app.py

import streamlit as st
import cv2
import numpy as np
import os
import tempfile
import librosa
import librosa.display
import matplotlib.pyplot as plt
import tempfile
import librosa
import librosa.display
import matplotlib.pyplot as plt
from PIL import Image
import torch

# Import deforestation modules
from prediction_engine import load_onnx_model

# Import deforestation modules
from prediction_engine import load_onnx_model
from utils.helpers import calculate_deforestation_metrics, create_overlay

# Import audio classification modules
from utils.audio_processing import preprocess_audio
from utils.audio_model import load_audio_model, predict_audio, class_names

# Import YOLO detection modules
from utils.onnx_inference import YOLOv11

# Ensure torch classes path is initialized to avoid warnings
torch.classes.__path__ = []

# Set page config
st.set_page_config(
    page_title="Nature Nexus - Forest Surveillance",
    page_icon="🌳",
    layout="wide",
    initial_sidebar_state="expanded"
)


# Constants
DEFOREST_MODEL_INPUT_SIZE = 256
AUDIO_MODEL_PATH = "models/best_model.pth"
YOLO_MODEL_PATH = "models/best_model.onnx"

# Initialize session state for navigation
if 'current_service' not in st.session_state:
    st.session_state.current_service = 'deforestation'
if 'audio_input_method' not in st.session_state:
    st.session_state.audio_input_method = 'upload'
if 'detection_input_method' not in st.session_state:
    st.session_state.detection_input_method = 'image'

# Sidebar for navigation
with st.sidebar:
    st.title("Nature Nexus")
    st.subheader("Forest Surveillance System")
    
    selected_service = st.radio(
        "Select Service:",
        ["Deforestation Detection", "Forest Audio Surveillance", "Object Detection"]
    )
    
    if selected_service == "Deforestation Detection":
        st.session_state.current_service = 'deforestation'
    elif selected_service == "Forest Audio Surveillance":
        st.session_state.current_service = 'audio'
    else:
        st.session_state.current_service = 'detection'
    
    st.markdown("---")
    
    # Service-specific sidebar content
    if st.session_state.current_service == 'deforestation':
        st.info(
            """
            **Deforestation Detection**
            
            Upload satellite or aerial images to detect areas of deforestation.
            """
        )
    elif st.session_state.current_service == 'audio':
        st.info(
            """
            **Forest Audio Surveillance**
            
            Detect unusual human-related sounds in forested regions.
            """
        )
        
        # Audio service specific controls
        st.subheader("Audio Configuration")
        audio_input_method = st.radio(
            "Select Input Method:",
            ("Upload Audio", "Record Audio"),
            index=0 if st.session_state.audio_input_method == 'upload' else 1
        )
        st.session_state.audio_input_method = 'upload' if audio_input_method == "Upload Audio" else 'record'
        
        # Audio class information
        st.markdown("**Detection Classes:**")
        
        # Group classes by category
        human_sounds = ['footsteps', 'coughing', 'laughing', 'breathing', 
                       'drinking_sipping', 'snoring', 'sneezing']
        tool_sounds = ['chainsaw', 'hand_saw']
        vehicle_sounds = ['car_horn', 'engine', 'siren']
        other_sounds = ['crackling_fire', 'fireworks']
        
        st.markdown("👤 **Human Sounds:** " + ", ".join([s.capitalize() for s in human_sounds]))
        st.markdown("🔨 **Tool Sounds:** " + ", ".join([s.capitalize() for s in tool_sounds]))
        st.markdown("🚗 **Vehicle Sounds:** " + ", ".join([s.capitalize() for s in vehicle_sounds]))
        st.markdown("💥 **Other Sounds:** " + ", ".join([s.capitalize() for s in other_sounds]))
    else:  # Object Detection
        st.info(
            """
            **Object Detection**
            
            Detect trespassers, vehicles, fires, and other objects in forest surveillance footage.
            """
        )
        
        # Detection service specific controls
        st.subheader("Detection Configuration")
        detection_input_method = st.radio(
            "Select Input Method:",
            ("Image", "Video", "Camera"),
            index=0 if st.session_state.detection_input_method == 'image' else 
                  (1 if st.session_state.detection_input_method == 'video' else 2)
        )
        
        if detection_input_method == "Image":
            st.session_state.detection_input_method = 'image'
        elif detection_input_method == "Video":
            st.session_state.detection_input_method = 'video'
        else:
            st.session_state.detection_input_method = 'camera'
            
        # Detection threshold controls
        st.subheader("Detection Settings")
        confidence = st.slider("Confidence Threshold", 0.0, 1.0, 0.5)
        iou_thres = st.slider("IoU Threshold", 0.0, 1.0, 0.5)
        
        # Detection class information
        st.markdown("**Detection Classes:**")
        st.markdown("🚴 **Bike/Bicycle**")
        st.markdown("🚚 **Bus/Truck**")
        st.markdown("🚗 **Car**")
        st.markdown("🔥 **Fire**")
        st.markdown("👤 **Human**")
        st.markdown("💨 **Smoke**")

# Load deforestation model
@st.cache_resource
def load_cached_deforestation_model():
    model_path = "models/deforestation_model.onnx"
    return load_onnx_model(model_path, input_size=DEFOREST_MODEL_INPUT_SIZE)

# Load audio model
@st.cache_resource
def load_cached_audio_model():
    return load_audio_model(AUDIO_MODEL_PATH)

@st.cache_resource
def load_cached_yolo_model():
    return YOLOv11(YOLO_MODEL_PATH)

# Process image for deforestation detection
def process_image(model, image):
    """Process a single image and return results"""
    # Save original image dimensions for display
    orig_height, orig_width = image.shape[:2]

    # Make prediction
    mask = model.predict(image)

    # Resize mask back to original dimensions for display
    display_mask = cv2.resize(mask, (orig_width, orig_height))

    # Create binary mask for visualization
    binary_mask = (display_mask > 0.5).astype(np.uint8) * 255

    # Create colored overlay
    overlay = create_overlay(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), display_mask)

    # Calculate metrics
    metrics = calculate_deforestation_metrics(mask)

    return binary_mask, overlay, metrics

# Visualize audio for audio classification
def visualize_audio(audio_path):
    y, sr = librosa.load(audio_path, sr=16000)
    duration = len(y) / sr
    
    fig, ax = plt.subplots(2, 1, figsize=(10, 6))
    
    # Waveform plot
    librosa.display.waveshow(y, sr=sr, ax=ax[0])
    ax[0].set_title('Audio Waveform')
    ax[0].set_xlabel('Time (s)')
    ax[0].set_ylabel('Amplitude')
    
    # Spectrogram plot
    S = librosa.feature.melspectrogram(y=y, sr=sr)
    S_db = librosa.power_to_db(S, ref=np.max)
    img = librosa.display.specshow(S_db, sr=sr, x_axis='time', y_axis='mel', ax=ax[1])
    fig.colorbar(img, ax=ax[1], format='%+2.0f dB')
    ax[1].set_title('Mel Spectrogram')
    
    plt.tight_layout()
    st.pyplot(fig)
    
    return y, sr, duration

# Process audio for classification
def process_audio(audio_file):
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
        tmp_file.write(audio_file.read() if hasattr(audio_file, 'read') else audio_file)
        audio_path = tmp_file.name
    
    try:
        # Load audio model
        audio_model = load_cached_audio_model()
        
        # Visualize audio
        with st.spinner('Analyzing audio...'):
            y, sr, duration = visualize_audio(audio_path)
            st.caption(f"Audio duration: {duration:.2f} seconds")
        
        # Make prediction
        with st.spinner('Making prediction...'):
            class_name, confidence = predict_audio(audio_path, audio_model)
        
        # Display results
        st.subheader("Detection Results")
        
        col1, col2 = st.columns(2)
        with col1:
            st.metric("Detected Sound", class_name.replace('_', ' ').title())
        with col2:
            st.metric("Confidence", f"{confidence*100:.2f}%")
        
        # Show alerts based on class
        human_sounds = ['footsteps', 'coughing', 'laughing', 'breathing', 
                      'drinking_sipping', 'snoring', 'sneezing']
        tool_sounds = ['chainsaw', 'hand_saw']
        
        if class_name in human_sounds:
            st.warning("""
            ⚠️ **Human Activity Detected!**
            Potential human presence in the monitored area.
            """)
        elif class_name in tool_sounds:
            st.error("""
            🚨 **ALERT: Human Tool Detected!**
            Potential illegal logging or activity detected. Consider immediate verification.
            """)
        elif class_name in ['car_horn', 'engine', 'siren']:
            st.warning("""
            ⚠️ **Vehicle Detected!**
            Vehicle sounds detected in the monitored area.
            """)
        elif class_name == 'fireworks':
            st.error("""
            🚨 **ALERT: Fireworks Detected!**
            Potential fire hazard and disturbance to wildlife. Immediate verification required.
            """)
        elif class_name == 'crackling_fire':
            st.error("""
            🚨 **ALERT: Fire Detected!**
            Potential wildfire detected. Immediate verification required.
            """)
        else:
            st.success("✅ Environmental sound detected - no immediate threat")
            
    except Exception as e:
        st.error(f"Error processing audio: {str(e)}")
        st.exception(e)
    finally:
        # Clean up temp file
        try:
            os.unlink(audio_path)
        except:
            pass

# Deforestation detection UI
def show_deforestation_detection():
    # App title and description
    st.title("🌳 Deforestation Detection")
    st.markdown(
        """
        This service detects areas of deforestation in satellite or aerial images of forests.
        Upload an image to get started!
        """
    )

    # Model info
    st.info(
        f"⚙️ Model optimized for {DEFOREST_MODEL_INPUT_SIZE}x{DEFOREST_MODEL_INPUT_SIZE} pixel images using ONNX runtime"
    )

    # Load model
    try:
        model = load_cached_deforestation_model()
    except Exception as e:
        st.error(f"Error loading model: {e}")
        st.info(
            "Make sure you have converted your PyTorch model to ONNX format using the utils/onnx_converter.py script."
        )
        st.code(
            "python -m utils.onnx_converter models/best_model_100.pth models/deforestation_model.onnx"
        )
        return

    # File uploader for images
    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

    if uploaded_file is not None:
        # Load image
        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
        image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)

        # Display original image
        st.subheader("Original Image")
        st.image(
            cv2.cvtColor(image, cv2.COLOR_BGR2RGB),
            caption="Uploaded Image",
            use_container_width=True,
        )

        # Add a spinner while processing
        with st.spinner("Processing..."):
            try:
                # Process image
                binary_mask, overlay, metrics = process_image(model, image)

                # Display results in columns
                col1, col2 = st.columns(2)

                with col1:
                    st.subheader("Segmentation Result")
                    st.image(
                        binary_mask,
                        caption="Forest Areas (White)",
                        use_container_width=True,
                    )

                with col2:
                    st.subheader("Overlay Visualization")
                    st.image(
                        overlay,
                        caption="Green: Forest, Brown: Deforested",
                        use_container_width=True,
                    )

                # Display metrics
                st.subheader("Deforestation Analysis")

                # Create metrics cards
                metrics_col1, metrics_col2, metrics_col3 = st.columns(3)

                with metrics_col1:
                    st.metric(
                        label="Forest Coverage",
                        value=f"{metrics['forest_percentage']:.1f}%",
                    )

                with metrics_col2:
                    st.metric(
                        label="Deforested Area",
                        value=f"{metrics['deforested_percentage']:.1f}%",
                    )

                with metrics_col3:
                    st.metric(
                        label="Deforestation Level",
                        value=metrics["deforestation_level"],
                    )

            except Exception as e:
                st.error(f"Error during processing: {e}")

# Audio classification UI
def show_audio_classification():
    # App title and description
    st.title("🎧 Forest Audio Surveillance")
    st.markdown("""
    Detect unusual human-related sounds in forested regions to prevent illegal activities.
    Supported sounds: {}
    """.format(", ".join(class_names)))
    
    if st.session_state.audio_input_method == 'upload':
        st.header("Upload Audio File")
        
        sample_col, upload_col = st.columns(2)
        with sample_col:
            st.info("Upload a WAV, MP3 or OGG file with forest sounds")
            st.markdown("""
            **Tips for best results:**
            - Use audio with minimal background noise
            - Ensure the sound of interest is clear
            - 2-3 second clips work best
            """)
        
        with upload_col:
            audio_file = st.file_uploader(
                "Choose an audio file",
                type=["wav", "mp3", "ogg"],
                help="Supported formats: WAV, MP3, OGG"
            )
            
        if audio_file:
            st.success("File uploaded successfully!")
            with st.expander("Audio Preview", expanded=True):
                st.audio(audio_file)
            process_audio(audio_file)

    else:  # Record mode
        st.header("Record Live Audio")
        
        st.info("""
        Click the microphone button below to record a sound for analysis.  
        **Note:** Please ensure your browser has permission to access your microphone.  
        When prompted, click "Allow" to enable recording.
        """)
        
        recorded_audio = st.audio_input(
            label="Record a sound",
            key="audio_recorder",
            help="Click to record forest sounds for analysis",
            label_visibility="visible"
        )
        
        if recorded_audio:
            st.success("Audio recorded successfully!")
            with st.expander("Recorded Audio", expanded=True):
                st.audio(recorded_audio)
            process_audio(recorded_audio)
        else:
            st.write("Waiting for recording...")

# Object Detection UI
def show_object_detection():
    # App title and description
    st.title("🔍 Forest Object Detection")
    st.markdown(
        """
        Detect trespassers, vehicles, fires, and other objects in forest surveillance footage.
        Choose an input method to begin detection.
        """
    )

    # Model info
    st.info("⚙️ Object detection model optimized with ONNX runtime for faster inference")

    # Load model
    try:
        model = load_cached_yolo_model()
        # Update model confidence and IoU thresholds from sidebar
        confidence = st.session_state.get('confidence', 0.5)
        iou_thres = st.session_state.get('iou_thres', 0.5)
        model.conf_thres = confidence
        model.iou_thres = iou_thres
    except Exception as e:
        st.error(f"Error loading model: {e}")
        st.info(
            "Make sure you have the YOLO ONNX model file available at models/best_model.onnx"
        )
        return

    # Input method based selection
    if st.session_state.detection_input_method == 'image':
        # Image upload
        img_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
        if img_file is not None:
            # Load image
            file_bytes = np.asarray(bytearray(img_file.read()), dtype=np.uint8)
            image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
            if image is not None:
                # Display original image
                st.subheader("Original Image")
                st.image(
                    cv2.cvtColor(image, cv2.COLOR_BGR2RGB),
                    caption="Uploaded Image",
                    use_container_width=True,
                )
                
                # Process with detection model
                with st.spinner("Processing image..."):
                    try:
                        detections = model.detect(image)
                        result_image = model.draw_detections(image.copy(), detections)
                        
                        # Display results
                        st.subheader("Detection Results")
                        st.image(
                            cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB),
                            caption="Detected Objects",
                            use_container_width=True,
                        )
                        
                        # Display detection statistics
                        st.subheader("Detection Statistics")
                        
                        # Count detections by class
                        class_counts = {}
                        for det in detections:
                            class_name = det['class']
                            if class_name in class_counts:
                                class_counts[class_name] += 1
                            else:
                                class_counts[class_name] = 1
                        
                        # Display counts with emojis
                        cols = st.columns(3)
                        col_idx = 0
                        
                        for class_name, count in class_counts.items():
                            emoji = "👤" if class_name == "human" else (
                                   "🔥" if class_name == "fire" else (
                                   "💨" if class_name == "smoke" else (
                                   "🚗" if class_name == "car" else (
                                   "🚴" if class_name == "bike-bicycle" else "🚚"))))
                            
                            with cols[col_idx % 3]:
                                st.metric(f"{emoji} {class_name.capitalize()}", count)
                            col_idx += 1
                        
                        # Check for priority threats
                        if "fire" in class_counts or "smoke" in class_counts:
                            st.error("🚨 **ALERT: Fire Detected!** Potential forest fire detected. Immediate action required.")
                        
                        if "human" in class_counts or "car" in class_counts or "bike-bicycle" in class_counts or "bus-truck" in class_counts:
                            st.warning("⚠️ **Trespassers Detected!** Unauthorized entry detected in monitored area.")
                            
                    except Exception as e:
                        st.error(f"Error during detection: {e}")
                        st.exception(e)
    
    elif st.session_state.detection_input_method == 'video':
        # Video upload
        video_file = st.file_uploader("Upload Video", type=["mp4", "avi", "mov"])
        if video_file is not None:
            # Save uploaded video to temp file
            with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tfile:
                tfile.write(video_file.read())
                temp_video_path = tfile.name
            
            # Display video upload success
            st.success("Video uploaded successfully!")
            
            # Process video button
            if st.button("Process Video"):
                with st.spinner("Processing video... This may take a while."):
                    try:
                        # Open video file
                        cap = cv2.VideoCapture(temp_video_path)
                        
                        # Create video writer for output
                        output_path = "output_video.mp4"
                        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
                        fps = cap.get(cv2.CAP_PROP_FPS)
                        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
                        
                        # Create placeholder for video frames
                        video_placeholder = st.empty()
                        status_text = st.empty()
                        
                        # Process frames
                        frame_count = 0
                        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
                        
                        while cap.isOpened():
                            ret, frame = cap.read()
                            if not ret:
                                break
                                
                            # Process every 5th frame for speed
                            if frame_count % 5 == 0:
                                detections = model.detect(frame)
                                result_frame = model.draw_detections(frame.copy(), detections)
                                
                                # Update preview
                                if frame_count % 15 == 0:  # Update display less frequently
                                    video_placeholder.image(
                                        cv2.cvtColor(result_frame, cv2.COLOR_BGR2RGB),
                                        caption="Processing Video",
                                        use_container_width=True
                                    )
                                    progress = min(100, int((frame_count / total_frames) * 100))
                                    status_text.text(f"Processing: {progress}% complete")
                            else:
                                result_frame = frame  # Skip detection on some frames
                                
                            # Write frame to output video
                            out.write(result_frame)
                            frame_count += 1
                            
                        # Release resources
                        cap.release()
                        out.release()
                        
                        # Display completion message
                        st.success("Video processing complete!")
                        
                        # Provide download button for processed video
                        with open(output_path, "rb") as file:
                            st.download_button(
                                label="Download Processed Video",
                                data=file,
                                file_name="forest_surveillance_results.mp4",
                                mime="video/mp4"
                            )
                            
                    except Exception as e:
                        st.error(f"Error processing video: {e}")
                        st.exception(e)
                    finally:
                        # Clean up temp file
                        try:
                            os.unlink(temp_video_path)
                        except:
                            pass
    
    else:  # Camera mode
        # Live camera feed
        st.subheader("Live Camera Detection")
        st.info("Use your webcam to detect objects in real-time")
        
        cam = st.camera_input("Camera Feed")
        
        if cam:
            # Process camera input
            with st.spinner("Processing image..."):
                try:
                    # Convert image
                    image = cv2.imdecode(np.frombuffer(cam.getvalue(), np.uint8), cv2.IMREAD_COLOR)
                    
                    # Run detection
                    detections = model.detect(image)
                    result_image = model.draw_detections(image.copy(), detections)
                    
                    # Display results
                    st.image(
                        cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB),
                        caption="Detection Results",
                        use_container_width=True
                    )
                    
                    # Show detection summary
                    if detections:
                        # Count detections by class
                        class_counts = {}
                        for det in detections:
                            class_name = det['class']
                            if class_name in class_counts:
                                class_counts[class_name] += 1
                            else:
                                class_counts[class_name] = 1
                        
                        # Display as metrics
                        st.subheader("Detection Summary")
                        cols = st.columns(3)
                        for i, (class_name, count) in enumerate(class_counts.items()):
                            with cols[i % 3]:
                                st.metric(class_name.capitalize(), count)
                        
                        # Check for priority threats
                        if "fire" in class_counts or "smoke" in class_counts:
                            st.error("🚨 **ALERT: Fire Detected!** Potential forest fire detected.")
                        
                        if "human" in class_counts:
                            st.warning("⚠️ **Trespasser Detected!** Human presence detected.")
                    else:
                        st.info("No objects detected in frame")
                        
                except Exception as e:
                    st.error(f"Error processing camera feed: {e}")

# Main function
def main():
    # Check which service is selected and render appropriate UI
    if st.session_state.current_service == 'deforestation':
        show_deforestation_detection()
    elif st.session_state.current_service == 'audio':
        show_audio_classification()
    else:  # 'detection'
        show_object_detection()
    
    # Footer
    st.markdown("---")
    st.markdown("""
    <div style="text-align: center; padding: 10px;">
        <p>Nature Nexus - Forest Surveillance System | 🌳 Protect Natural Ecosystems</p>
        <p><small>Built with Streamlit and PyTorch</small></p>
    </div>
    """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()