form page

1_📝_form.py

@@ -1,372 +1,366 @@
-# from yaml import load
-from persist import persist, load_widget_state
-import streamlit as st
-from io import StringIO 
-import tempfile
-from pathlib import Path
-import requests
-from huggingface_hub import hf_hub_download, upload_file
-import pandas as pd
-from huggingface_hub import create_repo
-import os
-from middleMan import parse_into_jinja_markdown as pj
-
-import requests
-
-@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
-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]
-    license_map = pd.Series(
-        license_df["License identifier (to use in repo card)"].values, index=license_df.Fullname
-    ).to_dict()
-
-    available_metrics = [x['id'] for x in requests.get('https://huggingface.co/api/metrics').json()]
-
-    r = requests.get('https://huggingface.co/api/models-tags-by-type')
-    tags_data = r.json()
-    libraries = [x['id'] for x in tags_data['library']]
-    tasks = [x['id'] for x in tags_data['pipeline_tag']]
-
-    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):
-    #commit_message=None,
-    repo_type = "space"
-    commit_description=None,
-    revision=None,
-    create_pr=None
-    with tempfile.TemporaryDirectory() as tmpdir:
-        tmp_path = Path(tmpdir) / "README.md"
-        tmp_path.write_text(str(card_info))
-        url = upload_file(
-                    path_or_fileobj=str(tmp_path),
-                    path_in_repo="README.md",
-                    repo_id=repo_id,
-                    token=token,
-                    repo_type=repo_type,
-                    identical_ok=True,
-                    revision=revision,
-                )
-    return url
-
-def validate(self, repo_type="model"):
-    """Validates card against Hugging Face Hub's model card validation logic.
-    Using this function requires access to the internet, so it is only called
-    internally by `modelcards.ModelCard.push_to_hub`.
-    Args:
-        repo_type (`str`, *optional*):
-            The type of Hugging Face repo to push to. Defaults to None, which will use
-            use "model". Other options are "dataset" and "space".
-    """
-    if repo_type is None:
-        repo_type = "model"
-
-    # TODO - compare against repo types constant in huggingface_hub if we move this object there.
-    if repo_type not in ["model", "space", "dataset"]:
-        raise RuntimeError(
-            "Provided repo_type '{repo_type}' should be one of ['model', 'space',"
-            " 'dataset']."
-        )
-
-    body = {
-        "repoType": repo_type,
-        "content": str(self),
-    }
-    headers = {"Accept": "text/plain"}
-
-    try:
-        r = requests.post(
-            "https://huggingface.co/api/validate-yaml", body, headers=headers
-        )
-        r.raise_for_status()
-    except requests.exceptions.HTTPError as exc:
-        if r.status_code == 400:
-            raise RuntimeError(r.text)
-        else:
-            raise exc
-
-
-## Save uploaded [markdown] file to directory to be used by jinja parser function 
-def save_uploadedfile(uploadedfile):
-    with open(os.path.join("temp_uploaded_filed_Dir",uploadedfile.name),"wb") as f:
-        f.write(uploadedfile.getbuffer())
-    st.success("Saved File:{} to temp_uploaded_filed_Dir".format(uploadedfile.name))
-    return uploadedfile.name 
-
-
-def main_page():
-
-    		
-    if "model_name" not in st.session_state:
-        # Initialize session state.
-        st.session_state.update({
-            "input_model_name": "",
-            "languages": [],
-            "license": "",
-            "library_name": "",
-            "datasets": "",
-            "metrics": [],
-            "task": "",
-            "tags": "",
-            "model_description": "Some cool model...",
-            "the_authors":"",
-            "Shared_by":"",
-            "Model_details_text": "",
-            "Model_developers": "",
-            "blog_url":"",
-            "Parent_Model_url":"",
-            "Parent_Model_name":"",
-
-            "Model_how_to": "",
-            
-            "Model_uses": "",
-            "Direct_Use": "",
-            "Downstream_Use":"",
-            "Out-of-Scope_Use":"",
-
-            "Model_Limits_n_Risks": "",
-            "Recommendations":"",
-
-            "training_Data": "",
-            "model_preprocessing":"",
-            "Speeds_Sizes_Times":"",
-
-
-
-            "Model_Eval": "",
-            "Testing_Data":"",
-            "Factors":"",
-            "Metrics":"",
-            "Model_Results":"",
-
-            "Model_c02_emitted": "",
-            "Model_hardware":"",
-            "hours_used":"",
-            "Model_cloud_provider":"",
-            "Model_cloud_region":"",
-
-            "Model_cite": "",
-            "paper_url": "",
-            "github_url": "",
-            "bibtex_citation": "",
-            "APA_citation":"",
-
-            "Model_examin":"",
-            "Model_card_contact":"",
-            "Model_card_authors":"",
-            "Glossary":"",
-            "More_info":"",
-
-            "Model_specs":"",
-            "compute_infrastructure":"",
-            "technical_specs_software":"",
-
-            "check_box": bool,
-            "markdown_upload":" ",
-            "legal_view":bool,
-            "researcher_view":bool,
-            "beginner_technical_view":bool,
-            "markdown_state":"",
-        })
-    ## getting cache for each warnings 
-    languages_map, license_map, available_metrics, libraries, tasks, icd_map, treatment_mod = get_cached_data()
-
-    ## form UI setting 
-    st.header("Model basic information (Dose prediction)")
-
-    warning_placeholder = st.empty()
-
-    st.text_input("Model Name", key=persist("model_name"))
-    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.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.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
-    task = st.session_state.task or None
-    markdown_upload = st.session_state.markdown_upload
-    #uploaded_model_card = st.session_state.uploaded_model 
-    # Handle any warnings...
-    do_warn = False
-    warning_msg = "Warning: The following fields are required but have not been filled in: "
-    if not languages:
-        warning_msg += "\n- Languages"
-        do_warn = True
-    if not license:
-        warning_msg += "\n- License"
-        do_warn = True
-    if not task or not markdown_upload:
-        warning_msg += "\n- Please choose a task or upload a model card"
-        do_warn = True
-    if do_warn:
-        warning_placeholder.error(warning_msg)
-
-    with st.sidebar:
-
-        ######################################################
-        ### Uploading a model card from local drive
-        ######################################################
-        st.markdown("## Upload Model Card")
-       
-        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:
-           
-            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
-
-        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:
-            
-            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")
-        
-
-        #########################################
-        ### Download 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 🤗")
-
-
-def page_switcher(page):
-    st.session_state.runpage = page    
-
-def main():
-    
-    st.header("About Model Cards")
-    st.markdown(Path('about.md').read_text(), unsafe_allow_html=True)
-    btn = st.button('Create a Model Card 📝',on_click=page_switcher,args=(main_page,))
-    if btn:
-        st.experimental_rerun() # rerun is needed to clear the page
-
-if __name__ == '__main__':
-    load_widget_state()
-    if 'runpage' not in st.session_state :
-        st.session_state.runpage = main
-    st.session_state.runpage()
+# from yaml import load
+from persist import persist, load_widget_state
+import streamlit as st
+from io import StringIO 
+import tempfile
+from pathlib import Path
+import requests
+from huggingface_hub import hf_hub_download, upload_file
+import pandas as pd
+from huggingface_hub import create_repo
+import os
+from middleMan import parse_into_jinja_markdown as pj
+
+import requests
+
+@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
+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]
+    license_map = pd.Series(
+        license_df["License identifier (to use in repo card)"].values, index=license_df.Fullname
+    ).to_dict()
+
+    available_metrics = [x['id'] for x in requests.get('https://huggingface.co/api/metrics').json()]
+
+    r = requests.get('https://huggingface.co/api/models-tags-by-type')
+    tags_data = r.json()
+    libraries = [x['id'] for x in tags_data['library']]
+    tasks = [x['id'] for x in tags_data['pipeline_tag']]
+
+    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):
+    #commit_message=None,
+    repo_type = "space"
+    commit_description=None,
+    revision=None,
+    create_pr=None
+    with tempfile.TemporaryDirectory() as tmpdir:
+        tmp_path = Path(tmpdir) / "README.md"
+        tmp_path.write_text(str(card_info))
+        url = upload_file(
+                    path_or_fileobj=str(tmp_path),
+                    path_in_repo="README.md",
+                    repo_id=repo_id,
+                    token=token,
+                    repo_type=repo_type,
+                    # identical_ok=True,
+                    revision=revision,
+                )
+    return url
+
+def validate(self, repo_type="model"):
+    """Validates card against Hugging Face Hub's model card validation logic.
+    Using this function requires access to the internet, so it is only called
+    internally by `modelcards.ModelCard.push_to_hub`.
+    Args:
+        repo_type (`str`, *optional*):
+            The type of Hugging Face repo to push to. Defaults to None, which will use
+            use "model". Other options are "dataset" and "space".
+    """
+    if repo_type is None:
+        repo_type = "model"
+
+    # TODO - compare against repo types constant in huggingface_hub if we move this object there.
+    if repo_type not in ["model", "space", "dataset"]:
+        raise RuntimeError(
+            "Provided repo_type '{repo_type}' should be one of ['model', 'space',"
+            " 'dataset']."
+        )
+
+    body = {
+        "repoType": repo_type,
+        "content": str(self),
+    }
+    headers = {"Accept": "text/plain"}
+
+    try:
+        r = requests.post(
+            "https://huggingface.co/api/validate-yaml", body, headers=headers
+        )
+        r.raise_for_status()
+    except requests.exceptions.HTTPError as exc:
+        if r.status_code == 400:
+            raise RuntimeError(r.text)
+        else:
+            raise exc
+
+
+## Save uploaded [markdown] file to directory to be used by jinja parser function 
+def save_uploadedfile(uploadedfile):
+    with open(os.path.join("temp_uploaded_filed_Dir",uploadedfile.name),"wb") as f:
+        f.write(uploadedfile.getbuffer())
+    st.success("Saved File:{} to temp_uploaded_filed_Dir".format(uploadedfile.name))
+    return uploadedfile.name 
+
+
+def main_page():
+
+    		
+    if "model_name" not in st.session_state:
+        # Initialize session state.
+        st.session_state.update({
+            "input_model_name": "",
+            "license": "",
+            "library_name": "",
+            "datasets": "",
+            "metrics": [],
+            "task": "",
+            "tags": "",
+            "model_description": "Some cool model...",
+            "the_authors":"",
+            "Shared_by":"",
+            "Model_details_text": "",
+            "Model_developers": "",
+            "blog_url":"",
+            "Parent_Model_url":"",
+            "Parent_Model_name":"",
+
+            "Model_how_to": "",
+            
+            "Model_uses": "",
+            "Direct_Use": "",
+            "Downstream_Use":"",
+            "Out-of-Scope_Use":"",
+
+            "Model_Limits_n_Risks": "",
+            "Recommendations":"",
+
+            "training_Data": "",
+            "model_preprocessing":"",
+            "Speeds_Sizes_Times":"",
+
+
+
+            "Model_Eval": "",
+            "Testing_Data":"",
+            "Factors":"",
+            "Metrics":"",
+            "Model_Results":"",
+
+            "Model_c02_emitted": "",
+            "Model_hardware":"",
+            "hours_used":"",
+            "Model_cloud_provider":"",
+            "Model_cloud_region":"",
+
+            "Model_cite": "",
+            "paper_url": "",
+            "github_url": "",
+            "bibtex_citation": "",
+            "APA_citation":"",
+
+            "Model_examin":"",
+            "Model_card_contact":"",
+            "Model_card_authors":"",
+            "Glossary":"",
+            "More_info":"",
+
+            "Model_specs":"",
+            "compute_infrastructure":"",
+            "technical_specs_software":"",
+
+            "check_box": bool,
+            "markdown_upload":" ",
+            "legal_view":bool,
+            "researcher_view":bool,
+            "beginner_technical_view":bool,
+            "markdown_state":"",
+        })
+    ## getting cache for each warnings 
+    languages_map, license_map, available_metrics, libraries, tasks, icd_map, treatment_mod = get_cached_data()
+
+    ## form UI setting 
+    st.header("Model basic information (Dose prediction)")
+
+    warning_placeholder = st.empty()
+
+    st.text_input("Model Name", key=persist("model_name"))
+    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 = int(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.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.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
+    task = st.session_state.task or None
+    markdown_upload = st.session_state.markdown_upload
+    #uploaded_model_card = st.session_state.uploaded_model 
+    # Handle any warnings...
+    do_warn = False
+    warning_msg = "Warning: The following fields are required but have not been filled in: "
+    if not license:
+        warning_msg += "\n- License"
+        do_warn = True
+    if do_warn:
+        warning_placeholder.error(warning_msg)
+
+    with st.sidebar:
+
+        ######################################################
+        ### Uploading a model card from local drive
+        ######################################################
+        st.markdown("## Upload Model Card")
+       
+        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:
+           
+            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
+
+        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':
+            print("YO",st.session_state.task)
+            st.session_state.markdown_upload = "language_model_template1.md" ## language model template
+        
+        elif st.session_state.task:
+            
+            st.session_state.markdown_upload =  "current_card.md" ## default non language model template
+        print("st.session_state.markdown_upload",st.session_state.markdown_upload)
+        #########################################
+        ### 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 = "repo"#create_repo(repo_id, exist_ok=True, token=token)
+                print("repo_url",repo_url)
+                card_info = pj()
+                print(card_info)
+                new_url = card_upload(card_info,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
+        #########################################
+
+
+        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 🤗")
+
+
+def page_switcher(page):
+    st.session_state.runpage = page    
+
+def main():
+    
+    st.header("About Model Cards")
+    st.markdown(Path('about.md').read_text(), unsafe_allow_html=True)
+    btn = st.button('Create a Model Card 📝',on_click=page_switcher,args=(main_page,))
+    if btn:
+        st.experimental_rerun() # rerun is needed to clear the page
+
+if __name__ == '__main__':
+    load_widget_state()
+    if 'runpage' not in st.session_state :
+        st.session_state.runpage = main
+    st.session_state.runpage()