Version demo ready

1_📝_form.py

@@ -9,6 +9,7 @@ from huggingface_hub import hf_hub_download, upload_file
 import pandas as pd
 from huggingface_hub import create_repo
 import os
+from datetime import date
 from middleMan import parse_into_jinja_markdown as pj
 
 import requests
@@ -95,6 +96,27 @@ def card_upload(card_info,repo_id,token):
                 )
     return url
 
+def images_upload(images_list,repo_id,token):
+    repo_type = "model"
+    commit_description=None,
+    revision=None
+    create_pr=None
+    for img in images_list:
+        if img is not None:
+            with tempfile.TemporaryDirectory() as tmpdir:
+                tmp_path = Path(tmpdir) / "README.md"
+                tmp_path.write_text(str(img))
+                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
@@ -134,84 +156,55 @@ def validate(self, repo_type="model"):
 
 ## 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:
+    with open(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():
-
+    today=date.today()
     		
     if "model_name" not in st.session_state:
         # Initialize session state.
         st.session_state.update({
-            "input_model_name": "",
+            # Model Basic Information
+            "model_version": 0,
+            "icd10": [],
+            "treatment_modality": [],
+            "prescription_levels": [],
+            "additional_information": "",
+            "motivation": "",
+            "model_class":"",
+            "creation_date": today,
+            "architecture": "",
+            "model_developers": "",
+            "funded_by":"",
+            "shared_by":"",
             "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":"",
+            "finetuned_from": "",
+            "research_paper": "",
+            "git_repo": "",
+            # Technical Specifications
+            "nb_parameters": 5,
+            "input_channels": [],
+            "loss_function": "",
+            "batch_size": 1,
+            "patch_dimension": [],
+            "architecture_filename":None,
+            "libraries":[],
+            "hardware": "",
+            "inference_time": 10,
+            "get_started_code": "",
+            # Training Details
+            "training_set_size":10,
+            "validation_set_size":10,
+            "age_fig_filename":"",
+            "sex_fig_filename":"",
+            "dataset_source": "",
+            "acquisition_from": today,
+            "acquisition_to": today,
+            "markdown_upload": ""
         })
     ## getting cache for each warnings 
     languages_map, license_map, available_metrics, libraries, tasks, icd_map, treatment_mod = get_cached_data()
@@ -222,11 +215,11 @@ def main_page():
     warning_placeholder = st.empty()
 
     st.text_input("Model Name", key=persist("model_name"))
-    st.number_input("Version",key=persist("version"),step=0.1)
+    st.number_input("Version",key=persist("model_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.multiselect("Treatment site ICD10",list(icd_map), help="Reference ICD10 WHO: https://icd.who.int/icdapi",key=persist("icd10"))
+    right.multiselect("Treatment modality", list(treatment_mod), help="Reference LOINC Modality Radiation treatment: https://loinc.org/21964-2", key=persist("treatment_modality"))
     left, right = st.columns(2)
     nlines = int(left.number_input("Number of prescription levels", 0, 20, 1))
     # cols = st.columns(ncol)
@@ -234,7 +227,7 @@ def main_page():
         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.text_area("Class", placeholder="RULE 11, FROM MDCG 2021-24", key=persist('model_class'))
     st.date_input("Creation date", key=persist('creation_date'))
     st.text_area("Type of architecture",value="UNet", key=persist('architecture'))
 
@@ -244,13 +237,12 @@ def main_page():
     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.text_area("Funded by", key=persist('funded_by'))
+    st.text_area("Shared by", key=persist('shared_by'))
     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.text_area("Fine tuned from model", key=persist('finetuned_from'))
+    st.text_area("Related Research Paper", help="Research paper related to this model.", key=persist("research_paper"))
+    st.text_input("Related GitHub Repository", help="Link to a GitHub repository used in the development of this model", key=persist("git_repo"))
     # 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"))
@@ -269,7 +261,7 @@ def main_page():
     # warnings setting
     # languages=st.session_state.languages or None
     license=st.session_state.license or None
-    task = st.session_state.task or None
+    task = None #st.session_state.task or None
     markdown_upload = st.session_state.markdown_upload
     #uploaded_model_card = st.session_state.uploaded_model 
     # Handle any warnings...
@@ -293,8 +285,6 @@ def main_page():
         # 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
@@ -323,10 +313,8 @@ def main_page():
         if submit:
             if len(repo_id.split('/')) == 2:
                 repo_url = 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)
+                new_url = card_upload(pj(),repo_id, token=token)
+                # images_upload([st.session_state['architecture_filename'], st.session_state["age_fig_filename"], st.session_state["sex_fig_filename"]],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")

current_card.md

@@ -1,224 +1,83 @@
 ---
 language: 
   - en
-license: openrail
 ---
 
-# {{ model_id }}
+# {{ model_name }}
 
 <!--> Provide a quick summary of what the model is/does. <!-->
 
 #  Table of Contents
 
-- [{{ model_id }}](#-model_id-)
+- [{{ model_name }}](#-model_name)
 - [Table of Contents](#table-of-contents)
-- [Model Details](#model-details)
-  - [Model Description](#model-description)
-- [Uses](#uses)
-  - [Direct Use](#direct-use)
-  - [Downstream Use [Optional]](#downstream-use-optional)
-  - [Out-of-Scope Use](#out-of-scope-use)
-- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
-  - [Recommendations](#recommendations)
-- [Training Details](#training-details)
-  - [Training Data](#training-data)
-  - [Training Procedure](#training-procedure)
-    - [Preprocessing](#preprocessing)
-    - [Speeds, Sizes, Times](#speeds-sizes-times)
-- [Evaluation](#evaluation)
-  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
-    - [Testing Data](#testing-data)
-    - [Factors](#factors)
-    - [Metrics](#metrics)
-  - [Results](#results)
-- [Model Examination](#model-examination)
-- [Environmental Impact](#environmental-impact)
-- [Technical Specifications [optional]](#technical-specifications-optional)
-  - [Model Architecture and Objective](#model-architecture-and-objective)
-  - [Compute Infrastructure](#compute-infrastructure)
-    - [Hardware](#hardware)
-    - [Software](#software)
-- [Citation](#citation)
-- [Glossary [optional]](#glossary-optional)
-- [More Information [optional]](#more-information-optional)
-- [Model Card Authors [optional]](#model-card-authors-optional)
-- [Model Card Contact](#model-card-contact)
-- [How to Get Started with the Model](#how-to-get-started-with-the-model)
-
-
-# Model Details
-
-## Model Description
+- [1. Model Basic Information](#model-basic-information)
+- [2. Technical Specifications](#technical-specifications)
+- [3. Training Details](#training-details)
+- [4. Model Evaluation](#model-evaluation)
+- [5. Model Examination](#model-examination)
 
-<!--> This section provides basic information about what the model is, its current status, and where it came from.. <!-->
-{{ the_model_description | default("More information needed", true)}}
 
-- **Developed by:** {{ developers | default("More information needed", true)}}
-- **Shared by [Optional]:** {{ shared_by | default("More information needed", true)}}
-- **Model type:** Language model
-- **Language(s) (NLP):** {{ language | default("More information needed", true)}}
+# 1. Model Basic Information
+<!--> This section provides basic information about what the model is, its current status, and where it came from.. <!-->
+- **Version:** {{ version | default("0")}}
+## 1.1. Intended use
+- **Treatment site ICD10:** {{ icd10 | default("More information needed", true) }}
+- **Treatment modality:** {{ treatment_modality | default("More information needed", true) }}
+- **Prescription levels [Gy]:** {{ prescription_levels | default("More information needed", true)}}
+- **Additional information:** {{ additional_information | default("More information needed", true)}}
+- **Motivation for development:** {{ motivation | default("More information needed", true)}}
+- **Class:** {{ model_class | default("More information needed", true)}}
+- **Creation date:** {{ creation_date | default("More information needed", true)}}
+- **Type of architecture** {{ architecture | default("More information needed", true)}}
+## 1.2. Development and deployment
+- **Developed by:** Name: {{ dev_name | default("More information needed", true)}}, Instutution: {{dev_institution | default("More information needed", true)}}, Email: {{ dev_email | default("More information needed", true)}}
+- **Funded by:** {{ funded_by | default("More information needed", true)}}
+- **Shared by:** {{ shared_by | default("More information needed", true)}}
 - **License:** {{ license | default("More information needed", true)}}
-- **Related Models:** {{ related_models | default("More information needed", true)}}
-    - **Parent Model:** {{ parent_model | default("More information needed", true)}}
-- **Resources for more information:** {{ more_resources | default("More information needed", true)}}
-
-# Uses
-
-<!--> Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. <!-->
-
-## Direct Use
-
-<!--> This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. <!-->
-
-{{ direct_use | default("More information needed", true)}}
-
-## Downstream Use [Optional]
-
-<!--> This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app <!-->
-
-{{ downstream_use | default("More information needed", true)}}
-
-## Out-of-Scope Use
-
-<!--> This section addresses misuse, malicious use, and uses that the model will not work well for. <!-->
-
-{{ out_of_scope_use | default("More information needed", true)}}
-
-# Bias, Risks, and Limitations
-
-<!--> This section is meant to convey both technical and sociotechnical limitations. <!-->
-
-{{ bias_risks_limitations | default("More information needed", true)}}
-
-## Recommendations
-
-<!--> This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. <!-->
-
-{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.", true)}}
-
-# Training Details
-
-## Training Data
-
-<!--> This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. <!-->
-
-{{ training_data | default("More information needed", true)}}
-
-## Training Procedure
-
-<!--> This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. <!-->
-
-### Preprocessing
-
-{{ preprocessing | default("More information needed", true)}}
-
-### Speeds, Sizes, Times
-
-<!--> This section provides information about throughput, start/end time, checkpoint size if relevant, etc. <!-->
-
-{{ speeds_sizes_times | default("More information needed", true)}}
- 
-# Evaluation
-
-<!--> This section describes the evaluation protocols and provides the results. <!-->
-
-## Testing Data, Factors & Metrics
-
-### Testing Data
-
-<!--> This should link to a Data Card if possible. <!-->
-
-{{ testing_data | default("More information needed", true)}}
-
-### Factors
-
-<!--> These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. <!-->
-
-{{ testing_factors | default("More information needed", true)}}
+- **Finetuned from model:** {{ finetuned_by | default("More information needed", true)}}
+- **Related Research Paper(s):** {{ research_paper | default("More information needed", true)}}
+- **Related Git Repository:** {{ git_repo | default("More information needed", true)}}
 
-### Metrics
+## 1.3. How to Get Started with the Model
 
-<!--> These are the evaluation metrics being used, ideally with a description of why. <!-->
-
-{{ testing_metrics | default("More information needed", true)}}
-
-## Results 
-
-{{ results | default("More information needed", true)}}
-
-# Model Examination
-
-{{ model_examination | default("More information needed", true)}}
-
-# Environmental Impact
-
-<!--> Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly <!-->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** {{ hardware | default("More information needed", true)}}
-- **Hours used:** {{ hours_used | default("More information needed", true)}}
-- **Cloud Provider:** {{ cloud_provider | default("More information needed", true)}}
-- **Compute Region:** {{ cloud_region | default("More information needed", true)}}
-- **Carbon Emitted:** {{ co2_emitted | default("More information needed", true)}}
-
-# Technical Specifications [optional]
-
-## Model Architecture and Objective
-
-{{ model_specs | default("More information needed", true)}}
-
-## Compute Infrastructure
-
-{{ compute_infrastructure | default("More information needed", true)}}
-
-### Hardware
-
-{{ hardware | default("More information needed", true)}}
-
-### Software
-
-{{ software | default("More information needed", true)}}
-
-# Citation
-
-<!--> If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. <!-->
-
-**BibTeX:**
-
-{{ citation_bibtex | default("More information needed", true)}}
-
-**APA:**
-
-{{ citation_apa | default("More information needed", true)}}
-
-# Glossary [optional]
-
-<!--> If relevant, include terms and calculations in this section that can help readers understand the model or model card. <!-->
-
-{{ glossary | default("More information needed", true)}}
-
-# More Information [optional]
+Use the code below to get started with the model.
+<details>
+<summary> Click to expand </summary>
 
-{{ more_information | default("More information needed", true)}}
+{{ get_started_code | default("More information needed", true)}}
 
-# Model Card Authors [optional]
+</details>
 
-{{ model_card_authors | default("More information needed", true)}}
+# 2. Technical Specifications
+## 2.1. Model architecture
+- **Total number of parameters:** {{ nb_parameters | default("More information needed", true)}}
+- **Input channels:** {{ input_channels | default("More information needed", true)}}
+- **Loss function** {{ loss_function | default("More information needed", true)}}
+- **Batch size** {{ batch_size | default("More information needed", true)}}
+- **Patch dimension** {{ patch_dimension | default("More information needed", true)}}
+<img width="100%" src="https://cdn-uploads.huggingface.co/production/uploads/65c9dbefd6cbf9dfed67367e/xGGUE5spRY6tar5R4VeKX.png" alt="error while loading image"> 
+_Figure 1: Model architecture_ 
 
-# Model Card Contact
+- **Libraries/Dependencies:** {{ libraries | default("More information needed", true)}}
+- **Hardware recommended:** {{ hardware | default("More information needed", true)}}
+- **Inference time for recommended [seconds]** {{ inference_time | default("More information needed", true)}}
 
-{{ model_card_contact | default("More information needed", true)}}
+# 3. Training Details
+- **Training set size:** {{ training_set_size | default("More information needed", true)}}
+- **Validation set size:** {{ validation_set_size | default("More information needed", true)}}
 
-# How to Get Started with the Model
+Age distribution | Sex distribution
+--- | ---
+![](age_distribution.png) | ![](sex_distribution.png)
 
-Use the code below to get started with the model.
 
-<details>
-<summary> Click to expand </summary>
 
-{{ get_started_code | default("More information needed", true)}}
+- **Dataset source:** {{ dataset_source | default("More information needed", true)}}
+- **Acquisition date** from {{ acquisition_from | default("More information needed", true)}} to {{ acquisition_to | default("More information needed", true)}} 
+# 4. Model Evaluation
 
-</details>
+# 5. Model Examination
+<!--> This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. <!-->
 
 

middleMan.py

@@ -23,30 +23,42 @@ def parse_into_jinja_markdown():
     # - parent model
     # to fix:
         # citation on form: check box for bibtex or apa: then parse 
-    return (temp.render(model_id = st.session_state["model_name"],
-        
-        the_model_description = st.session_state["model_description"],developers=st.session_state["Model_developers"],shared_by = st.session_state["Shared_by"],model_license = st.session_state['license'],
-        parent_model_link = st.session_state['Parent_Model_url'],
-            direct_use = st.session_state["Direct_Use"], downstream_use = st.session_state["Downstream_Use"],out_of_scope_use = st.session_state["Out-of-Scope_Use"],
-            bias_risks_limitations = st.session_state["Model_Limits_n_Risks"], bias_recommendations = st.session_state['Recommendations'],
-            model_examination = st.session_state['Model_examin'],
-            speeds_sizes_times = st.session_state['Speeds_Sizes_Times'],
-            hardware= st.session_state['Model_hardware'], hours_used = st.session_state['hours_used'], cloud_provider = st.session_state['Model_cloud_provider'], cloud_region = st.session_state['Model_cloud_region'], co2_emitted = st.session_state['Model_c02_emitted'],
-            citation_bibtex= st.session_state["APA_citation"], citation_apa = st.session_state['bibtex_citation'],
-            training_data = st.session_state['training_Data'], preprocessing =st.session_state['model_preprocessing'],
-            model_specs = st.session_state['Model_specs'], compute_infrastructure = st.session_state['compute_infrastructure'],software = st.session_state['technical_specs_software'],
-            glossary = st.session_state['Glossary'], 
-            more_information = st.session_state['More_info'], 
-            model_card_authors = st.session_state['the_authors'],
-            model_card_contact = st.session_state['Model_card_contact'],
-            get_started_code =st.session_state["Model_how_to"],
-            repo_link = st.session_state["github_url"],
-            paper_link = st.session_state["paper_url"],
-            blog_link = st.session_state["blog_url"],
-            testing_data = st.session_state["Testing_Data"],
-            testing_factors = st.session_state["Factors"],
-            results = st.session_state['Model_Results'],
-            testing_metrics = st.session_state["Metrics"]
+    return (temp.render(model_name = st.session_state["model_name"], model_version = st.session_state["model_version"],
+            icd10 = st.session_state["icd10"], treatment_modality = st.session_state["treatment_modality"], prescription_levels = st.session_state["prescription_levels"],
+            additional_information = st.session_state["additional_information"], motivation = st.session_state["motivation"], model_class = st.session_state["model_class"],
+            creation_date = st.session_state["creation_date"], architecture = st.session_state["architecture"],
+            model_developers=st.session_state["model_developers"],funded_by = st.session_state["funded_by"],shared_by = st.session_state["shared_by"],
+            license = st.session_state['license'],finetuned_from = st.session_state['finetuned_from'], research_paper = st.session_state["research_paper"], git_repo = st.session_state["git_repo"],
+            
+
+            nb_parameters = st.session_state["nb_parameters"], input_channels = st.session_state["input_channels"],
+            loss_function = st.session_state["loss_function"], batch_size = st.session_state["batch_size"], patch_dimension = st.session_state["patch_dimension"],
+            architecture_filename = st.session_state["architecture_filename"], libraries = st.session_state["libraries"], hardware = st.session_state["hardware"],
+            inference_time = st.session_state["inference_time"], get_started_code = st.session_state["get_started_code"],
+
+            training_set_size = st.session_state["training_set_size"], validation_set_size = st.session_state["validation_set_size"],
+            age_fig_filename = st.session_state["age_fig_filename"], sex_fig_filename = st.session_state["sex_fig_filename"],
+            dataset_source = st.session_state["dataset_source"], acquisition_from = st.session_state["acquisition_from"], acquisition_to = st.session_state["acquisition_to"], 
+            # direct_use = st.session_state["Direct_Use"], downstream_use = st.session_state["Downstream_Use"],out_of_scope_use = st.session_state["Out-of-Scope_Use"],
+            # bias_risks_limitations = st.session_state["Model_Limits_n_Risks"], bias_recommendations = st.session_state['Recommendations'],
+            # model_examination = st.session_state['Model_examin'],
+            # speeds_sizes_times = st.session_state['Speeds_Sizes_Times'],
+            # hardware= st.session_state['Model_hardware'], hours_used = st.session_state['hours_used'], cloud_provider = st.session_state['Model_cloud_provider'], cloud_region = st.session_state['Model_cloud_region'], co2_emitted = st.session_state['Model_c02_emitted'],
+            # citation_bibtex= st.session_state["APA_citation"], citation_apa = st.session_state['bibtex_citation'],
+            # training_data = st.session_state['training_Data'], preprocessing =st.session_state['model_preprocessing'],
+            # model_specs = st.session_state['Model_specs'], compute_infrastructure = st.session_state['compute_infrastructure'],software = st.session_state['technical_specs_software'],
+            # glossary = st.session_state['Glossary'], 
+            # more_information = st.session_state['More_info'], 
+            # model_card_authors = st.session_state['the_authors'],
+            # model_card_contact = st.session_state['Model_card_contact'],
+            # get_started_code =st.session_state["Model_how_to"],
+            # repo_link = st.session_state["github_url"],
+            # paper_link = st.session_state["paper_url"],
+            # blog_link = st.session_state["blog_url"],
+            # testing_data = st.session_state["Testing_Data"],
+            # testing_factors = st.session_state["Factors"],
+            # results = st.session_state['Model_Results'],
+            # testing_metrics = st.session_state["Metrics"]
             ))
 
 

pages/1_👀_Technical_Specifications.py

@@ -8,10 +8,11 @@ import requests
 # global variable_output
 
 
-
 def get_cached_data():
     # json.load(open('file_TG263.json'))
     struct_dict = {"Target":["GTV","CTV","PTV"],"Anatomy":["SpinalCord","BrainStem"]}
+    
+
     r = requests.get('https://huggingface.co/api/models-tags-by-type')
     tags_data = r.json()
     libraries = [x['id'] for x in tags_data['library']]
@@ -28,14 +29,14 @@ def cs_body():
     st.header('Technical Specifications')
     st.write("Provide an overview of any additional technical specifications for this model")
     st.markdown('##### Model architecture')
-    st.number_input("Total number of trainable parameters [million]",value=5)
+    st.number_input("Total number of trainable parameters [million]",value=5,key=persist("nb_parameters"))
     left, middle, right = st.columns(3)
     nlines = int(left.number_input("Input channels", 0, 20, 1))
     for i in range(nlines):
         type_input = middle.selectbox(f"Input type # {i}", list(struct_dict.keys()))
         right.selectbox("Input",struct_dict[type_input], help="From https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/acm2.12701")
-    st.text_input("Loss function",placeholder="MSE")
-    st.number_input("Batch size",value=1)
+    st.text_input("Loss function",placeholder="MSE", key=persist("loss_function"))
+    st.number_input("Batch size",value=1,key=persist("batch_size"))
     left, right = st.columns(2)
     nlines = int(left.number_input("Patch dimension", 2, 3, 3))
     # cols = st.columns(ncol)
@@ -45,10 +46,10 @@ def cs_body():
     if arch_fig is not None:
         st.image(arch_fig)
     
-    st.selectbox("Library/Dependencies", 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("Hardware recommended", placeholder="GPU 20Gb RAM", key=persist("Model_hardware"))
+    st.multiselect("Library/Dependencies", libraries, default=[libraries[0]], 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('libraries'))
+    st.text_input("Hardware recommended", placeholder="GPU 20Gb RAM", key=persist("hardware"))
     st.number_input("Inference time for recommended hardware [seconds]",value=10, key=persist("inference_time"))
-    st.text_area("Installation / Getting started", placeholder="Installation procedure / code to run inference", key=persist("Model_how_to"))
+    st.text_area("Installation / Getting started", placeholder="Installation procedure / code to run inference", key=persist("get_started_code"))
 
 
         

pages/2_🏋️‍♀️_Model_training.py

@@ -31,8 +31,10 @@ def cs_body():
         
         fig, ax = plt.subplots()
         ax.set_title("Age distribution")
-        ax.hist(np.random.normal(size=500))
-        left.pyplot(fig)
+        ax.hist(np.random.normal(loc=40,scale=4.0,size=500))
+        age = left.pyplot(fig)
+
+        
 
         fig, ax = plt.subplots()
         ax.pie([45,55],labels=["Men","Women"])