type": "application/json"}
-
-    response = requests.post(
-        url,
-        auth=(API_KEY, "X"),
-        headers=headers,
-        data=json.dumps(ticket_data)
-    )
-
-    if response.status_code == 201:
-        ticket = response.json()
-        ticket_id = ticket.get('id')
-        print(f"✅ Ticket created successfully: Ticket ID {ticket_id}")
-        print(json.dumps(ticket, indent=2))
-
-        # Attach image to ticket if image_path exists
-        if image_path and os.path.exists(image_path):
-            attachment_url = f"https://{FRESHDESK_DOMAIN}/api/v2/tickets/{ticket_id}/attachments"
-            try:
-                with open(image_path, 'rb') as f:
-                    files = {'attachments[]': (os.path.basename(image_path), f, 'image/jpeg')}
-                    # Do not set Content-Type header; let requests handle it
-                    attachment_response = requests.post(
-                        attachment_url,
-                        auth=(API_KEY, "X"),
-                        files=files
-                    )
-                    if attachment_response.status_code == 201:
-                        print(f"✅ Image attached to ticket {ticket_id}")
-                    else:
-                        print(f"❌ Failed to attach image: {attachment_response.status_code} - {attachment_response.text}")
-            except Exception as e:
-                print(f"❌ Error accessing image file {image_path}: {str(e)}")
-        else:
-            print(f"❌ Image file not found: {image_path}")
-
-        return f"Ticket created for {incident_type} with ID {ticket_id}"
-    else:
-        print(f"❌ Failed to create ticket: {response.status_code} - {response.text}")
-        return f"Failed to create ticket for {incident_type}: {response.status_code} - {response.text}"
-# Image inference function
-def detect_image(image):
-    try:
-        pil_img = image
-        
-        # Fire detection
-        fire_results = fire_model.predict(
-            source=pil_img,
-            conf=FIRE_CONF_THRESHOLD,
-            iou=FIRE_IOU_THRESHOLD,
-            show_labels=True,
-            show_conf=True,
-            imgsz=640,
-            verbose=False
-        )
-        
-        fire_detected = False
-        fire_confidence = 0.0
-        fire_annotated_img = fire_results[0].plot()
-        fire_confidences = []
-        fire_classes = []
-        for r in fire_results:
-            if r.boxes:
-                for box in r.boxes:
-                    confidence = box.conf[0].item()
-                    class_id = int(box.cls[0].item())
-                    fire_confidences.append(confidence)
-                    fire_classes.append(class_id)
-                    if confidence >= FIRE_CONF_THRESHOLD:
-                        fire_detected = True
-                        fire_confidence = max(fire_confidence, confidence)
-        print(f"Fire model raw confidences: {fire_confidences}, classes: {fire_classes}")
-        
-        # Accident detection
-        accident_results = accident_model.predict(
-            source=pil_img,
-            conf=ACCIDENT_CONF_THRESHOLD,
-            iou=ACCIDENT_IOU_THRESHOLD,
-            show_labels=True,
-            show_conf=True,
-            imgsz=640,
-            verbose=False
-        )
-        
-        accident_detected = False
-        accident_confidence = 0.0
-        accident_annotated_img = accident_results[0].plot()
-        accident_confidences = []
-        accident_classes = []
-        accident_boxes = accident_results[0].boxes
-        if accident_boxes:
-            for box in accident_boxes:
-                confidence = box.conf[0].item()
-                class_id = int(box.cls[0].item())
-                accident_confidences.append(confidence)
-                accident_classes.append(class_id)
-                if confidence >= ACCIDENT_CONF_THRESHOLD:
-                    accident_detected = True
-                    accident_confidence = max(accident_confidence, confidence)
-        print(f"Accident model raw confidences: {accident_confidences}, classes: {accident_classes}")
-        
-        # Combine annotated images
-        fire_annotated_img = np.array(fire_annotated_img)
-        accident_annotated_img = np.array(accident_annotated_img)
-        combined_img = Image.fromarray(np.maximum(fire_annotated_img, accident_annotated_img))
-
-        # Detection info
-        detection_info = "Detection Results:\n"
-        if fire_detected:
-            detection_info += f"Fire detected with confidence: {fire_confidence*100:.1f}%\n"
-        else:
-            detection_info += f"No fire detected. Raw confidences: {fire_confidences}, Classes: {fire_classes}\n"
-        if accident_detected:
-            detection_info += f"Accident detected with confidence: {accident_confidence*100:.1f}%\n"
-        else:
-            detection_info += f"No accident detected. Raw confidences: {accident_confidences}, Classes: {accident_classes}\n"
-
-        # Create a single Freshdesk ticket
-        ticket_info = ""
-        if fire_detected and not accident_detected:
-            ticket_info = create_freshdesk_ticket("Fire Incident", fire_confidence, pil_img)
-        elif accident_detected and not fire_detected:
-            ticket_info = create_freshdesk_ticket("Accident Incident", accident_confidence, pil_img)
-        elif fire_detected and accident_detected:
-            ticket_info = create_freshdesk_ticket("Fire and Accident Incident", max(fire_confidence, accident_confidence), pil_img)
-        else:
-            ticket_info = "No ticket created: No incidents detected"
-
-        return combined_img, detection_info, ticket_info
-    
-    except Exception as e:
-        return image, f"Error during detection: {str(e)}\nRaw confidences: Fire {fire_confidences}, Accident {accident_confidences}", "No ticket created due to error"
-
-# Video processing function
-def detect_video(video_path):
-    try:
-        cap = cv2.VideoCapture(video_path)
-        
-        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        fps = int(cap.get(cv2.CAP_PROP_FPS))
-        
-        output_path = tempfile.mktemp(suffix='.mp4')
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-        out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
-        
-        frame_count = 0
-        fire_detected_once = False
-        accident_detected_once = False
-        fire_detection_frames = []
-        accident_detection_frames = []
-        fire_confidences_all = []
-        accident_confidences_all = []
-        fire_classes_all = []
-        accident_classes_all = []
-        
-        while cap.isOpened():
-            ret, frame = cap.read()
-            if not ret:
-                break
-                
-            frame_count += 1
-            pil_img = Image.fromarray(frame[..., ::-1])  # Convert BGR to RGB
-            
-            # Fire detection
-            fire_results = fire_model.predict(
-                source=pil_img,
-                conf=FIRE_CONF_THRESHOLD,
-                iou=FIRE_IOU_THRESHOLD,
-                show_labels=True,
-                show_conf=True,
-                imgsz=640,
-                verbose=False
-            )
-            
-            fire_detected = False
-            fire_confidence = 0.0
-            fire_annotated_frame = fire_results[0].plot()
-            fire_confidences = []
-            fire_classes = []
-            for r in fire_results:
-                if r.boxes:
-                    for box in r.boxes:
-                        confidence = box.conf[0].item()
-                        class_id = int(box.cls[0].item())
-                        fire_confidences.append(confidence)
-                        fire_classes.append(class_id)
-                        if confidence >= FIRE_CONF_THRESHOLD:
-                            fire_detected = True
-                            fire_confidence = max(fire_confidence, confidence)
-                            fire_detection_frames.append(frame_count)
-            fire_confidences_all.extend(fire_confidences)
-            fire_classes_all.extend(fire_classes)
-            
-            # Accident detection
-            accident_results = accident_model.predict(
-                source=pil_img,
-                conf=ACCIDENT_CONF_THRESHOLD,
-                iou=ACCIDENT_IOU_THRESHOLD,
-                show_labels=True,
-                show_conf=True,
-                imgsz=640,
-                verbose=False
-            )
-            
-            accident_detected = False
-            accident_confidence = 0.0
-            accident_annotated_frame = accident_results[0].plot()
-            accident_confidences = []
-            accident_classes = []
-            accident_boxes = accident_results[0].boxes
-            if accident_boxes:
-                for box in accident_boxes:
-                    confidence = box.conf[0].item()
-                    class_id = int(box.cls[0].item())
-                    accident_confidences.append(confidence)
-                    accident_classes.append(class_id)
-                    if confidence >= ACCIDENT_CONF_THRESHOLD:
-                        accident_detected = True
-                        accident_confidence = max(accident_confidence, confidence)
-                        accident_detection_frames.append(frame_count)
-            accident_confidences_all.extend(accident_confidences)
-            accident_classes_all.extend(accident_classes)
-            
-            # Combine annotated frames
-            fire_annotated_frame = np.array(fire_annotated_frame)
-            accident_annotated_frame = np.array(accident_annotated_frame)
-            combined_frame = np.maximum(fire_annotated_frame, accident_annotated_frame)
-            out.write(combined_frame)
-            
-            # Create a single ticket for the first detection of each incident type
-            if fire_detected and not fire_detected_once:
-                create_freshdesk_ticket("Fire Incident", fire_confidence, pil_img)
-                fire_detected_once = True
-            if accident_detected and not accident_detected_once:
-                create_freshdesk_ticket("Accident Incident", accident_confidence, pil_img)
-                accident_detected_once = True
-        
-        cap.release()
-        out.release()
-        
-        detection_info = f"Video processed successfully!\n"
-        detection_info += f"Total frames: {frame_count}\n"
-        detection_info += f"Frames with fire detections: {len(set(fire_detection_frames))}\n"
-        detection_info += f"Frames with accident detections: {len(set(accident_detection_frames))}\n"
-        if fire_detection_frames:
-            detection_info += f"Fire detection frames: {sorted(set(fire_detection_frames))[:10]}...\n"
-        else:
-            detection_info += f"No fire detections. Raw confidences (sample): {fire_confidences_all[:10]}, Classes: {fire_classes_all[:10]}...\n"
-        if accident_detection_frames:
-            detection_info += f"Accident detection frames: {sorted(set(accident_detection_frames))[:10]}...\n"
-        else:
-            detection_info += f"No accident detections. Raw confidences (sample): {accident_confidences_all[:10]}, Classes: {accident_classes_all[:10]}...\n"
-        
-        ticket_info = f"Tickets created: {'Fire' if fire_detected_once else ''}{' and ' if fire_detected_once and accident_detected_once else ''}{'Accident' if accident_detected_once else ''}." if fire_detected_once or accident_detected_once else "No tickets created: No incidents detected"
-        
-        return output_path, detection_info, ticket_info
-    
-    except Exception as e:
-        return None, f"Error processing video: {str(e)}\nRaw confidences: Fire {fire_confidences_all[:10]}, Accident {accident_confidences_all[:10]}", "No ticket created due to error"
-
-# Create Gradio interface for CCTV automation
-with gr.Blocks(title="Rapid Rescue - Automated CCTV Fire and Accident Detection") as iface:
-    gr.Markdown("""
-    # 🚨 Rapid Rescue - Automated CCTV Fire and Accident Detection System
-    
-    This AI system automatically detects fires and accidents in images and videos from CCTV feeds using two YOLO models:
-    - YOLOv8n for fire detection (Confidence: 0.25, IoU: 0.45)
-    - YOLOv8m for accident detection (Confidence: 0.3, IoU: 0.55)
-    
-    **Features:**
-    - Fully automated detection with fixed thresholds
-    - Creates Freshdesk tickets for detected incidents with saved image URLs
-    - Supports both images and videos from CCTV feeds
-    - Images saved in media/fire and media/accidents directories
-    - Optimized for deployment on Hugging Face Spaces
-    
-    **Usage:**
-    1. Upload an image or video from a CCTV feed
-    2. Click process to run detection
-    3. View results with bounding boxes, confidence scores, class labels, and ticket creation status
-    """)
-    
-    with gr.Tabs():
-        with gr.Tab("Image Detection"):
-            with gr.Row():
-                with gr.Column():
-                    image_input = gr.Image(type="pil", label="Upload CCTV Image")
-                    image_button = gr.Button("Detect Fire and Accidents", variant="primary")
-                
-                with gr.Column():
-                    image_output = gr.Image(label="Detection Results")
-                    image_info = gr.Textbox(label="Detection Information", lines=8)
-                    ticket_info = gr.Textbox(label="Ticket Creation Status", lines=2)
-            
-            image_button.click(
-                fn=detect_image,
-                inputs=[image_input],
-                outputs=[image_output, image_info, ticket_info]
-            )
-        
-        with gr.Tab("Video Detection"):
-            with gr.Row():
-                with gr.Column():
-                    video_input = gr.Video(label="Upload Video")
-                    video_button = gr.Button("Process Video", variant="primary")
-                
-                with gr.Column():
-                    video_output = gr.Video(label="Processed Video")
-                    video_info = gr.Textbox(label="Processing Information", lines=8)
-                    ticket_info = gr.Textbox(label="Ticket Creation Status", lines=2)
-            
-            video_button.click(
-                fn=detect_video,
-                inputs=[video_input],
-                outputs=[video_output, video_info, ticket_info]
-            )
-    
-    gr.Markdown("""
-    ### Notes
-    - Freshdesk tickets are created automatically when fire or accident is detected (one per incident type).
-    - Images are saved in media/fire or media/accidents directories with unique filenames.
-    - Ticket includes URL to the saved image.
-    - For videos, one ticket is created per incident type with the first detected frame saved.
-    - Deploy on Hugging Face Spaces with `requirements.txt` and model files (`fire.pt`, `best.pt`).
-    - Debug info includes raw confidence scores and class labels to verify detection performance.
-    """)
-
-# Launch the app
-if __name__ == "__main__":
+# -*- coding: utf-8 -*-
+"""app.py
+Automated Fire and Accident Detection for CCTV with Freshdesk Ticket Creation
+"""
+# Near the top of app.py, after imports
+import ultralytics.nn.modules.block
+from custom_blocks import SCDown
+ultralytics.nn.modules.block.SCDown = SCDown
+import cv2
+import os
+import PIL.Image as Image
+import gradio as gr
+import numpy as np
+from ultralytics import YOLO
+import requests
+import json
+from datetime import datetime
+import tempfile
+import torch
+import uuid  # Added for unique filenames
+
+# Freshdesk Configuration
+FRESHDESK_DOMAIN = "7kctech-supportdesk.freshdesk.com"
+API_KEY = os.getenv("FRESHDESK_API_KEY", "JoJNI8nIY3hWQsk87e")  # Fallback for local testing
+
+# Base URL for Hugging Face Space
+BASE_URL = "https://huggingface.co/spaces/Zynaly/Surveillance-Intelligent-Camera/tree/main"
+
+# Directory for saving images
+MEDIA_DIR = "media"
+FIRE_DIR = os.path.join(MEDIA_DIR, "fire")
+ACCIDENT_DIR = os.path.join(MEDIA_DIR, "accidents")
+
+# Create directories if they don't exist
+os.makedirs(FIRE_DIR, exist_ok=True)
+os.makedirs(ACCIDENT_DIR, exist_ok=True)
+
+# Fixed thresholds for automated detection
+FIRE_CONF_THRESHOLD = 0.25
+FIRE_IOU_THRESHOLD = 0.45
+ACCIDENT_CONF_THRESHOLD = 0.3
+ACCIDENT_IOU_THRESHOLD = 0.55
+
+# Load models with explicit task definition
+fire_model = YOLO("fire.pt", task="detect")  # Fire detection model
+accident_model = YOLO("best.pt", task="detect")  # Accident detection model
+
+# Function to save image and return its URL
+def save_image(image, incident_type):
+    try:
+        # Generate unique filename
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        unique_id = str(uuid.uuid4())[:8]
+        filename = f"{incident_type}_{timestamp}_{unique_id}.jpg"
+        save_path = os.path.join(MEDIA_DIR, incident_type, filename)
+        
+        # Save the image
+        image.save(save_path)
+        
+        # Construct the URL
+        url = f"{BASE_URL}/{save_path}"
+        return url
+    except Exception as e:
+        print(f"Error saving image: {str(e)}")
+        return None
+
+# Function to create Freshdesk ticket
+# Function to create Freshdesk ticket with image attachment
+# Function to create Freshdesk ticket with image attachment
+def create_freshdesk_ticket(incident_type, confidence_score, img):
+    # Save the image to the appropriate directory and get its local path
+    image_url = None
+    image_path = None
+    if incident_type.lower() == "fire incident":
+        image_url = save_image(img, "fire")
+        image_path = os.path.join(MEDIA_DIR, "fire", os.path.basename(image_url.split("/")[-1]))
+    elif incident_type.lower() == "accident incident":
+        image_url = save_image(img, "accidents")
+        image_path = os.path.join(MEDIA_DIR, "accidents", os.path.basename(image_url.split("/")[-1]))
+    elif incident_type.lower() == "fire and accident incident":
+        fire_url = save_image(img, "fire")
+        accident_url = save_image(img, "accidents")
+        image_url = fire_url or accident_url
+        image_path = os.path.join(MEDIA_DIR, "fire" if fire_url else "accidents", os.path.basename(image_url.split("/")[-1]))
+
+    # Shortened subject
+    subject = f"{incident_type} Detected - Confidence: {confidence_score*100:.1f}%"
+
+    # Detailed description
+    description = f"""
+{incident_type} is critical.
+Details:
+1. Address: 123 Main Street, Lahore
+2. Phone: 923013225853
+3. Confidence Score: {confidence_score*100:.1f}%
+4. Image URL: {image_url or 'https://example.com/roboi.jpg'}
+5. Incident Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+    """
+
+    ticket_data = {
+        "email": "safe.city@example.com",
+        "subject": subject,
+        "description": description,
+        "priority": 4,  # Urgent
+        "status": 2     # Open
+    }
+
+    # Create ticket
+    url = f"https://{FRESHDESK_DOMAIN}/api/v2/tickets"
+    headers = {"Content-Type": "application/json"}
+
+    response = requests.post(
+        url,
+        auth=(API_KEY, "X"),
+        headers=headers,
+        data=json.dumps(ticket_data)
+    )
+
+    if response.status_code == 201:
+        ticket = response.json()
+        ticket_id = ticket.get('id')
+        print(f"✅ Ticket created successfully: Ticket ID {ticket_id}")
+        print(json.dumps(ticket, indent=2))
+
+        # Attach image to ticket if image_path exists
+        if image_path and os.path.exists(image_path):
+            attachment_url = f"https://{FRESHDESK_DOMAIN}/api/v2/tickets/{ticket_id}/attachments"
+            try:
+                with open(image_path, 'rb') as f:
+                    files = {'attachments[]': (os.path.basename(image_path), f, 'image/jpeg')}
+                    # Do not set Content-Type header; let requests handle it
+                    attachment_response = requests.post(
+                        attachment_url,
+                        auth=(API_KEY, "X"),
+                        files=files
+                    )
+                    if attachment_response.status_code == 201:
+                        print(f"✅ Image attached to ticket {ticket_id}")
+                    else:
+                        print(f"❌ Failed to attach image: {attachment_response.status_code} - {attachment_response.text}")
+            except Exception as e:
+                print(f"❌ Error accessing image file {image_path}: {str(e)}")
+        else:
+            print(f"❌ Image file not found: {image_path}")
+
+        return f"Ticket created for {incident_type} with ID {ticket_id}"
+    else:
+        print(f"❌ Failed to create ticket: {response.status_code} - {response.text}")
+        return f"Failed to create ticket for {incident_type}: {response.status_code} - {response.text}"
+# Image inference function
+def detect_image(image):
+    try:
+        pil_img = image
+        
+        # Fire detection
+        fire_results = fire_model.predict(
+            source=pil_img,
+            conf=FIRE_CONF_THRESHOLD,
+            iou=FIRE_IOU_THRESHOLD,
+            show_labels=True,
+            show_conf=True,
+            imgsz=640,
+            verbose=False
+        )
+        
+        fire_detected = False
+        fire_confidence = 0.0
+        fire_annotated_img = fire_results[0].plot()
+        fire_confidences = []
+        fire_classes = []
+        for r in fire_results:
+            if r.boxes:
+                for box in r.boxes:
+                    confidence = box.conf[0].item()
+                    class_id = int(box.cls[0].item())
+                    fire_confidences.append(confidence)
+                    fire_classes.append(class_id)
+                    if confidence >= FIRE_CONF_THRESHOLD:
+                        fire_detected = True
+                        fire_confidence = max(fire_confidence, confidence)
+        print(f"Fire model raw confidences: {fire_confidences}, classes: {fire_classes}")
+        
+        # Accident detection
+        accident_results = accident_model.predict(
+            source=pil_img,
+            conf=ACCIDENT_CONF_THRESHOLD,
+            iou=ACCIDENT_IOU_THRESHOLD,
+            show_labels=True,
+            show_conf=True,
+            imgsz=640,
+            verbose=False
+        )
+        
+        accident_detected = False
+        accident_confidence = 0.0
+        accident_annotated_img = accident_results[0].plot()
+        accident_confidences = []
+        accident_classes = []
+        accident_boxes = accident_results[0].boxes
+        if accident_boxes:
+            for box in accident_boxes:
+                confidence = box.conf[0].item()
+                class_id = int(box.cls[0].item())
+                accident_confidences.append(confidence)
+                accident_classes.append(class_id)
+                if confidence >= ACCIDENT_CONF_THRESHOLD:
+                    accident_detected = True
+                    accident_confidence = max(accident_confidence, confidence)
+        print(f"Accident model raw confidences: {accident_confidences}, classes: {accident_classes}")
+        
+        # Combine annotated images
+        fire_annotated_img = np.array(fire_annotated_img)
+        accident_annotated_img = np.array(accident_annotated_img)
+        combined_img = Image.fromarray(np.maximum(fire_annotated_img, accident_annotated_img))
+
+        # Detection info
+        detection_info = "Detection Results:\n"
+        if fire_detected:
+            detection_info += f"Fire detected with confidence: {fire_confidence*100:.1f}%\n"
+        else:
+            detection_info += f"No fire detected. Raw confidences: {fire_confidences}, Classes: {fire_classes}\n"
+        if accident_detected:
+            detection_info += f"Accident detected with confidence: {accident_confidence*100:.1f}%\n"
+        else:
+            detection_info += f"No accident detected. Raw confidences: {accident_confidences}, Classes: {accident_classes}\n"
+
+        # Create a single Freshdesk ticket
+        ticket_info = ""
+        if fire_detected and not accident_detected:
+            ticket_info = create_freshdesk_ticket("Fire Incident", fire_confidence, pil_img)
+        elif accident_detected and not fire_detected:
+            ticket_info = create_freshdesk_ticket("Accident Incident", accident_confidence, pil_img)
+        elif fire_detected and accident_detected:
+            ticket_info = create_freshdesk_ticket("Fire and Accident Incident", max(fire_confidence, accident_confidence), pil_img)
+        else:
+            ticket_info = "No ticket created: No incidents detected"
+
+        return combined_img, detection_info, ticket_info
+    
+    except Exception as e:
+        return image, f"Error during detection: {str(e)}\nRaw confidences: Fire {fire_confidences}, Accident {accident_confidences}", "No ticket created due to error"
+
+# Video processing function
+def detect_video(video_path):
+    try:
+        cap = cv2.VideoCapture(video_path)
+        
+        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps = int(cap.get(cv2.CAP_PROP_FPS))
+        
+        output_path = tempfile.mktemp(suffix='.mp4')
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+        
+        frame_count = 0
+        fire_detected_once = False
+        accident_detected_once = False
+        fire_detection_frames = []
+        accident_detection_frames = []
+        fire_confidences_all = []
+        accident_confidences_all = []
+        fire_classes_all = []
+        accident_classes_all = []
+        
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+                
+            frame_count += 1
+            pil_img = Image.fromarray(frame[..., ::-1])  # Convert BGR to RGB
+            
+            # Fire detection
+            fire_results = fire_model.predict(
+                source=pil_img,
+                conf=FIRE_CONF_THRESHOLD,
+                iou=FIRE_IOU_THRESHOLD,
+                show_labels=True,
+                show_conf=True,
+                imgsz=640,
+                verbose=False
+            )
+            
+            fire_detected = False
+            fire_confidence = 0.0
+            fire_annotated_frame = fire_results[0].plot()
+            fire_confidences = []
+            fire_classes = []
+            for r in fire_results:
+                if r.boxes:
+                    for box in r.boxes:
+                        confidence = box.conf[0].item()
+                        class_id = int(box.cls[0].item())
+                        fire_confidences.append(confidence)
+                        fire_classes.append(class_id)
+                        if confidence >= FIRE_CONF_THRESHOLD:
+                            fire_detected = True
+                            fire_confidence = max(fire_confidence, confidence)
+                            fire_detection_frames.append(frame_count)
+            fire_confidences_all.extend(fire_confidences)
+            fire_classes_all.extend(fire_classes)
+            
+            # Accident detection
+            accident_results = accident_model.predict(
+                source=pil_img,
+                conf=ACCIDENT_CONF_THRESHOLD,
+                iou=ACCIDENT_IOU_THRESHOLD,
+                show_labels=True,
+                show_conf=True,
+                imgsz=640,
+                verbose=False
+            )
+            
+            accident_detected = False
+            accident_confidence = 0.0
+            accident_annotated_frame = accident_results[0].plot()
+            accident_confidences = []
+            accident_classes = []
+            accident_boxes = accident_results[0].boxes
+            if accident_boxes:
+                for box in accident_boxes:
+                    confidence = box.conf[0].item()
+                    class_id = int(box.cls[0].item())
+                    accident_confidences.append(confidence)
+                    accident_classes.append(class_id)
+                    if confidence >= ACCIDENT_CONF_THRESHOLD:
+                        accident_detected = True
+                        accident_confidence = max(accident_confidence, confidence)
+                        accident_detection_frames.append(frame_count)
+            accident_confidences_all.extend(accident_confidences)
+            accident_classes_all.extend(accident_classes)
+            
+            # Combine annotated frames
+            fire_annotated_frame = np.array(fire_annotated_frame)
+            accident_annotated_frame = np.array(accident_annotated_frame)
+            combined_frame = np.maximum(fire_annotated_frame, accident_annotated_frame)
+            out.write(combined_frame)
+            
+            # Create a single ticket for the first detection of each incident type
+            if fire_detected and not fire_detected_once:
+                create_freshdesk_ticket("Fire Incident", fire_confidence, pil_img)
+                fire_detected_once = True
+            if accident_detected and not accident_detected_once:
+                create_freshdesk_ticket("Accident Incident", accident_confidence, pil_img)
+                accident_detected_once = True
+        
+        cap.release()
+        out.release()
+        
+        detection_info = f"Video processed successfully!\n"
+        detection_info += f"Total frames: {frame_count}\n"
+        detection_info += f"Frames with fire detections: {len(set(fire_detection_frames))}\n"
+        detection_info += f"Frames with accident detections: {len(set(accident_detection_frames))}\n"
+        if fire_detection_frames:
+            detection_info += f"Fire detection frames: {sorted(set(fire_detection_frames))[:10]}...\n"
+        else:
+            detection_info += f"No fire detections. Raw confidences (sample): {fire_confidences_all[:10]}, Classes: {fire_classes_all[:10]}...\n"
+        if accident_detection_frames:
+            detection_info += f"Accident detection frames: {sorted(set(accident_detection_frames))[:10]}...\n"
+        else:
+            detection_info += f"No accident detections. Raw confidences (sample): {accident_confidences_all[:10]}, Classes: {accident_classes_all[:10]}...\n"
+        
+        ticket_info = f"Tickets created: {'Fire' if fire_detected_once else ''}{' and ' if fire_detected_once and accident_detected_once else ''}{'Accident' if accident_detected_once else ''}." if fire_detected_once or accident_detected_once else "No tickets created: No incidents detected"
+        
+        return output_path, detection_info, ticket_info
+    
+    except Exception as e:
+        return None, f"Error processing video: {str(e)}\nRaw confidences: Fire {fire_confidences_all[:10]}, Accident {accident_confidences_all[:10]}", "No ticket created due to error"
+
+# Create Gradio interface for CCTV automation
+with gr.Blocks(title="Rapid Rescue - Automated CCTV Fire and Accident Detection") as iface:
+    gr.Markdown("""
+    # 🚨 Rapid Rescue - Automated CCTV Fire and Accident Detection System
+    
+    This AI system automatically detects fires and accidents in images and videos from CCTV feeds using two YOLO models:
+    - YOLOv8n for fire detection (Confidence: 0.25, IoU: 0.45)
+    - YOLOv8m for accident detection (Confidence: 0.3, IoU: 0.55)
+    
+    **Features:**
+    - Fully automated detection with fixed thresholds
+    - Creates Freshdesk tickets for detected incidents with saved image URLs
+    - Supports both images and videos from CCTV feeds
+    - Images saved in media/fire and media/accidents directories
+    - Optimized for deployment on Hugging Face Spaces
+    
+    **Usage:**
+    1. Upload an image or video from a CCTV feed
+    2. Click process to run detection
+    3. View results with bounding boxes, confidence scores, class labels, and ticket creation status
+    """)
+    
+    with gr.Tabs():
+        with gr.Tab("Image Detection"):
+            with gr.Row():
+                with gr.Column():
+                    image_input = gr.Image(type="pil", label="Upload CCTV Image")
+                    image_button = gr.Button("Detect Fire and Accidents", variant="primary")
+                
+                with gr.Column():
+                    image_output = gr.Image(label="Detection Results")
+                    image_info = gr.Textbox(label="Detection Information", lines=8)
+                    ticket_info = gr.Textbox(label="Ticket Creation Status", lines=2)
+            
+            image_button.click(
+                fn=detect_image,
+                inputs=[image_input],
+                outputs=[image_output, image_info, ticket_info]
+            )
+        
+        with gr.Tab("Video Detection"):
+            with gr.Row():
+                with gr.Column():
+                    video_input = gr.Video(label="Upload Video")
+                    video_button = gr.Button("Process Video", variant="primary")
+                
+                with gr.Column():
+                    video_output = gr.Video(label="Processed Video")
+                    video_info = gr.Textbox(label="Processing Information", lines=8)
+                    ticket_info = gr.Textbox(label="Ticket Creation Status", lines=2)
+            
+            video_button.click(
+                fn=detect_video,
+                inputs=[video_input],
+                outputs=[video_output, video_info, ticket_info]
+            )
+    
+    gr.Markdown("""
+    ### Notes
+    - Freshdesk tickets are created automatically when fire or accident is detected (one per incident type).
+    - Images are saved in media/fire or media/accidents directories with unique filenames.
+    - Ticket includes URL to the saved image.
+    - For videos, one ticket is created per incident type with the first detected frame saved.
+    - Deploy on Hugging Face Spaces with `requirements.txt` and model files (`fire.pt`, `best.pt`).
+    - Debug info includes raw confidence scores and class labels to verify detection performance.
+    """)
+
+# Launch the app
+if __name__ == "__main__":
     iface.launch()