switch to streamlit UI, add download for updated cards, other minor fixes

.gitignore

@@ -1,2 +1,3 @@
 __pycache__
 compliancecards.code-workspace
+temp.ipynb

.ipynb_checkpoints/app-checkpoint.ipynb

@@ -0,0 +1,166 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import streamlit as st\n",
+    "\n",
+    "from streamlit_jupyter import StreamlitPatcher, tqdm\n",
+    "\n",
+    "StreamlitPatcher().jupyter()  # register streamlit with jupyter-compatible wrappers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "# AI"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-08-13 14:11:32.399 \n",
+      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
+      "  command:\n",
+      "\n",
+      "    streamlit run /mnt/wsl/PHYSICALDRIVE1p1/@home/non/.venv/lib/python3.10/site-packages/ipykernel_launcher.py [ARGUMENTS]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import yaml\n",
+    "from pathlib import Path\n",
+    "from compliance_analysis import run_compliance_analysis_on_project, run_compliance_analysis_on_data, run_compliance_analysis_on_model\n",
+    "\n",
+    "# def process_files(files):\n",
+    "#     results = []\n",
+    "#     for file in files:\n",
+    "#         with open(file.name, 'r') as f:\n",
+    "#             content = f.read()\n",
+    "#         if Path(file.name).name == \"project_cc.yaml\":\n",
+    "#             project_cc_yaml = yaml.safe_load(content)\n",
+    "#             msg = run_compliance_analysis_on_project(project_cc_yaml)\n",
+    "#             results.append(msg)            \n",
+    "#         # if Path(file.name).name == \"data_cc.yaml\":\n",
+    "#         #     data_cc_yaml = yaml.safe_load(content)\n",
+    "#         #     msg = run_compliance_analysis_on_data(data_cc_yaml)\n",
+    "#         #     results.append(msg)        \n",
+    "#         # if Path(file.name).name == \"model_cc.yaml\":\n",
+    "#         #     model_cc_yaml = yaml.safe_load(content)\n",
+    "#         #     msg = run_compliance_analysis_on_model(model_cc_yaml)\n",
+    "#         #     results.append(msg)\n",
+    "            \n",
+    "#     return results\n",
+    "\n",
+    "import yaml\n",
+    "from pathlib import Path\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "def process_files(files):\n",
+    "    results = []\n",
+    "    for file in files:\n",
+    "        content = file.read().decode(\"utf-8\")\n",
+    "        if Path(file.name).name == \"project_cc.yaml\":\n",
+    "            project_cc_yaml = yaml.safe_load(content)\n",
+    "            if project_cc_yaml:\n",
+    "                msg = run_compliance_analysis_on_project(project_cc_yaml)\n",
+    "                results.append(msg)            \n",
+    "    return results\n",
+    "\n",
+    "def extract_properties(files):\n",
+    "    properties = []\n",
+    "    for file in files:\n",
+    "        content = file.read().decode(\"utf-8\")\n",
+    "        project_cc_yaml = yaml.safe_load(content)\n",
+    "        if project_cc_yaml:\n",
+    "            properties.extend([key for key in project_cc_yaml])\n",
+    "    return properties\n",
+    "\n",
+    "def sentence_builder(keys):\n",
+    "    return f\"Selected options: {', '.join(keys)}\"\n",
+    "\n",
+    "# Streamlit app\n",
+    "st.title(\"AI\")\n",
+    "\n",
+    "uploaded_files = st.file_uploader(\"Upload YAML Files\", type=\"yaml\", accept_multiple_files=True)\n",
+    "\n",
+    "if uploaded_files:\n",
+    "    # Process the files and display the output\n",
+    "    if st.button(\"Process Files\"):\n",
+    "        results = process_files(uploaded_files)\n",
+    "        for result in results:\n",
+    "            st.text(result)\n",
+    "    \n",
+    "    # Extract properties\n",
+    "    properties = extract_properties(uploaded_files)\n",
+    "    \n",
+    "    # Create a DataFrame with properties and a checkbox column\n",
+    "    df = pd.DataFrame({\n",
+    "        \"Property\": properties,\n",
+    "        \"Select\": [False] * len(properties)  # Default to unchecked\n",
+    "    })\n",
+    "\n",
+    "    # Display DataFrame with checkboxes using st.column_config.CheckboxColumn\n",
+    "    edited_df = st.data_editor(\n",
+    "        df,\n",
+    "        column_config={\n",
+    "            \"Select\": st.column_config.CheckboxColumn(\"Select\"),\n",
+    "        },\n",
+    "        key=\"data_editor\"\n",
+    "    )\n",
+    "\n",
+    "    # Get selected properties\n",
+    "    selected_properties = edited_df[edited_df[\"Select\"]][\"Property\"].tolist()\n",
+    "    \n",
+    "    # Build the sentence based on selected properties\n",
+    "    if selected_properties:\n",
+    "        sentence = sentence_builder(selected_properties)\n",
+    "        st.text(sentence)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

app.py

@@ -3,32 +3,211 @@ import yaml
 from pathlib import Path
 from compliance_analysis import run_compliance_analysis_on_project, run_compliance_analysis_on_data, run_compliance_analysis_on_model
 
-def process_files(files):
-    results = []
+# def process_files(files):
+#     results = []
+#     for file in files:
+#         with open(file.name, 'r') as f:
+#             content = f.read()
+#         if Path(file.name).name == "project_cc.yaml":
+#             project_cc_yaml = yaml.safe_load(content)
+#             msg = run_compliance_analysis_on_project(project_cc_yaml)
+#             results.append(msg)            
+#         # if Path(file.name).name == "data_cc.yaml":
+#         #     data_cc_yaml = yaml.safe_load(content)
+#         #     msg = run_compliance_analysis_on_data(data_cc_yaml)
+#         #     results.append(msg)        
+#         # if Path(file.name).name == "model_cc.yaml":
+#         #     model_cc_yaml = yaml.safe_load(content)
+#         #     msg = run_compliance_analysis_on_model(model_cc_yaml)
+#         #     results.append(msg)
+            
+#     return results
+
+# def extract_properties(files):
+#     for file in files:
+#         with open(file.name, 'r') as f:    
+#             content = f.read()
+#             project_cc_yaml = yaml.safe_load(content)
+#         project_cc = [key for key in project_cc_yaml]
+#     return project_cc
+
+# def sentence_builder(countries):
+#     return f"{countries}"
+
+# # # Gradio interface
+# with gr.Blocks() as demo:
+#     file_input = gr.File(label="Upload Files", file_count="multiple")
+#     output = gr.Textbox(label="Output", lines=10)
+
+#     submit_button = gr.Button("Process Files")
+#     submit_button.click(process_files, inputs=file_input, outputs=output)
+
+#     # Create the CheckboxGroup (initially empty)
+#     checkbox_group = gr.CheckboxGroup(choices=[], label="", interactive=True)
+    
+#     # Create the output Textbox
+#     output = gr.Textbox()
+
+#     # Function to update the CheckboxGroup when files are uploaded
+#     def update_checkboxes(files):
+#         choices = extract_properties(files)
+#         return gr.CheckboxGroup(choices=choices, label="", interactive=True)
+    
+#     # Create a Button to trigger the sentence builder
+#     submit_button = gr.Button("Submit")
+
+#     # Set up the interaction for file input and updating checkboxes
+#     file_input.change(update_checkboxes, inputs=file_input, outputs=checkbox_group)
+    
+#     gr.CheckboxGroup.change(update_checkboxes)
+    
+#     output = gr.Textbox()
+#     submit_button = gr.Button("Submit")
+#     submit_button.click(sentence_builder, inputs=checkbox_group, outputs=output)
+
+
+# if __name__ == "__main__":
+#     demo.launch()
+
+import streamlit as st
+import yaml
+from pathlib import Path
+import pandas as pd
+
+
+def load_data(files):
+    cards = []
     for file in files:
-        with open(file.name, 'r') as f:
-            content = f.read()
+        content = file.read().decode("utf-8")
         if Path(file.name).name == "project_cc.yaml":
             project_cc_yaml = yaml.safe_load(content)
-            msg = run_compliance_analysis_on_project(project_cc_yaml)
-            results.append(msg)            
-        # if Path(file.name).name == "data_cc.yaml":
-        #     data_cc_yaml = yaml.safe_load(content)
-        #     msg = run_compliance_analysis_on_data(data_cc_yaml)
-        #     results.append(msg)        
-        # if Path(file.name).name == "model_cc.yaml":
-        #     model_cc_yaml = yaml.safe_load(content)
-        #     msg = run_compliance_analysis_on_model(model_cc_yaml)
-        #     results.append(msg)
-            
+            data = project_cc_yaml
+            card_type = "project"
+            cards.append((card_type, data))
+        if Path(file.name).name == "data_cc.yaml":
+            data_cc_yaml = yaml.safe_load(content)
+            data = data_cc_yaml
+            card_type = "data"
+            cards.append((card_type, data))
+        if Path(file.name).name == "model_cc.yaml":
+            model_cc_yaml = yaml.safe_load(content)
+            data = model_cc_yaml
+            card_type = "model"
+            cards.append((card_type, data))
+    return cards
+
+# def process_files(files):
+#     results = []
+#     for file in files:
+#         content = file.read().decode("utf-8")
+#         if Path(file.name).name == "project_cc.yaml":
+#             project_cc_yaml = yaml.safe_load(content)
+#             if project_cc_yaml:
+#                 msg = run_compliance_analysis_on_project(project_cc_yaml)
+#                 results.append(msg)            
+#     return results
+
+def process_files(data):
+    results = []
+    msg = run_compliance_analysis_on_project(yaml.safe_load(data))
+    results.append(msg)            
     return results
 
-# Gradio interface
-with gr.Blocks() as demo:
-    file_input = gr.File(label="Upload Files", file_count="multiple")
-    output = gr.Textbox(label="Output", lines=10)
+def extract_properties(data):
+        
+    flattened_data = []
+
+    for category, items in data.items():
+        for item, attributes in items.items():
+            flattened_data.append({
+                "Category": category,
+                "Item": item,
+                "Verbose": attributes["verbose"],
+                "Value": attributes["value"]
+            })
+    df = pd.DataFrame(flattened_data)
+    
+    return df
+
+def sentence_builder(keys):
+    return f"Selected options: {', '.join(keys)}"
+
+# Streamlit app
+# st.set_page_config(page_title="AI", layout="wide")
+# st.markdown(
+#     """
+#     <style>
+#     [data-testid="stSidebar"][aria-expanded="true"] > div:first-child{
+#         width: 600px;
+#     }
+#     [data-testid="stSidebar"][aria-expanded="false"] > div:first-child{
+#         width: 600px;
+#         margin-left: -400px;
+#     }
+     
+#     """,
+#     unsafe_allow_html=True,
+# )
+
+st.title("AI")
+
+uploaded_files = st.file_uploader("Upload YAML Files", type="yaml", accept_multiple_files=True)
+# project_files = st.file_uploader("Upload Project Files", type="yaml", accept_multiple_files=True)
+
+if uploaded_files:
+
+    cards = load_data(uploaded_files)
+    
+    for card in cards:
+        
+        data = card[1]
     
-    submit_button = gr.Button("Process Files")
-    submit_button.click(process_files, inputs=file_input, outputs=output)
+        if data != None:    
+            # df = extract_properties(data)
+            # df["Value"] = df["Value"].astype(bool)
+
+            # edited_df = st.data_editor(
+            #     df,
+            #     column_config={
+            #         "Value": st.column_config.CheckboxColumn("Value"),
+            #     },
+            #     key="data_editor"
+            # )
+
+            st.title("Compliance Checkboxes")
+            st.title(f"{card[0]}")
+
+            for section, items in data.items():
+                st.header(section.replace('_', ' ').title())  # section header
+                for key, details in items.items():
+                    details['value'] = st.checkbox(details['verbose'], value=details['value'])
 
-demo.launch()
+            # st.write("Updated Data:", data)
+            
+            yaml_data = yaml.dump(data)
+
+            st.download_button(
+                label=f"Download Updated Data as YAML{card[0]}",
+                data=yaml_data,
+                file_name="updated_data.yaml",
+                mime="text/yaml"
+            )
+
+            # json_data = json.dumps(data, indent=2)
+            # st.download_button(
+            #     label="Download Updated Data as JSON",
+            #     data=json_data,
+            #     file_name="updated_data.json",
+            #     mime="application/json"
+            # )
+
+            # selected_properties = edited_df[edited_df["Value"]]["Item"].tolist()
+        
+            # if selected_properties:
+            #     sentence = sentence_builder(selected_properties)
+            #     st.text(sentence)            
+            
+            if st.button(f"Process {card[0]}"):
+                results = process_files(yaml_data)
+                for result in results:
+                    st.text(result) 

compliance_analysis.py

@@ -1,5 +1,5 @@
 import yaml
-from utils import set_type, set_operator_role_and_location, set_eu_market_status, check_within_scope
+from utils import set_type, set_operator_role_and_location, set_eu_market_status, check_within_scope, check_prohibited
 
 # Create some variables we will use throughout our analysis
 
@@ -33,17 +33,16 @@ def run_compliance_analysis_on_project(project_cc_yaml):
     set_eu_market_status(project_variables, project_cc_yaml)
 
     # Check if the project is within scope of the Act. If it's not, the analysis is over.
-    if check_within_scope(project_cc_yaml):
+    if check_within_scope(project_variables, project_cc_yaml):
         msg = ("Project is within the scope of Act. Let's continue...") 
     else: 
         msg = ("Project is not within the scope of what is regulated by the Act.")
-
-    # # Check for prohibited practices. If any exist, the analysis is over.
-    # if check_prohibited(project_cc_yaml) == True: 
-    #     print("Project contains prohibited practices and is therefore non-compliant.")
-    #     msg = ("Project is non-compliant due to a prohibited practice.")
-    # else: 
-    #     print("Project does not contain prohibited practies. Let's continue...")
+        return msg
+    
+    # Check for prohibited practices. If any exist, the analysis is over.
+    if check_prohibited(project_variables, project_cc_yaml) == True:
+        msg = ("Project is non-compliant due to a prohibited practice.")
+        return msg
 
     # If project is high-risk AI system, check that is has met all the requirements for such systems: 
 

data_cc.yaml

@@ -105,23 +105,23 @@ quality_management_system: # Art. 17(1)(f)
     verbose: 'Systems and procedures for data management, including data acquisition, data collection, data analysis, data labelling, data storage, data filtration, data mining, data aggregation, data retention and any other operation regarding the data that is performed before and for the purposes of the placing on the market or putting into service of high-risk AI systems'
     value: !!bool false
 
-gpai_requirements: # Art. 53(1); Annex XI(2)(c)
-  gpai_requirements: 
-    data_type: 
-      verbose: 'Documentation for the dataset is available that contains the type of data'
-      value: !!bool false
-    data_provenance: 
-      verbose: 'Documentation for the dataset is available that contains the provenance of data'
-      value: !!bool false 
-    data_curation:
-      verbose: 'Documentation for the dataset is available that contains the curation methodologies (e.g. cleaning, filtering, etc.)'
-      value: !!bool false 
-    data_number:
-      verbose: 'Documentation for the dataset is available that contains the number of data points'
-      value: !!bool false 
-    data_scope:
-      verbose: 'Documentation for the dataset is available that contains the number of data scope and main characteristics'
-      value: !!bool false 
-    data_origin:
-      verbose: 'Documentation for the dataset is available that contains information on how the data was obtained and selected as well as all other measures to detect the unsuitability of data sources and methods to detect identifiable biases'
-      value: !!bool false 
+# gpai_requirements: # Art. 53(1); Annex XI(2)(c)
+#   gpai_requirements: 
+#     data_type: 
+#       verbose: 'Documentation for the dataset is available that contains the type of data'
+#       value: !!bool false
+#     data_provenance: 
+#       verbose: 'Documentation for the dataset is available that contains the provenance of data'
+#       value: !!bool false 
+#     data_curation:
+#       verbose: 'Documentation for the dataset is available that contains the curation methodologies (e.g. cleaning, filtering, etc.)'
+#       value: !!bool false 
+#     data_number:
+#       verbose: 'Documentation for the dataset is available that contains the number of data points'
+#       value: !!bool false 
+#     data_scope:
+#       verbose: 'Documentation for the dataset is available that contains the number of data scope and main characteristics'
+#       value: !!bool false 
+#     data_origin:
+#       verbose: 'Documentation for the dataset is available that contains information on how the data was obtained and selected as well as all other measures to detect the unsuitability of data sources and methods to detect identifiable biases'
+#       value: !!bool false

model_cc.yaml

@@ -140,7 +140,7 @@ obligations_for_providers_of_gpai_models:
     verbose: 'The architecture and number of parameters'
     value: !!bool false
   input_output_modality: # Art. 53; Annex XI(1)(1)(e)
-    verbos: 'Modality (e.g. text, image) and format of inputs and outputs'
+    verbose: 'Modality (e.g. text, image) and format of inputs and outputs'
     value: !!bool false
   license: # Art. 53; Annex XI(1)(1)(f)
     verbose: 'The license'

project_cc.yaml

@@ -1,5 +1,20 @@
+<<<<<<< HEAD
 
 # Information related to high-level characteristics of AI project, including the role of the operator, market status, and type of AI
+=======
+smb:
+  smb: # Art. 11(1) 
+    verbose: 'AI project is operated by a small or medium-sized enterprise' 
+    value: true
+
+eu_market_status:
+  placed_on_market: # Art. 3(9)
+    verbose: 'AI project is being made available on the Union market for the first time'
+    value: false 
+  put_into_service: #Art. 3(11)
+    verbose: 'AI project is supplied for first use directly to the deployer or for own use in the Union for its intended purpose;'
+    value: false 
+>>>>>>> 2a132e5 (switch to streamlit UI, add download for updated cards, other minor fixes)
 
 operator_role:
   provider: # Art. 2
@@ -93,6 +108,7 @@ excepted:
     verbose: 'AI project involves AI models that are released under a free and open-source licence that allows for the access, usage, modification, and distribution of the model, and whose parameters, including the weights, the information on the model architecture, and the information on model usage, are made publicly available. This exception shall not apply to general purpose AI models with systemic risks'
     value: !!bool false  
 
+<<<<<<< HEAD
 # Information related to practices prohibited by the Act
 
 prohibited_practice:
@@ -130,6 +146,43 @@ prohibited_practice:
     real_time_exception_investigation:
       verbose: 'The AI project involves use of ‘real-time’ remote biometric identification systems in publicly accessible spaces for the purposes of law enforcement stricly for the localisation or identification of a person suspected of having committed a criminal offence, for the purpose of conducting a criminal investigation or prosecution or executing a criminal penalty for offences referred to in Annex II and punishable in the Member State concerned by a custodial sentence or a detention order for a maximum period of at least four years.'
       value: !!bool false 
+=======
+# prohibited_practice:
+#   ai_system:
+#     manipulative: # Art. 5(1)(a)
+#       verbose: 'The AI project deploys subliminal or purposefully manipulative or deceptive techniques, with the objective or effect of materially distorting the behavior of people by appreciably impairing their ability to make an informed decision, thereby causing them to take a decision that they would not have otherwise taken in a manner that causes or is reasonably likely to cause significant harm'
+#       value: !!bool false 
+#     exploit_vulnerable: # Art. 5(1)(b)
+#       verbose: 'The AI project exploits the vulnerabilities of natural people due to their age, disability or a specific social or economic situation, with the objective or effect of materially distorting their behaviour in a manner that causes or is reasonably likely to cause significant harm'
+#       value: !!bool false 
+#     social_score: # Art. 5(1)(c)
+#       verbose: 'The AI project is for the evaluation or classification of natural people over a certain period of time based on their social behaviour or known, inferred or predicted personal or personality characteristics, with the social score leading to at least one of the following: (i) detrimental or unfavourable treatment of certain natural people in social contexts that are unrelated to the contexts in which the data was originally generated or collected; (ii) detrimental or unfavourable treatment of certain natural people that is unjustified or disproportionate to their social behaviour or its gravity'
+#       value: !!bool false 
+#     crime_prediction:  # Art. 5(1)(d)
+#       verbose: 'This AI project makes risk assessments of natural persons in order to assess or predict the risk of them committing a criminal offence, based solely on the profiling of the natural person or on assessing their personality traits and characteristics (and does not support the human assessment of the involvement of a person in a criminal activity, which is already based on objective and verifiable facts directly linked to a criminal activity)'
+#       value: !!bool false
+#     untarged_face: # Art. 5(1)(e)
+#       verbose: 'This AI project creates or expand facial recognition databases through the untargeted scraping of facial images from the internet or CCTV footage'
+#       value: !!bool false 
+#     emotion_prediction: # Art. 5(1)(f)
+#       verbose: 'The AI project infers emotions of a natural person in the areas of workplace and education institutions and is not intended to be put in place or into the market for medical or safety reasons'
+#       value: !!bool false 
+#   biometric:
+#     categorization: # Art. 5(1)(g)
+#       verbose: 'The AI project involves the use of biometric categorisation systems that categorise individually natural persons based on their biometric data to deduce or infer their race, political opinions, trade union membership, religious or philosophical beliefs, sex life or sexual orientation; this prohibition does not cover any labelling or filtering of lawfully acquired biometric datasets, such as images, based on biometric data or categorizing of biometric data in the area of law enforcement'
+#       value: !!bool false 
+#     real_time:  # Art. 5(1)(h)
+#       verbose: 'The AI project involves use of ‘real-time’ remote biometric identification systems in publicly accessible spaces for the purposes of law enforcement'
+#       value: !!bool false 
+#     real_time_exception_victim: # Art. 5(1)(h)
+#       verbose: 'The AI project involves use of ‘real-time’ remote biometric identification systems in publicly accessible spaces for the purposes of law enforcement stricly for the targeted search for specific victims of abduction, trafficking in human beings or sexual exploitation of human beings, or the search for missing persons'
+#       value: !!bool false 
+#     real_time_exception_threat: 
+#       verbose: 'The AI project involves use of ‘real-time’ remote biometric identification systems in publicly accessible spaces for the purposes of law enforcement stricly for the prevention of a specific, substantial and imminent threat to the life or physical safety of natural persons or a genuine and present or genuine and foreseeable threat of a terrorist attack'
+#     real_time_exception_investigation:
+#       verbose: 'The AI project involves use of ‘real-time’ remote biometric identification systems in publicly accessible spaces for the purposes of law enforcement stricly for the localisation or identification of a person suspected of having committed a criminal offence, for the purpose of conducting a criminal investigation or prosecution or executing a criminal penalty for offences referred to in Annex II and punishable in the Member State concerned by a custodial sentence or a detention order for a maximum period of at least four years.'
+#       value: !!bool false 
+>>>>>>> 2a132e5 (switch to streamlit UI, add download for updated cards, other minor fixes)
 
 # Requirements for those projects which involve high-risk AI systems
 

utils.py

@@ -55,7 +55,10 @@ def set_eu_market_status(project_variables, project_cc_yaml):
     return project_variables
 
 
-def check_within_scope(project_cc_yaml):
+def check_within_scope(project_variables, project_cc_yaml):
+    
+    ai_system = project_variables['ai_project_type']['ai_system']
+    
     if not check_excepted(project_cc_yaml):
         if provider and ((ai_system and (placed_on_market or put_into_service)) or (gpai_model and placed_on_market)):   # Article 2.1(a)
             return True
@@ -76,7 +79,7 @@ def check_excepted(project_cc_yaml):
     else:
         return False 
 
-def check_prohibited (project_variables, project_cc_yaml):
+def check_prohibited(project_variables, project_cc_yaml):
 
     ai_system = project_variables['ai_project_type']['ai_system'] 
     
@@ -93,18 +96,4 @@ def check_prohibited (project_variables, project_cc_yaml):
         return True
     else: 
         print("You are not engaged in any prohibited practices.")
-        return False
-
-# def check_all_true(file_path):
-#     data = yaml.safe_load(file_path)
-
-#     # Iterate through top-level keys
-#     for top_key, top_value in data.items():
-#         if isinstance(top_value, dict):
-#             # Iterate through second-level keys
-#             for second_key, second_value in top_value.items():
-#                 if not second_value:
-#                     print("You are non-compliant with the Act")
-#                     break
-#             else:
-#                 print("No problems here")
+        return False