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import gradio as gr
from transformers import pipeline
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
# Load a pre-trained image classification model from Hugging Face
# Replace with a fine-tuned model if available
home_inspector = pipeline("image-classification", model="google/vit-base-patch16-224")
# Function to analyze the uploaded image
def inspect_home(image):
# Analyze the image using the Hugging Face model
results = home_inspector(image)
# Format the results
detected_issues = {result["label"]: round(result["score"], 2) for result in results}
# Generate repair suggestions based on detected issues
suggestions = []
for issue in detected_issues:
suggestion = f"For {issue}, consider inspecting the area closely and consulting a professional if necessary."
suggestions.append(suggestion)
# Generate a PDF report
generate_report(detected_issues, suggestions)
# Return results and suggestions
return detected_issues, suggestions
# Function to generate a PDF report
def generate_report(issues, suggestions, filename="home_inspection_report.pdf"):
c = canvas.Canvas(filename, pagesize=letter)
# Add a title
c.setFont("Helvetica-Bold", 16)
c.drawString(100, 750, "Home Inspection Report")
# Add a subtitle
c.setFont("Helvetica", 12)
c.drawString(100, 730, "AI-Powered Home Inspector")
# Add issues and suggestions
y = 700
for issue, suggestion in zip(issues, suggestions):
c.setFont("Helvetica-Bold", 12)
c.drawString(100, y, f"Issue: {issue} (Confidence: {issues[issue]})")
c.setFont("Helvetica", 10)
c.drawString(100, y - 20, f"Suggestion: {suggestion}")
y -= 40
# Save the PDF
c.save()
# Custom CSS for a modern blue and silver interface
custom_css = """
body {
background: linear-gradient(135deg, #1e3c72, #2a5298);
color: white;
font-family: 'Helvetica', sans-serif;
}
.gradio-container {
background: rgba(255, 255, 255, 0.1);
border-radius: 10px;
padding: 20px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.gradio-input, .gradio-output {
background: rgba(255, 255, 255, 0.2);
border: 1px solid rgba(255, 255, 255, 0.3);
border-radius: 5px;
padding: 10px;
color: white;
}
.gradio-button {
background: #4a90e2;
color: white;
border: none;
border-radius: 5px;
padding: 10px 20px;
font-size: 16px;
cursor: pointer;
transition: background 0.3s ease;
}
.gradio-button:hover {
background: #357abd;
}
"""
# Gradio interface
with gr.Blocks(css=custom_css) as demo:
gr.Markdown("# 🏠 AI-Powered Home Inspector")
gr.Markdown("Upload a photo of your home, and the AI will detect issues and provide repair suggestions.")
with gr.Row():
with gr.Column():
image_input = gr.Image(label="Upload a Photo", type="pil")
inspect_button = gr.Button("Inspect Home", variant="primary")
with gr.Column():
issues_output = gr.Label(label="Detected Issues")
suggestions_output = gr.Textbox(label="Repair Suggestions", lines=5)
# Link the button to the function
inspect_button.click(
fn=inspect_home,
inputs=image_input,
outputs=[issues_output, suggestions_output]
)
gr.Markdown("### Report Generated")
gr.Markdown("A very detailed PDF report has been saved as `home_inspection_report.pdf`.")
# Launch the app
demo.launch() |