Upload 5 files

Welcome.py

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+# Importing necessary libraries
+import streamlit as st
+import cv2
+import json
+import numpy as np
+import threading 
+import time
+import pandas as pd
+import mediapipe as mp
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import streamlit.components.v1 as com
+from mediapipe_functions import *
+import tempfile
+from streamlit_lottie import st_lottie
+
+st.set_page_config(
+    page_title="Welcome to iSLR",
+    page_icon="👋",
+)
+
+# st.markdown("""
+# <style>
+# .css-9s5bis.edgvbvh3
+# {
+#     visibility:hidden
+# }
+# .css-h5rgaw.egzxvld1
+# {
+#     visibility:hidden
+# }
+# </style>
+# """, unsafe_allow_html=True)
+
+st.sidebar.success("Select a demo above.")
+
+st.title("ASL Sign Language Recognition App")
+
+f=st.file_uploader("Please upload a video of a demo of ASL sign", type=['mp4'])
+
+if f is not None:
+    tfile = tempfile.NamedTemporaryFile(delete=False) 
+    tfile.write(f.read())
+    
+    st.sidebar.video(tfile.name)
+    cap = cv2.VideoCapture(tfile.name)
+    stframe = st.empty()
+    
+    # for local video file
+    # cap = cv2.VideoCapture('videos\goodbye.mp4')
+    
+    final_landmarks=[]
+    with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+            image, results = mediapipe_detection(frame, holistic)
+            draw(image, results)
+            landmarks = extract_coordinates(results)
+            final_landmarks.extend(landmarks)
+    
+    df1 = pd.DataFrame(final_landmarks,columns=['x','y','z'])
+    
+    # Necessary functions 
+    ROWS_PER_FRAME = 543
+    def load_relevant_data_subset(df):
+        data_columns = ['x', 'y', 'z']
+        data = df
+        n_frames = int(len(data) / ROWS_PER_FRAME)
+        data = data.values.reshape(n_frames, ROWS_PER_FRAME, len(data_columns))
+        return data.astype(np.float32)
+    
+    # Loading data
+    test_df = load_relevant_data_subset(df1)
+    test_df = tf.convert_to_tensor(test_df)
+    
+    # Inference
+    interpreter = tf.lite.Interpreter("code\model.tflite")
+    prediction_fn = interpreter.get_signature_runner("serving_default")
+    output = prediction_fn(inputs=test_df)
+    sign = np.argmax(output["outputs"])
+    
+    sign_json=pd.read_json("code\sign_to_prediction_index_map.json",typ='series')
+    sign_df=pd.DataFrame(sign_json)
+    sign_df.iloc[sign]
+    top_indices = np.argsort(output['outputs'])[::-1][:5]
+    top_values = output['outputs'][top_indices]
+    
+    output_df = sign_df.iloc[top_indices]
+    output_df['Value'] = top_values
+    output_df.rename(columns = {0:'Index'}, inplace = True)
+    st.write(output_df)
+# callbacks : on_change, on_click
+# com.iframe("https://embed.lottiefiles.com/animation/132349")
+with open('assets\\animations\\14592-loader-cat.json') as source:
+    animation=json.load(source)
+st_lottie(animation, width=300)

mediapipe_functions.py

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+import cv2
+import mediapipe as mp
+import numpy as np
+
+mp_holistic = mp.solutions.holistic # holistic model
+mp_drawing = mp.solutions.drawing_utils # drawing utilities
+
+def mediapipe_detection(image, model):
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # color conversion
+    image.flags.writeable = False # img no longer writeable
+    landmarks = model.process(image) # make landmark prediction
+    image.flags.writeable = True  # img now writeable
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # color reconversion
+    return image, landmarks
+
+def draw(image, results):
+    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION,
+                              mp_drawing.DrawingSpec(color=(0,0,255), thickness=3, circle_radius=3),
+                              mp_drawing.DrawingSpec(color=(0,0,0), thickness=1, circle_radius=0))
+    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS,
+                              mp_drawing.DrawingSpec(color=(0,150,0), thickness=3, circle_radius=3),
+                              mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius=2))
+    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS,
+                              mp_drawing.DrawingSpec(color=(200,56,12), thickness=3, circle_radius=3),
+                              mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius=2))
+    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS,
+                              mp_drawing.DrawingSpec(color=(250,56,12), thickness=3, circle_radius=3),
+                              mp_drawing.DrawingSpec(color=(0,0,0), thickness=2, circle_radius=2))
+    
+def extract_coordinates(results):
+    face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]) if results.face_landmarks else np.empty((468, 3))*np.nan
+    pose = np.array([[res.x, res.y, res.z] for res in results.pose_landmarks.landmark]) if results.pose_landmarks else np.empty((33, 3))*np.nan
+    lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]) if results.left_hand_landmarks else np.empty((21, 3))*np.nan
+    rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]) if results.right_hand_landmarks else np.empty((21, 3))*np.nan
+    return np.concatenate([face, lh, pose, rh])
+
+def extract_landmarks(frames):
+    final_landmarks = []
+    with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:
+        for frame in frames:
+            image, results = mediapipe_detection(frame, holistic)
+            draw(image, results)
+            landmarks = extract_coordinates(results)
+            final_landmarks.extend(landmarks)
+    return final_landmarks

requirements.txt

@@ -0,0 +1,780 @@
+absl-py==1.4.0
+accelerate==0.12.0
+access==1.1.8
+affine==2.4.0
+aiobotocore==2.4.2
+aiohttp @ file:///home/conda/feedstock_root/build_artifacts/aiohttp_1663850771047/work
+aiohttp-cors==0.7.0
+aioitertools==0.11.0
+aiorwlock==1.3.0
+aiosignal @ file:///home/conda/feedstock_root/build_artifacts/aiosignal_1667935791922/work
+albumentations==1.3.0
+alembic==1.9.4
+altair==4.2.2
+annoy==1.17.1
+ansiwrap==0.8.4
+anyio @ file:///home/conda/feedstock_root/build_artifacts/anyio_1666191106763/work/dist
+apache-beam==2.44.0
+aplus==0.11.0
+appdirs==1.4.4
+argon2-cffi @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi_1640817743617/work
+argon2-cffi-bindings @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi-bindings_1649500320262/work
+arrow==1.2.3
+arviz==0.12.1
+astroid==2.14.2
+astropy==4.3.1
+astunparse==1.6.3
+async-timeout @ file:///home/conda/feedstock_root/build_artifacts/async-timeout_1640026696943/work
+asynctest==0.13.0
+atpublic==2.3
+attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1671632566681/work
+audioread==3.0.0
+autocfg==0.0.8
+autopep8==1.6.0
+aws-requests-auth==0.4.3
+Babel==2.11.0
+backcall @ file:///home/conda/feedstock_root/build_artifacts/backcall_1592338393461/work
+backoff==1.10.0
+backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1618230623929/work
+backports.zoneinfo==0.2.1
+bayesian-optimization==1.4.2
+bayespy==0.5.25
+beatrix-jupyterlab @ file:///home/kbuilder/miniconda3/conda-bld/dlenv-tf-2-11-cpu_1674530713176/work/beatrix_jupyterlab-2023.123.173907.tar.gz
+beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1649463573192/work
+bidict==0.22.1
+biopython==1.81
+blake3==0.2.1
+bleach==6.0.0
+blessed==1.19.1
+blis==0.7.9
+bokeh==2.4.3
+Boruta==0.3
+boto3==1.26.77
+botocore==1.27.59
+-e git+https://github.com/SohierDane/BigQuery_Helper@8615a7f6c1663e7f2d48aa2b32c2dbcb600a440f#egg=bq_helper
+bqplot==0.12.36
+branca==0.6.0
+brewer2mpl==1.4.1
+brotlipy==0.7.0
+cached-property==1.5.2
+cachetools==4.2.4
+Cartopy @ file:///home/conda/feedstock_root/build_artifacts/cartopy_1630680835556/work
+catalogue==2.0.8
+catalyst==22.4
+catboost==1.1.1
+category-encoders==2.6.0
+certifi==2022.12.7
+cesium==0.10.1
+cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1666183775483/work
+cftime==1.6.2
+charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1661170624537/work
+chex==0.1.5
+cleverhans==4.0.0
+click==8.1.3
+click-plugins==1.1.1
+cligj==0.7.2
+cloud-tpu-client==0.10
+cloud-tpu-profiler==2.4.0
+cloudpickle==2.2.1
+cmaes==0.9.1
+cmake==3.25.0
+cmdstanpy==1.1.0
+cmudict==1.0.13
+colorama==0.4.6
+colorcet==3.0.1
+colorful==0.5.5
+colorlog==6.7.0
+colorlover==0.3.0
+conda==22.9.0
+conda-content-trust @ file:///tmp/build/80754af9/conda-content-trust_1617045594566/work
+conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1669907009957/work
+conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1669733752472/work
+confection==0.0.4
+contextily==1.3.0
+convertdate==2.4.0
+crcmod==1.7
+cryptography @ file:///home/conda/feedstock_root/build_artifacts/cryptography_1666563349571/work
+cufflinks==0.17.3
+CVXcanon==0.1.2
+cycler @ file:///home/conda/feedstock_root/build_artifacts/cycler_1635519461629/work
+cymem==2.0.7
+cysignals==1.11.2
+Cython==0.29.33
+cytoolz==0.12.1
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+daal4py==2021.6.3
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+datasets==2.1.0
+datashader==0.14.4
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+datatable==1.0.0
+datatile==1.0.3
+db-dtypes==1.0.5
+deap==1.3.3
+debugpy==1.6.6
+decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
+defusedxml @ file:///home/conda/feedstock_root/build_artifacts/defusedxml_1615232257335/work
+Delorean==1.0.0
+deprecat==2.1.1
+deprecation==2.1.0
+descartes==1.1.0
+dill==0.3.6
+dipy==1.6.0
+distlib==0.3.6
+distributed==2022.2.0
+dm-tree==0.1.8
+docker==6.0.1
+docker-pycreds==0.4.0
+docopt==0.6.2
+docstring-to-markdown==0.11
+docutils==0.19
+earthengine-api==0.1.342
+easydict==1.10
+easyocr==1.6.2
+ecos==2.0.12
+eli5==0.13.0
+emoji==2.2.0
+en-core-web-lg @ https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.5.0/en_core_web_lg-3.5.0-py3-none-any.whl
+en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0-py3-none-any.whl
+entrypoints @ file:///home/conda/feedstock_root/build_artifacts/entrypoints_1643888246732/work
+ephem==4.1.4
+esda==2.4.3
+essentia==2.1b6.dev858
+et-xmlfile==1.1.0
+etils==0.9.0
+exceptiongroup==1.1.0
+explainable-ai-sdk==1.3.3
+explainers @ https://storage.googleapis.com/explainability_sdk_wheel/explainers-1-cp37-cp37m-linux_x86_64.whl
+fastai==2.7.11
+fastapi==0.89.1
+fastavro==1.7.0
+fastcore==1.5.28
+fastdownload==0.0.7
+fasteners==0.18
+fastjsonschema @ file:///home/conda/feedstock_root/build_artifacts/python-fastjsonschema_1663619548554/work/dist
+fastprogress==1.0.3
+fasttext==0.9.2
+fbpca==1.0
+feather-format==0.4.1
+featuretools==1.11.1
+filelock==3.9.0
+Fiona==1.8.22
+fitter==1.5.2
+flake8==5.0.4
+flashtext==2.7
+Flask==2.2.3
+flatbuffers==23.1.21
+flax==0.6.4
+flit_core @ file:///home/conda/feedstock_root/build_artifacts/flit-core_1667734568827/work/source/flit_core
+folium==0.14.0
+fonttools @ file:///home/conda/feedstock_root/build_artifacts/fonttools_1666389892786/work
+frozendict==2.3.5
+frozenlist==1.3.3
+fsspec==2023.1.0
+funcy==1.18
+fury==0.8.0
+future==0.18.3
+fuzzywuzzy==0.18.0
+gast==0.4.0
+gatspy==0.3
+gcsfs==2023.1.0
+gensim==4.0.1
+geographiclib==2.0
+Geohash==1.0
+geojson==3.0.1
+geopandas==0.10.2
+geoplot==0.5.1
+geopy==2.3.0
+geoviews==1.9.6
+ggplot @ https://github.com/hbasria/ggpy/archive/0.11.5.zip
+giddy==2.3.3
+gitdb==4.0.10
+GitPython==3.1.30
+gluoncv==0.10.5.post0
+gluonnlp==0.10.0
+google-api-core==1.34.0
+google-api-python-client==2.79.0
+google-apitools==0.5.31
+google-auth==1.35.0
+google-auth-httplib2==0.1.0
+google-auth-oauthlib==0.4.6
+google-cloud-aiplatform @ git+https://github.com/googleapis/python-aiplatform.git@4ed7a50fef58d694ddb29d4240965d44e383da2b
+google-cloud-automl==1.0.1
+google-cloud-bigquery==2.2.0
+google-cloud-bigtable==1.7.3
+google-cloud-core==1.7.3
+google-cloud-datastore==1.15.5
+google-cloud-dlp==3.11.1
+google-cloud-language==2.6.1
+google-cloud-monitoring==2.14.1
+google-cloud-pubsub==2.14.0
+google-cloud-pubsublite==1.6.0
+google-cloud-recommendations-ai==0.7.1
+google-cloud-resource-manager==1.8.1
+google-cloud-spanner==3.27.0
+google-cloud-storage==1.44.0
+google-cloud-translate==3.8.4
+google-cloud-videointelligence==2.8.3
+google-cloud-vision==2.8.0
+google-crc32c==1.5.0
+google-pasta==0.2.0
+google-resumable-media==1.3.3
+googleapis-common-protos==1.58.0
+gplearn==0.4.2
+gpustat==1.0.0
+gpxpy==1.5.0
+graphviz==0.8.4
+greenlet==2.0.1
+grpc-google-iam-v1==0.12.6
+grpcio==1.51.1
+grpcio-status==1.48.2
+gviz-api==1.10.0
+gym==0.23.1
+gym-notices==0.0.8
+h11==0.14.0
+h2o==3.40.0.1
+h5py==3.8.0
+haversine==2.7.0
+hdfs==2.7.0
+HeapDict==1.0.1
+hep-ml==0.7.1
+hijri-converter==2.2.4
+hmmlearn==0.2.8
+holidays==0.19
+holoviews==1.15.4
+hpsklearn==0.1.0
+html5lib==1.1
+htmlmin==0.1.12
+httplib2==0.20.4
+httptools==0.5.0
+huggingface-hub==0.12.1
+humanize==4.6.0
+hunspell==0.5.5
+husl==4.0.3
+hydra-slayer==0.4.0
+hyperopt==0.2.7
+hypertools==0.8.0
+ibis-framework==2.1.1
+idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work
+igraph==0.10.4
+imagecodecs==2021.11.20
+ImageHash==4.3.1
+imageio==2.25.0
+imbalanced-learn==0.10.1
+imgaug==0.4.0
+implicit @ file:///home/conda/feedstock_root/build_artifacts/implicit_1643471602441/work
+importlib-metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1653252814274/work
+importlib-resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1672681417544/work
+inequality==1.0.0
+iniconfig==2.0.0
+ipydatawidgets==4.3.3
+ipykernel @ file:///home/conda/feedstock_root/build_artifacts/ipykernel_1666723258080/work
+ipyleaflet==0.17.2
+ipympl==0.7.0
+ipython==7.34.0
+ipython-genutils==0.2.0
+ipython-sql==0.4.1
+ipyvolume==0.6.0
+ipyvue==1.8.0
+ipyvuetify==1.8.4
+ipywebrtc==0.6.0
+ipywidgets==7.7.1
+isort==5.11.5
+isoweek==1.3.3
+itsdangerous==2.1.2
+Janome==0.4.2
+jax==0.3.25
+jaxlib==0.3.25
+jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1669134318875/work
+jieba==0.42.1
+Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1654302431367/work
+jmespath==1.0.1
+joblib==1.2.0
+json5==0.9.11
+jsonlines==1.2.0
+jsonschema @ file:///home/conda/feedstock_root/build_artifacts/jsonschema-meta_1669810440410/work
+jupyter-console==6.6.1
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+jupyter-server-proxy==3.2.2
+jupyter_client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1673615989977/work
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+kaggle-environments==1.12.0
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+keras-tuner==1.1.3
+kiwisolver @ file:///home/conda/feedstock_root/build_artifacts/kiwisolver_1657953088445/work
+kmapper==2.0.1
+kmodes==0.12.2
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+kornia==0.5.8
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+kubernetes==25.3.0
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+langid==1.1.6
+lazy-object-proxy==1.9.0
+lazy_loader==0.1
+learntools @ git+https://github.com/Kaggle/learntools@dd9a7f6495abbba9c7987c16365c8785fb646125
+leven==1.0.4
+Levenshtein==0.20.9
+libclang==15.0.6.1
+libmambapy @ file:///home/conda/feedstock_root/build_artifacts/mamba-split_1664905016189/work/libmambapy
+libpysal==4.7.0
+librosa==0.10.0
+lightfm==1.16
+lightgbm==3.3.2
+lightning-utilities==0.7.1
+lime==0.2.0.1
+line-profiler==4.0.2
+llvmlite==0.39.1
+lml==0.1.0
+locket==1.0.0
+LunarCalendar==0.0.9
+lxml==4.9.2
+lz4==4.3.2
+Mako==1.2.4
+mamba @ file:///home/conda/feedstock_root/build_artifacts/mamba-split_1664905016189/work/mamba
+mapclassify==2.5.0
+marisa-trie==0.7.8
+Markdown==3.4.1
+markdown-it-py==2.1.0
+markovify==0.9.4
+MarkupSafe @ file:///home/conda/feedstock_root/build_artifacts/markupsafe_1648737551960/work
+matplotlib @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-suite_1661439848456/work
+matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1660814786464/work
+matplotlib-venn==0.11.7
+matrixprofile @ git+https://github.com/matrix-profile-foundation/matrixprofile.git@6bea7d4445284dbd9700a097974ef6d4613fbca7
+mccabe==0.7.0
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+memory-profiler==0.61.0
+mercantile==1.2.1
+mgwr==2.1.2
+missingno==0.5.1
+mistune @ file:///home/conda/feedstock_root/build_artifacts/mistune_1657892024508/work
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+nb-conda-kernels @ file:///home/conda/feedstock_root/build_artifacts/nb_conda_kernels_1636999991206/work
+nbclassic @ file:///home/conda/feedstock_root/build_artifacts/nbclassic_1667492839781/work
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+nest-asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1664684991461/work
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+notebook-executor @ file:///home/kbuilder/miniconda3/conda-bld/dlenv-tf-2-11-cpu_1674530713176/work/packages/notebook_executor
+notebook_shim @ file:///home/conda/feedstock_root/build_artifacts/notebook-shim_1667478401171/work
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+ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
+pudb==2022.1.3
+PuLP==2.7.0
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+pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work
+pycryptodome==3.17
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+Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1672682006896/work
+PyJWT==2.6.0
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+pylint==2.16.2
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+pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1672659226110/work
+pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1652235407899/work
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+pyshp @ file:///home/conda/feedstock_root/build_artifacts/pyshp_1659002966020/work
+PySocks @ file:///tmp/build/80754af9/pysocks_1594394576006/work
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+python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work
+python-dotenv==0.21.1
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+python-Levenshtein==0.20.9
+python-louvain==0.16
+python-lsp-jsonrpc==1.0.0
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+pyu2f @ file:///home/conda/feedstock_root/build_artifacts/pyu2f_1604248910016/work
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+randomgen==1.23.1
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+requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1673863902341/work
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+rich==13.2.0
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+rsa @ file:///home/conda/feedstock_root/build_artifacts/rsa_1658328885051/work
+Rtree==1.0.1
+ruamel-yaml-conda @ file:///tmp/build/80754af9/ruamel_yaml_1616016701961/work
+rvlib==0.0.6
+s2sphere==0.2.5
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+scattertext==0.1.12
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+scikit-learn-intelex==2021.6.3
+scikit-multilearn==0.2.0
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+scipy @ file:///home/conda/feedstock_root/build_artifacts/scipy_1637806658031/work
+seaborn==0.12.2
+segregation==2.3.1
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+Send2Trash @ file:///home/conda/feedstock_root/build_artifacts/send2trash_1628511208346/work
+sentencepiece==0.1.97
+sentry-sdk==1.15.0
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+Shapely @ file:///home/conda/feedstock_root/build_artifacts/shapely_1635194349843/work
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+six @ file:///tmp/build/80754af9/six_1644875935023/work
+sklearn-contrib-py-earth @ git+https://github.com/scikit-learn-contrib/py-earth.git@dde5f899255411a7b9cbbabf93a817eff4b02e5e
+sklearn-pandas==2.2.0
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+sniffio @ file:///home/conda/feedstock_root/build_artifacts/sniffio_1662051266223/work
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+tensorflow-estimator==2.11.0
+tensorflow-gcs-config==2.11.0
+tensorflow-hub==0.12.0
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+tensorflow-io-gcs-filesystem==0.29.0
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+tensorflow-probability==0.19.0
+tensorflow-serving-api==2.11.0
+tensorflow-text==2.11.0
+tensorflow-transform==1.12.0
+tensorpack==0.11
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+termcolor==2.2.0
+terminado @ file:///home/conda/feedstock_root/build_artifacts/terminado_1670253674810/work
+text-unidecode==1.3
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+Theano==1.0.5
+Theano-PyMC==1.1.2
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+tifffile==2021.11.2
+timm==0.6.12
+tinycss2 @ file:///home/conda/feedstock_root/build_artifacts/tinycss2_1666100256010/work
+tobler==0.9.0
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+torchaudio==0.13.0+cpu
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+torchmetrics==0.11.1
+torchtext==0.14.0
+torchvision==0.14.0+cpu
+tornado @ file:///home/conda/feedstock_root/build_artifacts/tornado_1656937818679/work
+TPOT==0.11.7
+tqdm==4.64.1
+traceml==1.0.8
+traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1673359992537/work
+traittypes==0.2.1
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+trueskill==0.4.5
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+typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1665144421445/work
+tzdata==2022.7
+tzlocal==4.2
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+umap-learn==0.5.3
+unicodedata2 @ file:///home/conda/feedstock_root/build_artifacts/unicodedata2_1649111917568/work
+Unidecode==1.3.6
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+urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1673452138552/work
+urwid==2.1.2
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+vaex-ml==0.18.1
+vaex-server==0.8.1
+vaex-viz==0.5.4
+vecstack==0.4.0
+virtualenv==20.17.1
+visions==0.7.5
+vowpalwabbit==9.7.0
+vtk==9.2.6
+Wand==0.6.11
+wandb==0.13.10
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+watchfiles==0.18.1
+wavio==0.0.7
+wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1673864653149/work
+webencodings==0.5.1
+websocket-client @ file:///home/conda/feedstock_root/build_artifacts/websocket-client_1667568040382/work
+websockets==10.4
+Werkzeug==2.2.3
+wfdb==4.1.0
+whatthepatch==1.0.4
+widgetsnbextension==3.6.2
+witwidget==1.8.1
+woodwork==0.16.4
+Wordbatch==1.4.9
+wordcloud==1.8.2.2
+wordsegment==1.3.1
+wrapt==1.14.1
+wurlitzer==3.0.3
+xarray==0.20.2
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+xgboost==1.6.2
+xvfbwrapper==0.2.9
+xxhash==3.2.0
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+yacs==0.1.8
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+yellowbrick==1.5
+zict==2.2.0
+zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1669453021653/work
+zstandard @ file:///home/conda/feedstock_root/build_artifacts/zstandard_1655887611100/work

sign_to_prediction_index_map.json

@@ -0,0 +1 @@
+{"TV": 0, "after": 1, "airplane": 2, "all": 3, "alligator": 4, "animal": 5, "another": 6, "any": 7, "apple": 8, "arm": 9, "aunt": 10, "awake": 11, "backyard": 12, "bad": 13, "balloon": 14, "bath": 15, "because": 16, "bed": 17, "bedroom": 18, "bee": 19, "before": 20, "beside": 21, "better": 22, "bird": 23, "black": 24, "blow": 25, "blue": 26, "boat": 27, "book": 28, "boy": 29, "brother": 30, "brown": 31, "bug": 32, "bye": 33, "callonphone": 34, "can": 35, "car": 36, "carrot": 37, "cat": 38, "cereal": 39, "chair": 40, "cheek": 41, "child": 42, "chin": 43, "chocolate": 44, "clean": 45, "close": 46, "closet": 47, "cloud": 48, "clown": 49, "cow": 50, "cowboy": 51, "cry": 52, "cut": 53, "cute": 54, "dad": 55, "dance": 56, "dirty": 57, "dog": 58, "doll": 59, "donkey": 60, "down": 61, "drawer": 62, "drink": 63, "drop": 64, "dry": 65, "dryer": 66, "duck": 67, "ear": 68, "elephant": 69, "empty": 70, "every": 71, "eye": 72, "face": 73, "fall": 74, "farm": 75, "fast": 76, "feet": 77, "find": 78, "fine": 79, "finger": 80, "finish": 81, "fireman": 82, "first": 83, "fish": 84, "flag": 85, "flower": 86, "food": 87, "for": 88, "frenchfries": 89, "frog": 90, "garbage": 91, "gift": 92, "giraffe": 93, "girl": 94, "give": 95, "glasswindow": 96, "go": 97, "goose": 98, "grandma": 99, "grandpa": 100, "grass": 101, "green": 102, "gum": 103, "hair": 104, "happy": 105, "hat": 106, "hate": 107, "have": 108, "haveto": 109, "head": 110, "hear": 111, "helicopter": 112, "hello": 113, "hen": 114, "hesheit": 115, "hide": 116, "high": 117, "home": 118, "horse": 119, "hot": 120, "hungry": 121, "icecream": 122, "if": 123, "into": 124, "jacket": 125, "jeans": 126, "jump": 127, "kiss": 128, "kitty": 129, "lamp": 130, "later": 131, "like": 132, "lion": 133, "lips": 134, "listen": 135, "look": 136, "loud": 137, "mad": 138, "make": 139, "man": 140, "many": 141, "milk": 142, "minemy": 143, "mitten": 144, "mom": 145, "moon": 146, "morning": 147, "mouse": 148, "mouth": 149, "nap": 150, "napkin": 151, "night": 152, "no": 153, "noisy": 154, "nose": 155, "not": 156, "now": 157, "nuts": 158, "old": 159, "on": 160, "open": 161, "orange": 162, "outside": 163, "owie": 164, "owl": 165, "pajamas": 166, "pen": 167, "pencil": 168, "penny": 169, "person": 170, "pig": 171, "pizza": 172, "please": 173, "police": 174, "pool": 175, "potty": 176, "pretend": 177, "pretty": 178, "puppy": 179, "puzzle": 180, "quiet": 181, "radio": 182, "rain": 183, "read": 184, "red": 185, "refrigerator": 186, "ride": 187, "room": 188, "sad": 189, "same": 190, "say": 191, "scissors": 192, "see": 193, "shhh": 194, "shirt": 195, "shoe": 196, "shower": 197, "sick": 198, "sleep": 199, "sleepy": 200, "smile": 201, "snack": 202, "snow": 203, "stairs": 204, "stay": 205, "sticky": 206, "store": 207, "story": 208, "stuck": 209, "sun": 210, "table": 211, "talk": 212, "taste": 213, "thankyou": 214, "that": 215, "there": 216, "think": 217, "thirsty": 218, "tiger": 219, "time": 220, "tomorrow": 221, "tongue": 222, "tooth": 223, "toothbrush": 224, "touch": 225, "toy": 226, "tree": 227, "uncle": 228, "underwear": 229, "up": 230, "vacuum": 231, "wait": 232, "wake": 233, "water": 234, "wet": 235, "weus": 236, "where": 237, "white": 238, "who": 239, "why": 240, "will": 241, "wolf": 242, "yellow": 243, "yes": 244, "yesterday": 245, "yourself": 246, "yucky": 247, "zebra": 248, "zipper": 249}

utils.py

@@ -0,0 +1,12 @@
+import numpy as np
+import pandas as pd
+
+def load_relevant_data_subset(df, ROWS_PER_FRAME):
+    data_columns = ['x', 'y', 'z']
+    data = df
+    n_frames = int(len(data) / ROWS_PER_FRAME)
+    data = data.values.reshape(n_frames, ROWS_PER_FRAME, len(data_columns))
+    return data.astype(np.float32)
+
+def display_dataframe():
+    return df