Delete app.py

app.py

@@ -1,99 +0,0 @@
-import gradio as gr
-import numpy as np
-import pandas as pd
-from app_utils import annotate_planogram_compliance, do_sorting, xml_to_csv
-from inference import run
-import json
-import os
-from tempfile import NamedTemporaryFile
-
-
-# Target names list
-target_names = [
-    "Bottle,100PLUS ACTIVE 1.5L",
-    "Bottle,100PLUS ACTIVE 500ML",
-    "Bottle,100PLUS LEMON LIME 1.5L",
-    # Add all other target names here
-]
-
-# Define the function to run planogram compliance check
-def planogram_compliance_check(planogram_image, master_planogram_image, annotation_file):
-    # Convert uploaded images to numpy arrays
-    planogram_img = np.array(planogram_image)
-    master_planogram_img = np.array(master_planogram_image)
-
-    # Perform inference on planogram image
-    result_list = run(
-        weights="base_line_best_model_exp5.pt",
-        source=planogram_img,
-        imgsz=[640, 640],
-        conf_thres=0.6,
-        iou_thres=0.6,
-    )
-
-    # Load annotation file and convert to DataFrame
-    if annotation_file is not None:
-        annotation_df = xml_to_csv(annotation_file)
-        sorted_xml_df = do_sorting(annotation_df)
-    else:
-        sorted_xml_df = None
-
-    # Run planogram compliance check
-    compliance_score, annotated_image = run_compliance_check(
-        planogram_img, master_planogram_img, sorted_xml_df, result_list
-    )
-
-    return compliance_score, annotated_image
-
-def run_compliance_check(planogram_img, master_planogram_img, sorted_xml_df, result_list):
-    # Placeholder for actual score calculation
-    compliance_score = 0.0
-
-    # Placeholder for annotated image
-    annotated_image = planogram_img.copy()
-
-    if sorted_xml_df is not None:
-        annotate_df = sorted_xml_df[["xmin", "ymin", "xmax", "ymax", "line_number", "cls"]].astype(int)
-    else:
-        annotate_df = None
-
-    mask = master_table != non_null_product
-    m_detected_table = np.ma.masked_array(master_table, mask=mask)
-    m_annotated_table = np.ma.masked_array(detected_table, mask=mask)
-
-    # wrong_indexes = np.ravel_multi_index(master_table*mask != detected_table*mask, master_table.shape)
-    wrong_indexes = np.where(master_table != detected_table)
-    correct_indexes = np.where(master_table == detected_table)
-
-    # Annotate planogram compliance on the image
-    annotated_image = annotate_planogram_compliance(
-        annotated_image, annotate_df, correct_indexes, wrong_indexes, target_names
-    )
-
-    # Calculate compliance score
-    correct_matches = (np.ma.masked_equal(master_table, non_null_product) == detected_table).sum()
-    total_products = (master_table != non_null_product).sum()
-    if total_products != 0:
-        compliance_score = correct_matches / total_products
-
-    return compliance_score, annotated_image
-
-
-# Gradio interface
-planogram_check_interface = gr.Interface(
-    fn=planogram_compliance_check,
-    inputs=[
-        gr.inputs.Image(label="Planogram Image"),
-        gr.inputs.Image(label="Master Planogram Image"),
-        gr.inputs.Dataframe(label="Annotation File (XML)")
-    ],
-    outputs=[
-        gr.outputs.Textbox(label="Compliance Score"),
-        gr.outputs.Image(label="Annotated Planogram Image"),
-    ],
-    title="Planogram Compliance Checker",
-    description="Upload planogram image, master planogram image, and annotation file (if available) to check compliance."
-)
-
-
-planogram_check_interface.launch()