app.py

import gradio as gr
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
import cv2, tempfile
import numpy as np
from PIL import Image

# Load YOLO model from HF Hub with token
# Replace 'your_token_here' with your actual HF token or use huggingface-cli login
model_path = hf_hub_download(
    repo_id="utkarsh-23/yolov8m-garbage-pothole-detector", 
    filename="best.pt",
    # token="your_token_here"  # Uncomment and add your token if needed
)
model = YOLO(model_path)

# Define class names
class_names = ['Container', 'Garbage', 'crocodile crack', 'longitudinal crack', 'pothole', 
               'HV-switch', 'crossarm', 'streetlight', 'traffic-light', 'transformer']

# Define department mapping
department_mapping = {
    'Container': 'Garbage',
    'Garbage': 'Garbage',
    'crocodile crack': 'Pothole',
    'longitudinal crack': 'Pothole', 
    'pothole': 'Pothole',
    'HV-switch': 'Streetlight',
    'crossarm': 'Streetlight',
    'streetlight': 'Streetlight',
    'traffic-light': 'Streetlight',
    'transformer': 'Streetlight'
}

# Image detection
def detect_image(image):
    if image is None:
        return None, "⚠️ Please upload an image first!"
    
    try:
        results = model(image)
        
        # Get detected classes and departments
        detected_objects = []
        detected_departments = set()
        
        if results[0].boxes is not None:
            for box in results[0].boxes:
                class_id = int(box.cls[0])
                confidence = float(box.conf[0])
                class_name = class_names[class_id] if class_id < len(class_names) else f"Class {class_id}"
                department = department_mapping.get(class_name, "Unknown")
                
                detected_objects.append(f"{class_name} ({confidence:.2f})")
                detected_departments.add(department)
        
        # Create classification text with emojis
        if detected_departments:
            if len(detected_departments) == 1:
                department = list(detected_departments)[0]
                dept_emoji = {"Garbage": "🗑️", "Pothole": "🕳️", "Streetlight": "💡"}.get(department, "📋")
                classification_text = f"{dept_emoji} **This image is classified under the {department} department**"
            else:
                departments_list = ", ".join(sorted(detected_departments))
                classification_text = f"📊 **This image is classified under multiple departments:** {departments_list}"
            
            # Add detailed detection info
            classification_text += "\n\n### 🔍 Detected Objects:\n"
            for obj in detected_objects:
                classification_text += f"• {obj}\n"
        else:
            classification_text = "❌ **No objects detected**\n\nPlease try with a different image containing garbage, potholes, or streetlight infrastructure."
        
        annotated_image = results[0].plot()
        return annotated_image, classification_text
        
    except Exception as e:
        return None, f"❌ **Error processing image:** {str(e)}"

# Video detection
def detect_video(video_path):
    if video_path is None:
        return None
    
    try:
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            return None
            
        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
        out_path = tempfile.mktemp(suffix=".mp4")
        
        # Get video properties
        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(out_path, fourcc, fps, (width, height))

        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            results = model(frame)
            annotated_frame = results[0].plot()
            out.write(annotated_frame)

        cap.release()
        out.release()
        return out_path
        
    except Exception as e:
        print(f"Error processing video: {e}")
        return None

# Custom CSS for better UI
custom_css = """
.gradio-container {
    max-width: 1200px !important;
    margin: auto !important;
}

.main-header {
    text-align: center;
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    color: white;
    padding: 2rem;
    border-radius: 10px;
    margin-bottom: 2rem;
    box-shadow: 0 4px 15px rgba(0,0,0,0.1);
}

.department-info {
    background: #f8f9fa;
    border-left: 4px solid #007bff;
    padding: 1rem;
    margin: 1rem 0;
    border-radius: 5px;
    color: #333 !important;
}

.department-info h3 {
    color: #2c3e50 !important;
    margin-bottom: 1rem !important;
    font-weight: 600 !important;
}

.department-info div {
    color: #333 !important;
}

.department-info strong {
    color: #2c3e50 !important;
    font-weight: 600 !important;
}

.upload-area {
    border: 2px dashed #007bff;
    border-radius: 10px;
    padding: 2rem;
    text-align: center;
    background: #f8f9fa;
    transition: all 0.3s ease;
    color: #333 !important;
}

.upload-area:hover {
    border-color: #0056b3;
    background: #e3f2fd;
}

/* Upload area text styling */
.upload-area .upload-text {
    color: #333 !important;
    font-weight: 500 !important;
}

/* Fix for file upload component text */
.file-upload {
    color: #333 !important;
}

.file-upload .upload-text {
    color: #333 !important;
}

/* Gradio file upload specific styling */
.gr-file-upload {
    color: #333 !important;
}

.gr-file-upload .upload-text,
.gr-file-upload .file-preview,
.gr-file-upload .file-name {
    color: #333 !important;
}

/* Additional upload component fixes */
[data-testid="upload-button"] {
    color: #333 !important;
}

.upload-container {
    color: #333 !important;
}

.upload-container * {
    color: #333 !important;
}

/* Specific targeting for upload text */
.svelte-1nausj1 {
    color: #333 !important;
}

.svelte-1nausj1 * {
    color: #333 !important;
}

.classification-result {
    background: #ffffff !important;
    border: 1px solid #e0e0e0 !important;
    border-radius: 8px !important;
    padding: 1.5rem !important;
    color: #333333 !important;
    font-size: 14px !important;
    line-height: 1.6 !important;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1) !important;
}

.classification-result h3 {
    color: #2c3e50 !important;
    margin-top: 1rem !important;
    margin-bottom: 0.5rem !important;
}

.classification-result p {
    color: #333333 !important;
    margin-bottom: 0.8rem !important;
}

.classification-result strong {
    color: #2c3e50 !important;
    font-weight: 600 !important;
}

.classification-result ul, .classification-result li {
    color: #444444 !important;
}

/* Fix for markdown content */
.markdown {
    background: #ffffff !important;
    color: #333333 !important;
}

.markdown h1, .markdown h2, .markdown h3, .markdown h4, .markdown h5, .markdown h6 {
    color: #2c3e50 !important;
}

.markdown p, .markdown li, .markdown span {
    color: #333333 !important;
}

.markdown strong {
    color: #2c3e50 !important;
}

footer {
    text-align: center;
    margin-top: 2rem;
    padding: 1rem;
    color: #666;
}

/* Additional text contrast fixes */
.block.svelte-90oupt {
    background: #ffffff !important;
}

.prose {
    color: #333333 !important;
}

.prose h1, .prose h2, .prose h3 {
    color: #2c3e50 !important;
}

.prose p, .prose li {
    color: #333333 !important;
}

/* Upload component text color fixes */
.image-container,
.video-container {
    color: #333 !important;
}

.image-container *,
.video-container * {
    color: #333 !important;
}

/* More specific upload text targeting */
div[data-testid*="upload"] {
    color: #333 !important;
}

div[data-testid*="upload"] * {
    color: #333 !important;
}

/* Force text visibility in upload areas */
.block.svelte-1t38q2d {
    color: #333 !important;
}

.block.svelte-1t38q2d * {
    color: #333 !important;
}

/* Additional upload text fixes */
.uploading,
.upload-instructions,
.drop-zone {
    color: #333 !important;
}

.uploading *,
.upload-instructions *,
.drop-zone * {
    color: #333 !important;
}
"""

# Header HTML
header_html = """
<div class="main-header">
    <h1>🔍 SAMADHAN </h1>
    <p>AI-Powered Classification for Urban Infrastructure Management</p>
    <div class="department-info">
        <h3 style="color: #2c3e50 !important; margin-bottom: 1rem;">📊 Detection Categories:</h3>
        <div style="display: flex; justify-content: center; gap: 2rem; margin-top: 1rem; flex-wrap: wrap;">
            <div style="color: #333 !important; font-weight: 500;"><strong style="color: #2c3e50 !important;">🗑️ Garbage Department:</strong> Container, Garbage</div>
            <div style="color: #333 !important; font-weight: 500;"><strong style="color: #2c3e50 !important;">🕳️ Pothole Department:</strong> Cracks, Potholes</div>
            <div style="color: #333 !important; font-weight: 500;"><strong style="color: #2c3e50 !important;">💡 Streetlight Department:</strong> Electrical Infrastructure</div>
        </div>
    </div>
</div>
"""

# Footer HTML
footer_html = """
<div style="text-align: center; margin-top: 2rem; padding: 1rem; color: #666;">
    <p>Built with ❤️ using YOLOv8 and Gradio | Powered by AI for Smart City Management</p>
</div>
"""

# Interfaces with enhanced UI
with gr.Blocks(css=custom_css, title="Infrastructure Detection System", theme=gr.themes.Soft()) as demo:
    gr.HTML(header_html)
    
    with gr.Tabs() as tabs:
        with gr.TabItem("📸 Image Detection", elem_id="image-tab"):
            with gr.Row():
                with gr.Column(scale=1):
                    image_input = gr.Image(
                        label="Upload Image",
                        type="numpy",
                        elem_classes="upload-area"
                    )
                    
                    gr.Examples(
                        examples=[],  # Add example image paths here if you have any
                        inputs=image_input,
                        label="Example Images"
                    )
                    
                    image_btn = gr.Button(
                        "🔍 Analyze Image", 
                        variant="primary", 
                        size="lg"
                    )
                    
                with gr.Column(scale=1):
                    image_output = gr.Image(
                        label="Detection Results",
                        type="numpy"
                    )
                    
                    classification_output = gr.Markdown(
                        label="Department Classification",
                        elem_classes="classification-result"
                    )
            
        with gr.TabItem("🎥 Video Detection", elem_id="video-tab"):
            with gr.Row():
                with gr.Column(scale=1):
                    video_input = gr.Video(
                        label="Upload Video",
                        elem_classes="upload-area"
                    )
                    
                    video_btn = gr.Button(
                        "🎬 Process Video", 
                        variant="primary", 
                        size="lg"
                    )
                    
                    gr.Markdown("""
                    ### 📝 Video Processing Notes:
                    - Supports common video formats (MP4, AVI, MOV)
                    - Processing time depends on video length
                    - Large videos may take several minutes
                    """)
                    
                with gr.Column(scale=1):
                    video_output = gr.Video(
                        label="Processed Video with Detections"
                    )
    
    # Event handlers
    image_btn.click(
        fn=detect_image,
        inputs=image_input,
        outputs=[image_output, classification_output],
        show_progress=True
    )
    
    video_btn.click(
        fn=detect_video,
        inputs=video_input,
        outputs=video_output,
        show_progress=True
    )
    
    # Auto-process when image is uploaded
    image_input.change(
        fn=detect_image,
        inputs=image_input,
        outputs=[image_output, classification_output],
        show_progress=True
    )
    
    gr.HTML(footer_html)

if __name__ == "__main__":
    print("🚀 Starting Infrastructure Detection System...")
    print("📊 Loading AI model...")
    demo.launch(
        share=False,
        inbrowser=True,
        show_error=True,
        favicon_path=None,
        app_kwargs={"docs_url": None}
    )