Delete app.py

app.py

@@ -1,168 +0,0 @@
-import streamlit as st
-from PIL import Image
-import pandas as pd
-import matplotlib.pyplot as plt
-import numpy as np
-from io import BytesIO
-import uuid
-import gc
-
-import sys
-import os
-
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-from segmentation_model import load_model,transform_image, run_inference, save_input_image, save_objects_and_metadata, extract_object
-# from models.identification_model import identify_and_extract_objects
-# from models.text_extraction_model import extract_text
-# from models.summarization_model import summarize_text
-# from utils.data_mapping import create_summary_table
-
-
-
-model = load_model()
-
-def resize_image(image, size=(800, 800)):
-    return image.resize(size, Image.ANTIALIAS)
-
-def display_masks(outputs, image, threshold=0.5):
-    masks = outputs[0]['masks']
-    scores = outputs[0]['scores']
-    
-    fig, ax = plt.subplots()
-    ax.imshow(np.array(image))
-
-    # extracted_objects = []
-    
-    for i in range(len(scores)):
-        if scores[i] > threshold:
-            mask = masks[i].squeeze().cpu().numpy()
-            mask = np.where(mask > 0.5, 1, 0).astype(np.uint8)
-
-            # object_img = extract_object(image,mask)
-            # extracted_objects.append(object_img)
-            # Display the mask
-            ax.imshow(mask, cmap='jet', alpha=0.5)  # Overlay mask on image
-    
-    st.pyplot(fig)
-
-    # return extracted_objects
-
-    
-
-st.title("Image Segmentation with Mask R-CNN")
-
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    # Convert uploaded file to PIL Image
-    image = uploaded_file
-    st.image(image, caption='Uploaded Image.', use_column_width=True)
-    image = Image.open(uploaded_file).convert('RGB')
-    # Generate a unique master ID for the image
-    master_id = str(uuid.uuid4())
-    
-    # Save the input image
-    save_input_image(image, master_id)
-    # Transform image
-    image_tensor = transform_image(image)
-    outputs = run_inference(model, image_tensor)
-
-    # extracted_objects = display_masks(outputs, image)
-    
-    # if extracted_objects:
-    # # Save the extracted objects and their metadata
-    #     metadata = save_objects_and_metadata(extracted_objects, master_id)
-    
-    # # Display metadata as a JSON output
-    #     st.write("Metadata for extracted objects:")
-    #     st.json(metadata)
-    
-    # # Display each extracted object
-    #     st.write("Extracted Objects:")
-    #     for i, obj_img in enumerate(extracted_objects):
-    #      st.image(obj_img, caption=f'Object {i+1}', use_column_width=True)
-    # else:
-    #     st.write("No objects were detected")
-
-
-    # del extracted_objects
-    # gc.collect()
-    
-    # Display results
-    display_masks(outputs, image) 
-
-
-
-
-
-# if uploaded_file is not None:
-#     image = Image.open(uploaded_file).convert("RGB")
-#     st.image(image, caption='Uploaded Image.', use_column_width=True)
-    
-#     image_tensor = transform_image(image)
-#     outputs = run_inference(model, image_tensor)
-    
-#     display_masks(outputs, image)
-
-
-
-# def upload_image():
-#     uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-#     if uploaded_file is not None:
-#         image = Image.open(uploaded_file)
-#         return image
-#     return None
-
-
-# # def display_segmentation(image):
-# #     st.image(image, caption="Original Image", use_column_width=True)
-    
-#     # Transform and run inference
-#     # image_tensor = transform_image(image)
-#     # outputs = run_inference(image_tensor)
-
-#     # # Save segmented objects
-#     # output_dir = 'segmented_objects/'
-#     # save_segmented_objects(image, outputs, output_dir)
-    
-#     # segmented_images = [Image.open(f"{output_dir}object_{i+1}.png") for i in range(len(outputs[0]['scores']))]
-#     # for img in segmented_images:
-#     #     st.image(img, caption="Segmented Object", use_column_width=True)
-
-
-
-
-# def main():
-#     st.title("Image Processing Pipeline")
-
-# #     uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png"])
-# #     if uploaded_file:
-# #         image_path = f"data/input_images/{uploaded_file.name}"
-# #         image = Image.open(uploaded_file)
-# #         image.save(image_path)  # Save the uploaded image for further processing
-# #         st.image(image, caption="Uploaded Image")
-
-# #         if st.button("Segment Image"):
-# #             segmented = segment_image(image_path)
-# #             st.image(segmented, caption="Segmented Image", use_column_width=True)
-
-# #         if st.button("Identify and Extract Objects"):
-# #             objects_data = identify_and_extract_objects(image_path)
-# #             extracted_objects = []
-
-# #             for obj_data in objects_data:
-# #                 object_image = Image.open(obj_data['Image Path'])
-# #                 text = extract_text(object_image)
-# #                 summary = summarize_text(text)
-# #                 obj_data['Text'] = text
-# #                 obj_data['Summary'] = summary
-# #                 extracted_objects.append(obj_data)
-
-# #                 st.image(object_image, caption=f"Object {obj_data['ID']} - Label {obj_data['Label']}")
-
-# #             summary_file = create_summary_table(extracted_objects)
-# #             st.write(pd.DataFrame(extracted_objects))
-# #             st.download_button(label="Download Summary Table", data=open(summary_file).read(), file_name="summary.csv")
-
-# if __name__ == "__main__":
-#     main()