app.py

import gradio as gr
import cv2
import numpy as np
from groq import Groq
import time
from PIL import Image as PILImage
import io
import os
import base64
import random

def create_monitor_interface():
    api_key = os.getenv("GROQ_API_KEY")
    
    class SafetyMonitor:
        def __init__(self):
            self.client = Groq()
            self.model_name = "llama-3.2-90b-vision-preview"
            self.max_image_size = (800, 800)  # Increased size for better visibility
            self.colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)]
            
        def resize_image(self, image):
            height, width = image.shape[:2]
            aspect = width / height
            
            if width > height:
                new_width = min(self.max_image_size[0], width)
                new_height = int(new_width / aspect)
            else:
                new_height = min(self.max_image_size[1], height)
                new_width = int(new_height * aspect)
                
            return cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)

        def analyze_frame(self, frame: np.ndarray) -> str:
            if frame is None:
                return "No frame received"
                
            # Convert and resize image
            if len(frame.shape) == 2:
                frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
            elif len(frame.shape) == 3 and frame.shape[2] == 4:
                frame = cv2.cvtColor(frame, cv2.COLOR_RGBA2RGB)
            
            frame = self.resize_image(frame)
            frame_pil = PILImage.fromarray(frame)
            
            # Convert to base64 with minimal quality
            buffered = io.BytesIO()
            frame_pil.save(buffered, 
                         format="JPEG", 
                         quality=50, 
                         optimize=True)
            img_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
            image_url = f"data:image/jpeg;base64,{img_base64}"
            
            try:
                completion = self.client.chat.completions.create(
                    model=self.model_name,
                    messages=[
                        {
                            "role": "user",
                            "content": [
                                {
                                    "type": "text",
                                    "text": """Analyze this workplace image and describe each safety concern in this format:
                                    - <location>Description</location>
                                    Use one line per issue, starting with a dash and location in tags."""
                                },
                                {
                                    "type": "image_url",
                                    "image_url": {
                                        "url": image_url
                                    }
                                }
                            ]
                        },
                        {
                            "role": "assistant",
                            "content": ""
                        }
                    ],
                    temperature=0.1,
                    max_tokens=500,
                    top_p=1,
                    stream=False,
                    stop=None
                )
                return completion.choices[0].message.content
            except Exception as e:
                print(f"Detailed error: {str(e)}")
                return f"Analysis Error: {str(e)}"

        def draw_observations(self, image, observations):
            height, width = image.shape[:2]
            font = cv2.FONT_HERSHEY_SIMPLEX
            font_scale = 0.5
            thickness = 2
            
            # Generate random positions for each observation
            for idx, obs in enumerate(observations):
                color = self.colors[idx % len(self.colors)]
                
                # Generate random box position
                box_width = width // 3
                box_height = height // 3
                x = random.randint(0, width - box_width)
                y = random.randint(0, height - box_height)
                
                # Draw rectangle
                cv2.rectangle(image, (x, y), (x + box_width, y + box_height), color, 2)
                
                # Add label with background
                label = obs[:40] + "..." if len(obs) > 40 else obs
                label_size = cv2.getTextSize(label, font, font_scale, thickness)[0]
                cv2.rectangle(image, (x, y - 20), (x + label_size[0], y), color, -1)
                cv2.putText(image, label, (x, y - 5), font, font_scale, (255, 255, 255), thickness)
            
            return image

        def process_frame(self, frame: np.ndarray) -> tuple[np.ndarray, str]:
            if frame is None:
                return None, "No image provided"
                
            analysis = self.analyze_frame(frame)
            display_frame = self.resize_image(frame.copy())
            
            # Parse observations from the analysis
            observations = []
            for line in analysis.split('\n'):
                line = line.strip()
                if line.startswith('-'):
                    # Extract text between <location> tags if present
                    if '<location>' in line and '</location>' in line:
                        start = line.find('<location>') + len('<location>')
                        end = line.find('</location>')
                        observation = line[end + len('</location>'):].strip()
                    else:
                        observation = line[1:].strip()  # Remove the dash
                    if observation:
                        observations.append(observation)
            
            # Draw observations on the image
            annotated_frame = self.draw_observations(display_frame, observations)
            
            return annotated_frame, analysis

    # Create the main interface
    monitor = SafetyMonitor()
    
    with gr.Blocks() as demo:
        gr.Markdown("# Safety Analysis System powered by Llama 3.2 90b vision")
        
        with gr.Row():
            input_image = gr.Image(label="Upload Image")
            output_image = gr.Image(label="Annotated Results")
        
        analysis_text = gr.Textbox(label="Detailed Analysis", lines=5)
            
        def analyze_image(image):
            if image is None:
                return None, "No image provided"
            try:
                processed_frame, analysis = monitor.process_frame(image)
                return processed_frame, analysis
            except Exception as e:
                print(f"Processing error: {str(e)}")
                return None, f"Error processing image: {str(e)}"
            
        input_image.change(
            fn=analyze_image,
            inputs=input_image,
            outputs=[output_image, analysis_text]
        )

    return demo

demo = create_monitor_interface()
demo.launch()