app.py

import os
import base64
import gradio as gr
from PIL import Image, ImageOps
import io
import json
from groq import Groq
import logging
import cv2
import numpy as np
import traceback

# Set up logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

# Load environment variables
GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
if not GROQ_API_KEY:
    logger.error("GROQ_API_KEY is not set in environment variables")
    raise ValueError("GROQ_API_KEY is not set")

# Initialize Groq client
client = Groq(api_key=GROQ_API_KEY)

def encode_image(image):
    try:
        if isinstance(image, str):  # If image is a file path
            with open(image, "rb") as image_file:
                return base64.b64encode(image_file.read()).decode('utf-8')
        elif isinstance(image, Image.Image):  # If image is a PIL Image
            buffered = io.BytesIO()
            image.save(buffered, format="PNG")
            return base64.b64encode(buffered.getvalue()).decode('utf-8')
        elif isinstance(image, np.ndarray):  # If image is a numpy array (from video)
            is_success, buffer = cv2.imencode(".png", image)
            if is_success:
                return base64.b64encode(buffer).decode('utf-8')
        else:
            raise ValueError(f"Unsupported image type: {type(image)}")
    except Exception as e:
        logger.error(f"Error encoding image: {str(e)}")
        raise

def resize_image(image, max_size=(800, 800)):
    """Resize image to avoid exceeding the API size limits."""
    try:
        image.thumbnail(max_size, Image.Resampling.LANCZOS)  # Use LANCZOS resampling for better quality
        return image
    except Exception as e:
        logger.error(f"Error resizing image: {str(e)}")
        raise
        
def extract_frames_from_video(video, frame_points=[0, 0.5, 1], max_size=(800, 800)):
    """Extract key frames from the video at specific time points."""
    cap = cv2.VideoCapture(video)
    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    fps = int(cap.get(cv2.CAP_PROP_FPS))
    duration = frame_count / fps

    frames = []
    for time_point in frame_points:
        cap.set(cv2.CAP_PROP_POS_MSEC, time_point * duration * 1000)
        ret, frame = cap.read()
        if ret:
            resized_frame = cv2.resize(frame, max_size)
            frames.append(resized_frame)
    cap.release()
    return frames

def analyze_construction_image(images=None, video=None):
    if not images and video is None:
        logger.warning("No images or video provided")
        return [("No input", "Error: Please upload images or a video for analysis.")]

    try:
        logger.info("Starting analysis")
        results = []

        if images:
            for i, image_file in enumerate(images):
                image = Image.open(image_file.name)  # For image uploads, we use image_file.name
                resized_image = resize_image(image)  # Resize image before processing
                image_data_url = f"data:image/png;base64,{encode_image(resized_image)}"
                messages = [
                    {
                        "role": "user",
                        "content": [
                            {
                                "type": "text",
                                "text": f"Analyze this construction site image (Image {i+1}/{len(images)}). Identify any safety issues or hazards, categorize them, provide a detailed description, and suggest steps to resolve them."
                            },
                            {
                                "type": "image_url",
                                "image_url": {
                                    "url": image_data_url
                                }
                            }
                        ]
                    }
                ]
                completion = client.chat.completions.create(
                    model="llama-3.2-90b-vision-preview",
                    messages=messages,
                    temperature=0.7,
                    max_tokens=1000,
                    top_p=1,
                    stream=False,
                    stop=None
                )
                result = completion.choices[0].message.content
                results.append((f"Image {i+1} analysis", result))

        if video:
            frames = extract_frames_from_video(video)  # Use video directly, as it's a file path
            for i, frame in enumerate(frames):
                image_data_url = f"data:image/png;base64,{encode_image(frame)}"
                messages = [
                    {
                        "role": "user",
                        "content": [
                            {
                                "type": "text",
                                "text": f"Analyze this frame from a construction site video (Frame {i+1}/5). Identify any safety issues or hazards, categorize them, provide a detailed description, and suggest steps to resolve them."
                            },
                            {
                                "type": "image_url",
                                "image_url": {
                                    "url": image_data_url
                                }
                            }
                        ]
                    }
                ]
                completion = client.chat.completions.create(
                    model="llama-3.2-90b-vision-preview",
                    messages=messages,
                    temperature=0.7,
                    max_tokens=1000,
                    top_p=1,
                    stream=False,
                    stop=None
                )
                result = completion.choices[0].message.content
                results.append((f"Video frame {i+1} analysis", result))

        logger.info("Analysis completed successfully")
        return results
    except Exception as e:
        logger.error(f"Error during analysis: {str(e)}")
        logger.error(traceback.format_exc())  # Log the full traceback for debugging
        return [("Analysis error", f"Error during analysis: {str(e)}")]
        
def chat_about_image(message, chat_history):
    try:
        # Prepare the conversation history for the API
        messages = [
            {"role": "system", "content": "You are an AI assistant specialized in analyzing construction site images and answering questions about them. Use the information from the initial analysis to answer user queries."},
        ]
        
        # Add chat history to messages
        for human, ai in chat_history:
            if human:
                messages.append({"role": "user", "content": human})
            if ai:
                messages.append({"role": "assistant", "content": ai})
        
        # Add the new user message
        messages.append({"role": "user", "content": message})
        
        # Make API call
        completion = client.chat.completions.create(
            model="llama-3.2-90b-vision-preview",
            messages=messages,
            temperature=0.7,
            max_tokens=500,
            top_p=1,
            stream=False,
            stop=None
        )
        
        response = completion.choices[0].message.content
        chat_history.append((message, response))
        
        return "", chat_history
    except Exception as e:
        logger.error(f"Error during chat: {str(e)}")
        return "", chat_history + [(message, f"Error: {str(e)}")]


# Custom CSS for improved styling
custom_css = """
.container { max-width: 1200px; margin: auto; padding-top: 1.5rem; }
.header { text-align: center; margin-bottom: 1rem; }
.header h1 { color: #2c3e50; font-size: 2.5rem; }
.subheader { 
    color: #34495e; 
    font-size: 1rem; 
    line-height: 1.2; 
    margin-bottom: 1.5rem; 
    text-align: center; 
    padding: 0 15px;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
}
.image-container { border: 2px dashed #3498db; border-radius: 10px; padding: 1rem; text-align: center; margin-bottom: 1rem; }
.analyze-button { background-color: #2ecc71 !important; color: white !important; width: 100%; }
.clear-button { background-color: #e74c3c !important; color: white !important; width: 100px !important; }
.chatbot { border: 1px solid #bdc3c7; border-radius: 10px; padding: 1rem; height: 500px; overflow-y: auto; }
.chat-input { border: 1px solid #bdc3c7; border-radius: 5px; padding: 0.5rem; width: 100%; }
.groq-badge { position: fixed; bottom: 10px; right: 10px; background-color: #f39c12; color: white; padding: 5px 10px; border-radius: 5px; font-weight: bold; }
.chat-container { display: flex; flex-direction: column; height: 100%; }
.input-row { display: flex; align-items: center; margin-top: 10px; justify-content: space-between; }
.input-row > div:first-child { flex-grow: 1; margin-right: 10px; }
"""

# Create the Gradio interface
with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as iface:
    gr.HTML(
        """
        <div class="container">
            <div class="header">
                <h1>🏗️ Construction Site Safety Analyzer</h1>
            </div>
            <p class="subheader">Enhance workplace safety and compliance with AI-powered image and video analysis using Llama 3.2 90B Vision and expert chat assistance.</p>
        </div>
        """
    )
    
    # First row: Upload Image
    with gr.Row():
        image_input = gr.File(label="Upload Construction Site Images", file_count="multiple", type="filepath", elem_classes="image-container")

    # Second row: Upload Video
    with gr.Row():
        video_input = gr.Video(label="Upload Construction Site Video", elem_classes="image-container")
    
    # Third row: Analyze Safety Hazards Button
    with gr.Row():
        analyze_button = gr.Button("🔍 Analyze Safety Hazards", elem_classes="analyze-button")
    
    # Fourth row: Chat Interface (Safety Analysis Results)
    with gr.Row():
        chatbot = gr.Chatbot(label="Safety Analysis Results and Expert Chat", elem_classes="chatbot")
    
    # Fifth row: Question Bar
    with gr.Row():
        msg = gr.Textbox(
            label="Ask about safety measures or regulations",
            placeholder="E.g., 'What OSHA guidelines apply to this hazard?'",
            show_label=False,
            elem_classes="chat-input"
        )

    # Sixth row: Clear Chat Button
    with gr.Row():
        clear = gr.Button("🗑️ Clear", elem_classes="clear-button")

    def update_chat(history, new_messages):
        history = history or []
        history.extend(new_messages)
        return history

    analyze_button.click(
        analyze_construction_image,
        inputs=[image_input, video_input],
        outputs=[chatbot],
        postprocess=lambda x: update_chat(chatbot.value, x)
    )

    msg.submit(chat_about_image, [msg, chatbot], [msg, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

    gr.HTML(
        """
        <div class="groq-badge">Powered by Groq</div>
        """
    )

# Launch the app
if __name__ == "__main__":
    iface.launch(debug=True)