Delete app_v1.py

app_v1.py

@@ -1,146 +0,0 @@
-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(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 = "Image File"
-        worksheet.cell(row=1, column=2).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 = image_file_name
-        worksheet.cell(row=last_row_index + i + 1, column=2).value = predicted_area_sqm
-
-    # Save the workbook to a temporary file
-    temp_file = "predicted_areas.xlsx"
-    workbook.save(temp_file)
-
-    # Get the path of the first uploaded image
-    first_image_path = image_files[0].name if image_files else None
-
-    return f"Sum of predicted house square footage: {total_area_sqm:.2f} square meters", temp_file ,first_image_path
-
-
-inputs = [
-    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"),
-    gr.outputs.Image(type="pil", label="Uploaded Image")
-]
-
-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()
-