Update app.py

app.py

@@ -1,114 +1,17 @@
 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")
+# Define a simple function
+def greet(name):
+    return f"Hello, {name}!"
 
-# 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`.")
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=greet,  # Function to call
+    inputs="text",  # Input type
+    outputs="text",  # Output type
+    title="Simple Test App",
+    description="Enter your name and see the greeting!"
+)
 
 # Launch the app
-demo.launch()
+iface.launch()