[INITIAL] add .gitignore, app.py and requirements.txt

.gitignore

@@ -0,0 +1,204 @@
+### macOS ###
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+### macOS Patch ###
+# iCloud generated files
+*.icloud
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+### Python Patch ###
+# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
+poetry.toml
+
+# ruff
+.ruff_cache/
+
+# LSP config files
+pyrightconfig.json

README.md

@@ -1,8 +1,8 @@
 ---
 title: Climate Plus Demo
-emoji: 💻
-colorFrom: red
-colorTo: gray
+emoji: 🌦️
+colorFrom: yellow
+colorTo: blue
 sdk: streamlit
 sdk_version: 1.19.0
 app_file: app.py
@@ -10,4 +10,4 @@ pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at <https://huggingface.co/docs/hub/spaces-config-reference>

app.py

@@ -0,0 +1,133 @@
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+import streamlit as st
+import pandas as pd
+
+model1 = AutoModelForSequenceClassification.from_pretrained(
+    "rexarski/bert-base-climate-fever-fixed"
+)
+tokenizer1 = AutoTokenizer.from_pretrained(
+    "rexarski/bert-base-climate-fever-fixed"
+)
+
+label_mapping1 = ["SUPPORTS", "REFUTES", "NOT_ENOUGH_INFO"]
+
+model2 = AutoModelForSequenceClassification.from_pretrained(
+    "rexarski/distilroberta-tcfd-disclosure"
+)
+
+tokenizer2 = AutoTokenizer.from_pretrained("distilroberta-base")
+
+label_mapping2 = [
+    "Governance a)",
+    "Governance b)",
+    "Metrics and Targets a)",
+    "Metrics and Targets b)",
+    "Metrics and Targets c)",
+    "Risk Management a)",
+    "Risk Management b)",
+    "Risk Management c)",
+    "Strategy a)",
+    "Strategy b)",
+    "Strategy c)",
+]
+
+
+def factcheck(text1, text2):
+    features = tokenizer1(
+        [text1],
+        [text2],
+        padding="max_length",
+        truncation=True,
+        return_tensors="pt",
+        max_length=512,
+    )
+
+    model1.eval()
+    with torch.no_grad():
+        scores = model1(**features).logits
+        labels = [
+            label_mapping1[score_max] for score_max in scores.argmax(dim=1)
+        ]
+    return labels[0]
+
+
+def tcfd_classify(text):
+    features = tokenizer2(
+        text,
+        padding="max_length",
+        truncation=True,
+        return_tensors="pt",
+        max_length=512,
+    )
+
+    model2.eval()
+    with torch.no_grad():
+        scores = model2(**features).logits
+        labels = [
+            label_mapping2[score_max] for score_max in scores.argmax(dim=1)
+        ]
+    return labels[0]
+
+
+data1 = {
+    "example": ["Example 1", "Example 2", "Example 3", "Example 4"],
+    "claim": [
+        "Sea ice has diminished much faster than scientists and climate models anticipated.",
+        "Climate Models Have Overestimated Global Warming",
+        "Climate skeptics argue temperature records have been adjusted in recent years to make the past appear cooler and the present warmer, although the Carbon Brief showed that NOAA has actually made the past warmer, evening out the difference.",
+        "Humans are too insignificant to affect global climate.",
+    ],
+    "evidence": [
+        "Past models have underestimated the rate of Arctic shrinkage and underestimated the rate of precipitation increase.",
+        """The 2017 United States-published National Climate Assessment notes that "climate models may still be underestimating or missing relevant feedback processes".""",
+        """Reconstructions have consistently shown that the rise in the instrumental temperature record of the past 150 years is not matched in earlier centuries, and the name "hockey stick graph" was coined for figures showing a long-term decline followed by an abrupt rise in temperatures.""",
+        "Human impact on the environment or anthropogenic impact on the environment includes changes to biophysical environments and ecosystems, biodiversity, and natural resources caused directly or indirectly by humans, including global warming, environmental degradation (such as ocean acidification), mass extinction and biodiversity loss, ecological crisis, and ecological collapse.",
+    ],
+    "label": ["SUPPORTS", "SUPPORTS", "NOT_ENOUGH_INFO", "REFUTES"],
+}
+
+data2 = {
+    "example": ["Example 1", "Example 2", "Example 3", "Example 4"],
+    "text": [
+        "As a global provider of transport and logistics services, we are often called on for expert input and industry insights by government representatives.",
+        "There are no sentences in the provided excerpts that disclose Scope 1 and Scope 2, and, if appropriate Scope 3 GHG emissions. The provided excerpts focus on other metrics and targets related to social impact investing, assets under management, and carbon footprint calculations.",
+        """Our strategy needs to be resilient under a range of climate-related scenarios. This year we have undertaken climate-related scenario testing of a select group of customers in the thermal coal supply chain. We assessed these customers using two of the International Energy Agency’s scenarios; the ‘New Policies Scenario’ and the ‘450 Scenario’. Our reporting reflects the Financial Stability Board’s (FSB) Task Force on Climate-Related Disclosures (TCFD) recommendations. Using the FSB TCFD’s disclosure framework, we have begun discussions with some of our customers in emissions-intensive industries. The ESG Committee is responsible for reviewing and approving our climate change-related objectives, including goals and targets. The Board Risk Committee has formal responsibility for the overview of ANZ’s management of new and emerging risks, including climate change-related risks.""",
+        "AXA created a Group-level Responsible Investment Committee (RIC), chaired by the Group Chief Investment Officer, and including representatives from AXA Asset Management entities, representatives of Corporate Responsibility (CR), Risk Management and Group Communication.",
+    ],
+    "label": [
+        "Risk Management a)",
+        "Metrics and Targets b)",
+        "Strategy c)",
+        "Goverance b)",
+    ],
+}
+
+df1 = pd.DataFrame(data1)
+df2 = pd.DataFrame(data2)
+
+
+st.markdown("## Factchecking")
+
+ex1_selected = st.selectbox(
+    "Select a climate claim-evidence pair", df1["example"]
+)
+selected_row1 = df1[df1["example"] == ex1_selected]
+
+st.markdown(f'**Claim**: {selected_row1["claim"].values[0]}')
+st.markdown(f'**Evidence**: {selected_row1["evidence"].values[0]}')
+st.markdown(f'**Label**: {selected_row1["label"].values[0]}')
+st.markdown("---")
+st.markdown(
+    f'**Prediction**: {factcheck(selected_row1["claim"].values[0], selected_row1["evidence"].values[0])}'
+)
+
+st.markdown("## TCFD disclosure classification")
+
+ex2_selected = st.selectbox("Select a TCFD disclosure example", df2["example"])
+selected_row2 = df2[df2["example"] == ex2_selected]
+
+st.markdown(f'**Text**: {selected_row2["text"].values[0]}')
+st.markdown(f'**Label**: {selected_row2["label"].values[0]}')
+st.markdown("---")
+st.markdown(f'**Prediction**: {tcfd_classify(selected_row2["text"].values[0])}')

requirements.txt

@@ -0,0 +1,5 @@
+torch
+transformers
+pandas
+numpy
+streamlit