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requirements.txt

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+gradio
+pillow
+torch
+torchvision
+opencv-python
+numpy
+openpyxl

web.py

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+import gradio as gr
+from PIL import Image
+import torch
+import torchvision.models as models
+import torchvision.transforms as transforms
+import cv2
+import numpy as np
+import openpyxl
+import os
+from tkinter import filedialog
+
+# Load the pre-trained EfficientNet-B7 model
+model = models.efficientnet_b7(pretrained=True)
+model.eval()
+
+# Define the transformations to be applied to the input image
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+def predict_house_area(room_id, excel_file, image_files):
+    total_area_sqm = 0
+    predicted_areas = []
+
+    # Check if the excel_file is provided
+    if excel_file is not None:
+        # Load the existing Excel workbook
+        workbook = openpyxl.load_workbook(excel_file.name)
+        worksheet = workbook.active
+    else:
+        # Create a new Excel workbook
+        workbook = openpyxl.Workbook()
+        worksheet = workbook.active
+
+        # Write the headers to the worksheet
+        worksheet.cell(row=1, column=1).value = "Room ID"
+        worksheet.cell(row=1, column=2).value = "Image File"
+        worksheet.cell(row=1, column=3).value = "Predicted Area (sqm)"
+
+    # Get the last row index to append new data
+    last_row_index = worksheet.max_row if worksheet.max_row else 1
+
+    # Loop over all the images
+    for i, image_file in enumerate(image_files):
+        # Load the input image
+        img = Image.open(image_file.name)
+        # Extract the image file name from the path
+        image_file_name = os.path.basename(image_file.name)
+        # Check if the image is PNG and convert to JPEG if it is
+        if img.format == "PNG":
+            # Convert the image to RGB format
+            img = img.convert("RGB")
+
+        # Apply the transformations to the input image
+        img_transformed = transform(img)
+
+        # Add a batch dimension to the transformed image tensor
+        img_transformed_batch = torch.unsqueeze(img_transformed, 0)
+
+        # Use the pre-trained model to make a prediction on the input image
+        with torch.no_grad():
+            output = model(img_transformed_batch)
+
+        # Convert the output tensor to a probability distribution using softmax
+        softmax = torch.nn.Softmax(dim=1)
+        output_probs = softmax(output)
+
+        # Extract the predicted class (house square footage) from the output probabilities
+        predicted_class = torch.argmax(output_probs)
+
+        # Calculate the predicted area based on the predicted class
+        predicted_area_sqm = 0
+        if predicted_class in [861, 648, 594, 894, 799, 896, 454]:
+            # Convert to grayscale and apply adaptive thresholding
+            gray = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2GRAY)
+            gray = cv2.GaussianBlur(gray, (5, 5), 0)
+            mask = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 2)
+
+            # Apply Canny edge detection to the binary mask
+            edges = cv2.Canny(mask, 30, 100)
+
+            # Apply dilation to fill gaps in the contour
+            kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
+            dilated = cv2.dilate(edges, kernel, iterations=2)
+            eroded = cv2.erode(dilated, kernel, iterations=1)
+
+            # Find contours in binary mask
+            contours, _ = cv2.findContours(eroded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+            # Find largest contour and calculate area
+            max_area = 0
+            for c in contours:
+                area = cv2.contourArea(c)
+                if area > max_area:
+                    max_area = area
+
+            # Convert pixel area to square meters
+            pixels_per_meter = 300  # adjust this value based on your image resolution and actual room dimensions
+            predicted_area_sqm = (max_area + 10) / (2 * pixels_per_meter ** 2)
+        else:
+            predicted_area_sqft = predicted_class.item()
+            predicted_area_sqm = predicted_area_sqft * 0.092903 / 4.2
+
+        # Add the predicted area to the sum
+        total_area_sqm += predicted_area_sqm
+
+        # Add the predicted area to the list of predicted areas
+        predicted_areas.append(predicted_area_sqm)
+
+        # Write the room ID, image file name, and predicted area to the worksheet
+        worksheet.cell(row=last_row_index + i + 1, column=1).value = room_id
+        worksheet.cell(row=last_row_index + i + 1, column=2).value = image_file_name
+        worksheet.cell(row=last_row_index + i + 1, column=3).value = predicted_area_sqm
+
+    # Save the workbook to a temporary file
+    temp_file = "predicted_areas.xlsx"
+    workbook.save(temp_file)
+
+    return f"Sum of predicted house square footage: {total_area_sqm:.2f} square meters", temp_file
+
+
+inputs = [
+    gr.inputs.Textbox(label = "Mã Phòng" , type = "text"),
+    gr.inputs.File(label="Excel File", type="file"),
+    gr.inputs.File(label="Images", type="file", file_count="multiple")
+]
+
+outputs = [
+    gr.outputs.Textbox(label="Sum of Predicted House Square Footage"),
+    gr.outputs.File(label="Excel Result")
+]
+
+interface = gr.Interface(
+    fn=predict_house_area,
+    inputs=inputs,
+    outputs=outputs,
+    title="House Predictor",
+    allow_flagging="never"  # Disable flag button
+)
+
+if __name__ == "__main__":
+    interface.launch(share=True)
+