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
from datetime import datetime
# 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 = []
instruction = ("You are an AI assistant specialized in analyzing images for safety issues. "
"Your task is first to explain what you see in the image and determine if the image shows a construction site. "
"If it does, identify any safety issues or hazards, categorize them, and provide a detailed description, "
"and suggest steps to resolve them. If it's not a construction site, simply state that")
if images:
for i, image_file in enumerate(images):
image = Image.open(image_file.name)
resized_image = resize_image(image)
image_data_url = f"data:image/png;base64,{encode_image(resized_image)}"
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": f"{instruction}\n\nAnalyze this image (Image {i+1}/{len(images)}). First, determine if it's a construction site. If it is, explain the image in detail, focusing on safety aspects. If it's not, briefly describe what you see."
},
{
"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)
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"{instruction}\n\nAnalyze this frame from a video (Frame {i+1}/{len(frames)}). First, explain the video and then determine if it's a construction site. If it is, explain what you observe, focusing on safety aspects. If it's not, briefly describe what you see."
},
{
"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())
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)}")]
def generate_summary_report(chat_history):
"""
Generate a summary report from the chat history.
"""
report = "Construction Site Safety Analysis Report\n"
report += "=" * 40 + "\n"
report += f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
for i, (user, ai) in enumerate(chat_history, 1):
if user:
report += f"Query {i}:\n{user}\n\n"
if ai:
report += f"Analysis {i}:\n{ai}\n\n"
report += "-" * 40 + "\n"
return report
def download_report(chat_history):
"""
Generate and provide a download link for the summary report.
"""
report = generate_summary_report(chat_history)
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"safety_analysis_report_{timestamp}.txt"
return (filename, report)
# 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(
"""
"""
)
# First row: Upload Image and Video side by side
with gr.Row():
with gr.Column(scale=1):
image_input = gr.File(label="Upload Construction Site Images", file_count="multiple", type="filepath", elem_classes="image-container")
with gr.Column(scale=1):
video_input = gr.Video(label="Upload Construction Site Video", elem_classes="image-container")
# Second row: Analyze Safety Hazards Button
with gr.Row():
analyze_button = gr.Button("🔍 Analyze Safety Hazards", elem_classes="analyze-button")
# Third row: Chat Interface (Safety Analysis Results)
with gr.Row():
chatbot = gr.Chatbot(label="Safety Analysis Results and Expert Chat", elem_classes="chatbot")
# Fourth 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"
)
# Fifth row: Clear Chat and Download Report Buttons
with gr.Row():
clear = gr.Button("🗑️ Clear Chat", elem_classes="clear-button")
download_button = gr.Button("📥 Download Report", elem_classes="download-button")
# Add a file component to handle the download
report_file = gr.File(label="Download Safety Analysis Report")
def update_chat(history, new_messages):
history = history or []
for title, content in new_messages:
history.append((None, f"{title}\n\n{content}"))
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)
download_button.click(
download_report,
inputs=[chatbot],
outputs=[report_file]
)
gr.HTML(
"""
Powered by Groq
"""
)
# Launch the app
if __name__ == "__main__":
try:
iface.launch(debug=True)
except Exception as e:
logger.error(f"Error when trying to launch the interface: {str(e)}")
logger.error(traceback.format_exc())
print("Failed to launch the Gradio interface. Please check the logs for more information.")