Create app.py

app.py

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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 detailed PDF report has been saved as `home_inspection_report.pdf`.")
+
+# Launch the app
+demo.launch()