Update 1_📝_form.py

1_📝_form.py

@@ -1,4 +1,4 @@
-from yaml import load
+# from yaml import load
 from persist import persist, load_widget_state
 import streamlit as st
 from io import StringIO 
@@ -11,16 +11,54 @@ from huggingface_hub import create_repo
 import os
 from middleMan import parse_into_jinja_markdown as pj
 
+import requests
 
-
-@st.cache
+# @st.cache
+def get_icd():
+    # Get ICD10 list
+    token_endpoint = 'https://icdaccessmanagement.who.int/connect/token'
+    client_id = '3bc9c811-7f2e-4dab-a2dc-940e47a38fef_a6108252-4503-4ff7-90ab-300fd27392aa'
+    client_secret = 'xPj7mleWf1Bilu9f7P10UQmBPvL5F6Wgd8/rJhO1T04='
+    scope = 'icdapi_access'
+    grant_type = 'client_credentials'
+    # set data to post
+    payload = {'client_id': client_id, 
+            'client_secret': client_secret, 
+            'scope': scope, 
+            'grant_type': grant_type}
+    # make request
+    r = requests.post(token_endpoint, data=payload, verify=False).json()
+    token = r['access_token']
+    # access ICD API
+    uri = 'https://id.who.int/icd/release/10/2019/C00-C75'
+    # HTTP header fields to set
+    headers = {'Authorization':  'Bearer '+token, 
+            'Accept': 'application/json', 
+            'Accept-Language': 'en',
+            'API-Version': 'v2'}        
+    # make request           
+    r = requests.get(uri, headers=headers, verify=False)
+    print("icd",r.json())
+    icd_map =[]
+    for child in r.json()['child']: 
+        r_child = requests.get(child, headers=headers,verify=False).json()
+        icd_map.append(r_child["code"]+" "+r_child["title"]["@value"])
+    return icd_map
+
+# @st.cache    
+def get_treatment_mod():
+    url = "https://clinicaltables.nlm.nih.gov/loinc_answers?loinc_num=21964-2"
+    r = requests.get(url).json()
+    treatment_mod = [treatment['DisplayText'] for treatment in r]
+    return treatment_mod
+
+
+# @st.cache _data or _resource
 def get_cached_data():
     languages_df = pd.read_html("https://hf.co/languages")[0]
     languages_map = pd.Series(languages_df["Language"].values, index=languages_df["ISO code"]).to_dict()
 
     license_df = pd.read_html("https://huggingface.co/docs/hub/repositories-licenses")[0]
-    print("license_df.keys()",license_df.keys())
-    print(license_df["License identifier (to use in repo card)"])
     license_map = pd.Series(
         license_df["License identifier (to use in repo card)"].values, index=license_df.Fullname
     ).to_dict()
@@ -31,7 +69,10 @@ def get_cached_data():
     tags_data = r.json()
     libraries = [x['id'] for x in tags_data['library']]
     tasks = [x['id'] for x in tags_data['pipeline_tag']]
-    return languages_map, license_map, available_metrics, libraries, tasks
+
+    icd_map = get_icd()
+    treatment_mod = get_treatment_mod()
+    return languages_map, license_map, available_metrics, libraries, tasks, icd_map, treatment_mod
 
 
 def card_upload(card_info,repo_id,token):
@@ -100,9 +141,8 @@ def save_uploadedfile(uploadedfile):
 
 
 def main_page():
-    print("HELLO")
-    st.cache_data.clear()
-   
+
+    		
     if "model_name" not in st.session_state:
         # Initialize session state.
         st.session_state.update({
@@ -175,31 +215,58 @@ def main_page():
             "markdown_state":"",
         })
     ## getting cache for each warnings 
-    languages_map, license_map, available_metrics, libraries, tasks = get_cached_data()
+    languages_map, license_map, available_metrics, libraries, tasks, icd_map, treatment_mod = get_cached_data()
 
     ## form UI setting 
-    st.header("Model Card Form")
+    st.header("Model basic information (Dose prediction)")
 
     warning_placeholder = st.empty()
 
     st.text_input("Model Name", key=persist("model_name"))
-    st.text_area("Model Description", help="The model description provides basic details about the model. This includes the architecture, version, if it was introduced in a paper, if an original implementation is available, the author, and general information about the model. Any copyright should be attributed here. General information about training procedures, parameters, and important disclaimers can also be mentioned in this section.", key=persist('model_description'))
-    st.multiselect("Language(s)", list(languages_map), format_func=lambda x: languages_map[x], help="The language(s) associated with this model. If this is not a text-based model, you should specify whatever language that is used in the dataset. For instance, if the dataset's labels are in english, you should select English here.", key=persist("languages"))
+    st.number_input("Version",key=persist("version"),step=0.1)
+    st.text("Intended use:")
+    left, right = st.columns([4,2])
+    left.multiselect("Treatment site ICD10",list(icd_map), help="Reference ICD10 WHO: https://icd.who.int/icdapi")
+    right.multiselect("Treatment modality", list(treatment_mod), help="Reference LOINC Modality Radiation treatment: https://loinc.org/21964-2" )
+    left, right = st.columns(2)
+    nlines = left.number_input("Number of prescription levels", 0, 20, 1)
+    # cols = st.columns(ncol)
+    for i in range(nlines):
+        right.number_input(f"Prescription [Gy] # {i}", key=i)
+    st.text_area("Additional information", placeholder = "Bilateral cases only", help="E.g. Bilateral cases only", key=persist('additional_information'))
+    st.text_area("Motivation for development", key=persist('motivation'))
+    st.text_area("Class", placeholder="RULE 11, FROM MDCG 2021-24", key=persist('class'))
+    st.date_input("Creation date", key=persist('creation_date'))
+    st.text_area("Type of architecture",value="UNet", key=persist('architecture'))
+
+    st.text("Developed by:")
+    left, middle, right = st.columns(3)
+    left.text_input("Name", key=persist('dev_name'))
+    middle.text_input("Institution", placeholder = "University/clinic/company", key=persist('dev_institution'))
+    right.text_input("Email", key=persist('dev_email'))
+
+    st.text_area("Funded by", key=persist('fund'))
+    st.text_area("Shared by", key=persist('shared'))
     st.selectbox("License", [""] + list(license_map.values()), help="The license associated with this model.", key=persist("license"))
-    st.selectbox("Library Name", [""] + libraries, help="The name of the library this model came from (Ex. pytorch, timm, spacy, keras, etc.). This is usually automatically detected in model repos, so it is not required.", key=persist('library_name'))
-    st.text_input("Parent Model (URL)", help="If this model has another model as its base, please provide the URL link to the parent model", key=persist("Parent_Model_name"))
-    st.text_input("Datasets (comma separated)", help="The dataset(s) used to train this model. Use dataset id from https://hf.co/datasets.", key=persist("datasets"))
-    st.multiselect("Metrics", available_metrics, help="Metrics used in the training/evaluation of this model. Use metric id from https://hf.co/metrics.", key=persist("metrics"))
-    st.selectbox("Task", [""] + tasks, help="What task does this model aim to solve?", key=persist('task'))
-    st.text_input("Tags (comma separated)", help="Additional tags to add which will be filterable on https://hf.co/models. (Ex. image-classification, vision, resnet)", key=persist("tags"))
-    st.text_input("Author(s) (comma separated)", help="The authors who developed this model. If you trained this model, the author is you.", key=persist("the_authors"))
+    st.text_area("Fine tuned from model", key=persist('fine_tuned_from'))
     st.text_input("Related Research Paper", help="Research paper related to this model.", key=persist("paper_url"))
     st.text_input("Related GitHub Repository", help="Link to a GitHub repository used in the development of this model", key=persist("github_url"))
     st.text_area("Bibtex Citation", help="Bibtex citations for related work", key=persist("bibtex_citations"))
-    st.text_input("Carbon Emitted:", help="You can estimate carbon emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700)", key=persist("Model_c02_emitted"))
-   
+    # st.selectbox("Library Name", [""] + libraries, help="The name of the library this model came from (Ex. pytorch, timm, spacy, keras, etc.). This is usually automatically detected in model repos, so it is not required.", key=persist('library_name'))
+    # st.text_input("Parent Model (URL)", help="If this model has another model as its base, please provide the URL link to the parent model", key=persist("Parent_Model_name"))
+    # st.text_input("Datasets (comma separated)", help="The dataset(s) used to train this model. Use dataset id from https://hf.co/datasets.", key=persist("datasets"))
+    # st.multiselect("Metrics", available_metrics, help="Metrics used in the training/evaluation of this model. Use metric id from https://hf.co/metrics.", key=persist("metrics"))
+    # st.selectbox("Task", [""] + tasks, help="What task does this model aim to solve?", key=persist('task'))
+    # st.text_input("Tags (comma separated)", help="Additional tags to add which will be filterable on https://hf.co/models. (Ex. image-classification, vision, resnet)", key=persist("tags"))
+    # st.text_input("Author(s) (comma separated)", help="The authors who developed this model. If you trained this model, the author is you.", key=persist("the_authors"))
+    # s
+    # st.text_input("Carbon Emitted:", help="You can estimate carbon emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700)", key=persist("Model_c02_emitted"))
    
-    
+    # st.header("Technical specifications")
+    # st.header("Training data, methodology, and results")
+    # st.header("Evaluation data, methodology, and results / commissioning")
+    # st.header("Ethical use considerations")
+
     # warnings setting
     languages=st.session_state.languages or None
     license=st.session_state.license or None
@@ -221,70 +288,70 @@ def main_page():
     if do_warn:
         warning_placeholder.error(warning_msg)
 
-    with st.sidebar:
+    # with st.sidebar:
 
-        ######################################################
-        ### Uploading a model card from local drive
-        ######################################################
-        st.markdown("## Upload Model Card")
+    #     ######################################################
+    #     ### Uploading a model card from local drive
+    #     ######################################################
+    #     st.markdown("## Upload Model Card")
        
-        st.markdown("#### Model Card must be in markdown (.md) format.") 
+    #     st.markdown("#### Model Card must be in markdown (.md) format.") 
 
-        # Read a single file 
-        uploaded_file = st.file_uploader("Choose a file", type = ['md'], help = 'Please choose a markdown (.md) file type to upload')
-        if uploaded_file is not None:
+    #     # Read a single file 
+    #     uploaded_file = st.file_uploader("Choose a file", type = ['md'], help = 'Please choose a markdown (.md) file type to upload')
+    #     if uploaded_file is not None:
            
-            file_details = {"FileName":uploaded_file.name,"FileType":uploaded_file.type} 
-            name_of_uploaded_file = save_uploadedfile(uploaded_file)
+    #         file_details = {"FileName":uploaded_file.name,"FileType":uploaded_file.type} 
+    #         name_of_uploaded_file = save_uploadedfile(uploaded_file)
            
-            st.session_state.markdown_upload = name_of_uploaded_file ## uploaded model card
+    #         st.session_state.markdown_upload = name_of_uploaded_file ## uploaded model card
 
-        elif st.session_state.task =='fill-mask' or 'translation' or 'token-classification' or ' sentence-similarity' or 'summarization' or 'question-answering' or 'text2text-generation' or 'text-classification' or 'text-generation' or 'conversational':
-            #st.session_state.markdown_upload = open(
-             #   "language_model_template1.md", "r+"
-            #).read() 
-            st.session_state.markdown_upload = "language_model_template1.md" ## language model template
+    #     elif st.session_state.task =='fill-mask' or 'translation' or 'token-classification' or ' sentence-similarity' or 'summarization' or 'question-answering' or 'text2text-generation' or 'text-classification' or 'text-generation' or 'conversational':
+    #         #st.session_state.markdown_upload = open(
+    #          #   "language_model_template1.md", "r+"
+    #         #).read() 
+    #         st.session_state.markdown_upload = "language_model_template1.md" ## language model template
         
-        elif st.session_state.task:
+    #     elif st.session_state.task:
             
-            st.session_state.markdown_upload =  "current_card.md" ## default non language model template
+    #         st.session_state.markdown_upload =  "current_card.md" ## default non language model template
        
-        #########################################
-        ### Uploading model card to HUB
-        #########################################
-        out_markdown =open( st.session_state.markdown_upload, "r+"
-            ).read() 
-        print_out_final = f"{out_markdown}"
-        st.markdown("## Export Loaded Model Card to Hub")
-        with st.form("Upload to 🤗 Hub"):
-            st.markdown("Use a token with write access from [here](https://hf.co/settings/tokens)")
-            token = st.text_input("Token", type='password')
-            repo_id = st.text_input("Repo ID")
-            submit = st.form_submit_button('Upload to 🤗 Hub', help='The current model card will be uploaded to a branch in the supplied repo ')
-
-        if submit:
-            if len(repo_id.split('/')) == 2:
-                repo_url = create_repo(repo_id, exist_ok=True, token=token)
-                new_url = card_upload(pj(),repo_id, token=token)
-                st.success(f"Pushed the card to the repo [here]({new_url})!") # note: was repo_url
-            else:
-                st.error("Repo ID invalid. It should be username/repo-name. For example: nateraw/food")
+    #     #########################################
+    #     ### Uploading model card to HUB
+    #     #########################################
+    #     out_markdown =open( st.session_state.markdown_upload, "r+"
+    #         ).read() 
+    #     print_out_final = f"{out_markdown}"
+    #     st.markdown("## Export Loaded Model Card to Hub")
+    #     with st.form("Upload to 🤗 Hub"):
+    #         st.markdown("Use a token with write access from [here](https://hf.co/settings/tokens)")
+    #         token = st.text_input("Token", type='password')
+    #         repo_id = st.text_input("Repo ID")
+    #         submit = st.form_submit_button('Upload to 🤗 Hub', help='The current model card will be uploaded to a branch in the supplied repo ')
+
+    #     if submit:
+    #         if len(repo_id.split('/')) == 2:
+    #             repo_url = create_repo(repo_id, exist_ok=True, token=token)
+    #             new_url = card_upload(pj(),repo_id, token=token)
+    #             st.success(f"Pushed the card to the repo [here]({new_url})!") # note: was repo_url
+    #         else:
+    #             st.error("Repo ID invalid. It should be username/repo-name. For example: nateraw/food")
         
 
-        #########################################
-        ### Download model card
-        #########################################
+    #     #########################################
+    #     ### Download model card
+    #     #########################################
 
 
-        st.markdown("## Download current Model Card")
+    #     st.markdown("## Download current Model Card")
 
-        if st.session_state.model_name is None or st.session_state.model_name== ' ':
-            downloaded_file_name = 'current_model_card.md'
-        else: 
-            downloaded_file_name = st.session_state.model_name+'_'+'model_card.md'
-        download_status = st.download_button(label = 'Download Model Card', data = pj(), file_name = downloaded_file_name, help = "The current model card will be downloaded as a markdown (.md) file")
-        if download_status == True:
-            st.success("Your current model card, successfully downloaded 🤗")
+    #     if st.session_state.model_name is None or st.session_state.model_name== ' ':
+    #         downloaded_file_name = 'current_model_card.md'
+    #     else: 
+    #         downloaded_file_name = st.session_state.model_name+'_'+'model_card.md'
+    #     download_status = st.download_button(label = 'Download Model Card', data = pj(), file_name = downloaded_file_name, help = "The current model card will be downloaded as a markdown (.md) file")
+    #     if download_status == True:
+    #         st.success("Your current model card, successfully downloaded 🤗")
 
 
 def page_switcher(page):