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app.py

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+import os
+import cv2
+import tempfile
+import requests
+import base64
+import numpy as np
+import logging
+from dataclasses import dataclass
+from typing import Optional, Union, Tuple
+from PIL import Image
+from io import BytesIO
+from ultralytics import YOLO
+import streamlit as st
+import yt_dlp as youtube_dl
+from config import Config
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+@dataclass
+class DetectionResult:
+    """Data class to store detection results"""
+    success: bool
+    image: Optional[np.ndarray] = None
+    error_message: Optional[str] = None
+
+@st.cache_resource
+def load_yolo_model(model_name: str) -> YOLO:
+    """Load YOLO model with caching"""
+    try:
+        if model_name not in Config.AVAILABLE_MODELS:
+            raise ValueError(f"Invalid model name: {model_name}")
+        return YOLO(model_name)
+    except Exception as e:
+        logger.error(f"Error loading model: {e}")
+        raise RuntimeError(f"Failed to load model: {str(e)}")
+
+class YOLOModel:
+    """Class to handle YOLO model operations"""
+    def __init__(self, model_name: str = Config.DEFAULT_MODEL):
+        if not Config.validate_config():
+            raise RuntimeError("Invalid configuration")
+        self.model = load_yolo_model(model_name)
+        
+    def detect_objects(self, image: np.ndarray) -> DetectionResult:
+        """Perform object detection on the input image"""
+        if self.model is None:
+            return DetectionResult(False, error_message="Model not loaded")
+        
+        try:
+            results = self.model(image)
+            annotated_image = image.copy()
+            
+            for result in results[0].boxes:
+                x1, y1, x2, y2 = map(int, result.xyxy[0])
+                label = self.model.names[int(result.cls)]
+                confidence = result.conf.item()
+                
+                if confidence < Config.CONFIDENCE_THRESHOLD:
+                    continue
+                    
+                cv2.rectangle(
+                    annotated_image, 
+                    (x1, y1), 
+                    (x2, y2), 
+                    Config.BBOX_COLOR, 
+                    2
+                )
+                label_text = f'{label} {confidence:.2f}'
+                cv2.putText(
+                    annotated_image,
+                    label_text,
+                    (x1, y1 - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    Config.FONT_SCALE,
+                    Config.BBOX_COLOR,
+                    Config.FONT_THICKNESS
+                )
+            
+            return DetectionResult(True, annotated_image)
+        except Exception as e:
+            logger.error(f"Error during object detection: {e}")
+            return DetectionResult(False, error_message=str(e))
+
+class ImageProcessor:
+    """Class to handle image processing operations"""
+    def __init__(self, model: YOLOModel):
+        self.model = model
+    
+    def process_image(self, image: Union[Image.Image, str]) -> DetectionResult:
+        """Process image from various sources (PIL Image or URL)"""
+        try:
+            if isinstance(image, str):
+                image = self._load_image_from_url(image)
+            
+            if image is None:
+                return DetectionResult(False, error_message="Failed to load image")
+            
+            # Convert image to RGB if it has an alpha channel
+            if image.mode == 'RGBA':
+                image = image.convert('RGB')
+                
+            np_image = np.array(image)
+            return self.model.detect_objects(np_image)
+        except Exception as e:
+            logger.error(f"Error processing image: {e}")
+            return DetectionResult(False, error_message=str(e))
+    
+    def _load_image_from_url(self, url: str) -> Optional[Image.Image]:
+        """Load image from URL with support for base64"""
+        try:
+            if url.startswith('data:image'):
+                header, encoded = url.split(',', 1)
+                image_data = base64.b64decode(encoded)
+                return Image.open(BytesIO(image_data))
+            else:
+                response = requests.get(url)
+                response.raise_for_status()
+                return Image.open(BytesIO(response.content))
+        except Exception as e:
+            logger.error(f"Error loading image from URL: {e}")
+            return None
+
+class VideoProcessor:
+    """Class to handle video processing operations"""
+    def __init__(self, model: YOLOModel):
+        self.model = model
+        os.makedirs(Config.TEMP_DIR, exist_ok=True)
+    
+    def process_video(self, input_path: str) -> Tuple[bool, Optional[str]]:
+        """Process video file and return path to processed video"""
+        cap = None
+        writer = None
+        progress_bar = st.progress(0)
+        status_text = st.empty()
+        
+        try:
+            cap = cv2.VideoCapture(input_path)
+            if not cap.isOpened():
+                return False, "Cannot open video file"
+            
+            total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+            if total_frames <= 0:
+                return False, "Invalid video file"
+                
+            output_path = os.path.join(Config.TEMP_DIR, "processed_video.mp4")
+            writer = self._setup_video_writer(cap, output_path)
+            
+            frame_count = 0
+            while True:
+                ret, frame = cap.read()
+                if not ret:
+                    break
+                
+                frame_count += 1
+                progress = min(100, int(frame_count * 100 / total_frames))
+                progress_bar.progress(progress)
+                status_text.text(f"Processing frame {frame_count}/{total_frames}")
+                
+                result = self.model.detect_objects(frame)
+                if result.success:
+                    writer.write(result.image)
+            
+            return True, output_path
+        except Exception as e:
+            logger.error(f"Error processing video: {e}")
+            return False, str(e)
+        finally:
+            if cap is not None:
+                cap.release()
+            if writer is not None:
+                writer.release()
+            progress_bar.empty()
+            status_text.empty()
+    
+    def _setup_video_writer(self, cap: cv2.VideoCapture, output_path: str) -> cv2.VideoWriter:
+        """Set up video writer with input video properties"""
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        fourcc = cv2.VideoWriter_fourcc(*Config.VIDEO_OUTPUT_FORMAT)
+        return cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+def download_youtube_video(youtube_url: str) -> Optional[str]:
+    """Download YouTube video and return path to downloaded file"""
+    try:
+        temp_dir = tempfile.gettempdir()
+        output_path = os.path.join(temp_dir, 'downloaded_video.mp4')
+        ydl_opts = {
+            'format': 'best',
+            'outtmpl': output_path
+        }
+        with youtube_dl.YoutubeDL(ydl_opts) as ydl:
+            ydl.download([youtube_url])
+        return output_path
+    except Exception as e:
+        logger.error(f"Failed to retrieve video from YouTube: {e}")
+        return None
+
+def cleanup_temp_files():
+    """Clean up temporary files"""
+    try:
+        for file in os.listdir(Config.TEMP_DIR):
+            file_path = os.path.join(Config.TEMP_DIR, file)
+            try:
+                if os.path.isfile(file_path):
+                    os.unlink(file_path)
+            except Exception as e:
+                logger.error(f"Error deleting {file_path}: {e}")
+    except Exception as e:
+        logger.error(f"Error cleaning up temp directory: {e}")
+
+def validate_image(image: Image.Image) -> Tuple[bool, str]:
+    """Validate image format and properties"""
+    try:
+        # Check image mode
+        if image.mode not in ['RGB', 'RGBA']:
+            return False, f"Unsupported image mode: {image.mode}"
+        
+        # Check image size
+        max_dimension = 1920
+        width, height = image.size
+        if width > max_dimension or height > max_dimension:
+            return False, f"Image too large. Maximum dimension: {max_dimension}px"
+        
+        # Check if image is valid
+        image.verify()
+        return True, "Image is valid"
+    except Exception as e:
+        return False, str(e)
+
+def main():
+    """Main application function"""
+    st.title("MULTIMEDIA OBJECT DETECTION USING YOLO")
+    
+    # Model selection with description
+    st.subheader("Model Selection")
+    model_choice = st.selectbox(
+        "Select YOLO Model",
+        options=Config.AVAILABLE_MODELS,
+        index=Config.AVAILABLE_MODELS.index(Config.DEFAULT_MODEL),
+        format_func=lambda x: f"{x} - {Config.YOLO_MODELS[x]}"
+    )
+    
+    # Initialize model using session state
+    if 'model' not in st.session_state or st.session_state.get('model_choice') != model_choice:
+        try:
+            st.session_state.model = YOLOModel(model_choice)
+            st.session_state.model_choice = model_choice
+        except Exception as e:
+            st.error(f"Error loading model: {str(e)}")
+            return
+    
+    model = st.session_state.model
+    image_processor = ImageProcessor(model)
+    video_processor = VideoProcessor(model)
+    
+    # Display model capabilities
+    model_type = "Detection"
+    if "pose" in model_choice:
+        model_type = "Pose Estimation"
+        st.info("This model will detect and estimate human poses in the image/video.")
+    elif "seg" in model_choice:
+        model_type = "Instance Segmentation"
+        st.info("This model will perform instance segmentation, creating precise masks for detected objects.")
+    else:
+        st.info("This model will detect and classify objects with bounding boxes.")
+    
+    tabs = st.tabs(["Image Detection", "Video Detection"])
+    
+    with tabs[0]:
+        st.header("Image Detection")
+        input_choice = st.radio("Select Input Method", ["Upload", "URL"])
+        
+        if input_choice == "Upload":
+            uploaded_image = st.file_uploader(
+                "Upload Image", 
+                type=Config.ALLOWED_IMAGE_TYPES
+            )
+            if uploaded_image is not None:
+                image = Image.open(uploaded_image)
+                result = image_processor.process_image(image)
+                if result.success:
+                    st.image(result.image, caption="Processed Image", use_container_width=True)
+                else:
+                    st.error(result.error_message)
+        
+        elif input_choice == "URL":
+            image_url = st.text_input("Image URL")
+            if image_url:
+                result = image_processor.process_image(image_url)
+                if result.success:
+                    st.image(result.image, caption="Processed Image", use_container_width=True)
+                else:
+                    st.error(result.error_message)
+    
+    with tabs[1]:
+        st.header("Video Detection")
+        video_choice = st.radio("Select Input Method", ["Upload", "YouTube"])
+        
+        if video_choice == "Upload":
+            try:
+                uploaded_video = st.file_uploader(
+                    "Upload Local Video", 
+                    type=Config.ALLOWED_VIDEO_TYPES
+                )
+                if uploaded_video is not None:
+                    if uploaded_video.size > 200 * 1024 * 1024:  # 200MB limit
+                        st.error("Video file is too large. Please upload a file smaller than 200MB.")
+                        return
+                        
+                    input_video_path = os.path.join(Config.TEMP_DIR, uploaded_video.name)
+                    with open(input_video_path, "wb") as f:
+                        f.write(uploaded_video.read())
+                    
+                    try:
+                        success, result = video_processor.process_video(input_video_path)
+                        if success:
+                            st.video(result)
+                        else:
+                            st.error(f"Error processing video: {result}")
+                    finally:
+                        cleanup_temp_files()
+            except Exception as e:
+                st.error(f"An error occurred: {str(e)}")
+        
+        elif video_choice == "YouTube":
+            video_url = st.text_input("YouTube Video URL")
+            if video_url:
+                with st.spinner("Downloading video..."):
+                    input_video_path = download_youtube_video(video_url)
+                    if input_video_path:
+                        try:
+                            success, result = video_processor.process_video(input_video_path)
+                            if success:
+                                st.video(result)
+                            else:
+                                st.error(result)
+                        finally:
+                            cleanup_temp_files()
+                    else:
+                        st.error("Failed to download YouTube video")
+
+if __name__ == "__main__":
+    main()

config.py

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+from typing import List, Dict
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+class Config:
+    # Model configurations with descriptions
+    YOLO_MODELS = {
+        "yolov8n.pt": "YOLOv8 Nano - Fastest and smallest model, best for CPU/edge devices",
+        "yolov8s.pt": "YOLOv8 Small - Good balance of speed and accuracy",
+        "yolov8m.pt": "YOLOv8 Medium - Better accuracy, still reasonable speed",
+        "yolov8l.pt": "YOLOv8 Large - High accuracy, slower speed",
+        "yolov8x.pt": "YOLOv8 XLarge - Highest accuracy, slowest speed",
+        # Pose estimation models
+        "yolov8n-pose.pt": "YOLOv8 Nano Pose - Fast pose estimation",
+        "yolov8s-pose.pt": "YOLOv8 Small Pose - Balanced pose estimation",
+        "yolov8m-pose.pt": "YOLOv8 Medium Pose - Accurate pose estimation",
+        "yolov8l-pose.pt": "YOLOv8 Large Pose - High accuracy pose estimation",
+        "yolov8x-pose.pt": "YOLOv8 XLarge Pose - Most accurate pose estimation",
+        # Segmentation models
+        "yolov8n-seg.pt": "YOLOv8 Nano Segmentation - Fast instance segmentation",
+        "yolov8s-seg.pt": "YOLOv8 Small Segmentation - Balanced segmentation",
+        "yolov8m-seg.pt": "YOLOv8 Medium Segmentation - Accurate segmentation",
+        "yolov8l-seg.pt": "YOLOv8 Large Segmentation - High accuracy segmentation",
+        "yolov8x-seg.pt": "YOLOv8 XLarge Segmentation - Most accurate segmentation"
+    }
+    
+    AVAILABLE_MODELS: List[str] = list(YOLO_MODELS.keys())
+    DEFAULT_MODEL: str = os.getenv('DEFAULT_MODEL', 'yolov8s.pt')
+    
+    # File configurations
+    ALLOWED_IMAGE_TYPES: List[str] = ["jpg", "jpeg", "png"]
+    ALLOWED_VIDEO_TYPES: List[str] = ["mp4", "mov", "avi"]
+    
+    # Video processing
+    TEMP_DIR: str = os.getenv('TEMP_DIR', 'temp')
+    VIDEO_OUTPUT_FORMAT: str = os.getenv('VIDEO_OUTPUT_FORMAT', 'mp4v')
+    MAX_VIDEO_DURATION: int = int(os.getenv('MAX_VIDEO_DURATION', '300'))  # 5 minutes default
+    
+    # UI configurations
+    CONFIDENCE_THRESHOLD: float = float(os.getenv('CONFIDENCE_THRESHOLD', '0.25'))
+    BBOX_COLOR: tuple = tuple(map(int, os.getenv('BBOX_COLOR', '0,255,0').split(',')))
+    FONT_SCALE: float = float(os.getenv('FONT_SCALE', '0.5'))
+    FONT_THICKNESS: int = int(os.getenv('FONT_THICKNESS', '2'))
+    
+    # Cache settings
+    CACHE_DIR: str = os.getenv('CACHE_DIR', '.cache')
+    MAX_CACHE_SIZE: int = int(os.getenv('MAX_CACHE_SIZE', '1024'))  # MB
+    
+    @classmethod
+    def validate_config(cls) -> bool:
+        """Validate configuration settings"""
+        try:
+            # Validate model exists
+            if cls.DEFAULT_MODEL not in cls.AVAILABLE_MODELS:
+                raise ValueError(f"Invalid default model: {cls.DEFAULT_MODEL}")
+            
+            # Validate directories exist or can be created
+            os.makedirs(cls.TEMP_DIR, exist_ok=True)
+            os.makedirs(cls.CACHE_DIR, exist_ok=True)
+            
+            return True
+        except Exception as e:
+            print(f"Configuration validation failed: {str(e)}")
+            return False