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import os
import base64
import requests
import gradio as gr
from PIL import Image
import io
# Load environment variables
GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
GROQ_API_URL = "https://api.groq.com/v1/chat/completions"
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
def analyze_construction_image(image):
if image is None:
return "Error: No image uploaded", "", ""
try:
# Convert PIL Image to base64
buffered = io.BytesIO()
image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
# Prepare the message for Groq API
messages = [
{
"role": "system",
"content": "You are an AI assistant specialized in analyzing construction site images. Identify issues, categorize them, and provide steps to resolve them."
},
{
"role": "user",
"content": [
{
"type": "text",
"text": "Analyze this construction image. Identify the snag category, provide a detailed snag description, and list steps to desnag."
},
{
"type": "image_url",
"image_url": f"data:image/png;base64,{img_str}"
}
]
}
]
# Make API request to Groq
headers = {
"Authorization": f"Bearer {GROQ_API_KEY}",
"Content-Type": "application/json"
}
data = {
"model": "llama3-2-vision-90b", # Adjust model name if necessary
"messages": messages,
"max_tokens": 300,
"temperature": 0.7
}
response = requests.post(GROQ_API_URL, headers=headers, json=data)
response.raise_for_status()
result = response.json()["choices"][0]["message"]["content"]
# Parse the result
lines = result.split('\n')
snag_category = lines[0] if len(lines) > 0 else "N/A"
snag_description = lines[1] if len(lines) > 1 else "N/A"
desnag_steps = "\n".join(lines[2:]) if len(lines) > 2 else "N/A"
return snag_category, snag_description, desnag_steps
except Exception as e:
return f"Error: {str(e)}", "", ""
# Create the Gradio interface
iface = gr.Interface(
fn=analyze_construction_image,
inputs=gr.Image(type="pil", label="Upload Construction Image"),
outputs=[
gr.Textbox(label="Snag Category"),
gr.Textbox(label="Snag Description"),
gr.Textbox(label="Steps to Desnag")
],
title="Construction Image Analyzer (Llama 3.2-Vision via Groq)",
description="Upload a construction site image to identify issues and get desnag steps using Llama 3.2-Vision technology through Groq API.",
examples=[
["example_image1.jpg"],
["example_image2.jpg"]
],
cache_examples=True,
theme="default"
)
# Launch the app
if __name__ == "__main__":
iface.launch() |