Create app.py

app.py

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+
+import easyocr
+from PIL import Image
+import cv2
+import re
+import gradio as gr
+
+# Initialize the OCR reader
+reader = easyocr.Reader(['en'])
+
+# Define patterns for different room types and dimensions
+room_patterns = {
+    'bedroom': r'bedroom|bed\s?rm',
+    'bathroom': r'bathroom|bath\s?rm',
+    'kitchen': r'kitchen',
+    'living room': r'living\s?room|sitting\s?room',
+    'dining room': r'dining\s?room',
+    # Add more patterns as needed
+}
+
+dimension_pattern = r"(\d+'\s?\d+\")|(\d+\.?\d*\s?[x×]\s?\d+\.?\d*)"  # Pattern to match dimensions like 10'6" or 10x12
+
+# Function to extract room data with counts and measurements
+def extract_room_data(results, room_patterns, dimension_pattern):
+    # ... (same as before) ...
+
+# Function to process the uploaded image
+def process_image(image):
+    # Convert the Gradio image to PIL Image
+    image = Image.fromarray(image)
+
+    # Perform OCR
+    results = reader.readtext(np.array(image), detail=0)
+
+    # Extract room information
+    room_data_with_counts = extract_room_data(results, room_patterns, dimension_pattern)
+
+    # Format output for display
+    output_text = "Extracted Room Data with Counts and Measurements:\n"
+    for room_type, data in room_data_with_counts.items():
+        output_text += f"- {room_type.capitalize()}:\n"
+        output_text += f"  Count: {data['count']}\n"
+        output_text += f"  Measurements: {data['measurements']}\n"
+
+    return output_text
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="pil"),  # Use type="pil" for PIL Image input
+    outputs="text",
+    title="Floor Plan Room Detection",
+    description="Upload a floor plan image to extract room information."
+)
+
+# Launch the interface
+iface.launch(share=True)  # Set share=True to get a shareable link