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Update app.py
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app.py
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@@ -10,68 +10,69 @@ import numpy as np
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# Declaring the variables for later use to talk to dataset
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# the token is saved as secret key-value pair in the environment which can be access as shown below
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auth_token = os.environ.get("space_to_dataset") or True
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DATASET_REPO_URL = 'ppsingh/annotation_data' # path to dataset repo
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DATA_FILENAME = "paralist.json"
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DATA_FILE = os.path.join("data", DATA_FILENAME)
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# cloning the dataset repo
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repo = Repository( local_dir="data", clone_from=DATASET_REPO_URL, repo_type="dataset", use_auth_token= auth_token)
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# Data file name
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file_name = 'paralist.json'
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# reading the json
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with open('data/{}'.format(file_name), 'r', encoding="utf8") as json_file:
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paraList = json.load(json_file)
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# getting outer level keys in json
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keys = paraList.keys()
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#data = pd.read_csv("test.csv")
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# sidebar with info and drop down to select from the keys
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st.sidebar.markdown(
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"""
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# Data Annotation Demo
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This app is demo how to use the space to provide user interface for the data annotation/tagging. The data resides in repo_type 'dataset'.
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"""
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)
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topic = None
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if keys is not None:
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topic = st.sidebar.selectbox(
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# st.write(line)
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#st.write(paraList)
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if topic is not None:
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subtopics = list(paraList[topic].keys())
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#st.write(subtopics)
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val = np.random.randint(0,len(subtopics)-1)
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choice = subtopics[val]
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st.write(choice)
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#if np.random.randint(0,1) == 0:
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# choice = "Gender"
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#else:
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# choice = "Women Empowernment"
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idx = np.random.randint(0,3)
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c1, c2, c3 = st.columns([3, 1, 1])
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with c1:
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st.write(paraList[topic][choice][idx]['textsegment'])
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with c2:
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st.text(choice)
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st.header('Feedback')
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feedback = None
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feedback = st.selectbox('0 If Tag is not a good keyword for text, 5 for prefect match',(0,1,2,3,4,5))
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if feedback:
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if st.button('Submit'):
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paraList[topic][choice][idx]['annotation'].append(feedback)
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# with open('data/{}'.format(file_name), 'r', encoding="utf8") as json_file:
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# json.dump(paraList,json_file, ensure_ascii = True)
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# repo.push_to_hub('added new annotation')
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@@ -100,6 +101,38 @@ if topic is not None:
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# st.write('Succcess')
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# Declaring the variables for later use to talk to dataset
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# the token is saved as secret key-value pair in the environment which can be access as shown below
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#auth_token = os.environ.get("space_to_dataset") or True
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#DATASET_REPO_URL = 'ppsingh/annotation_data' # path to dataset repo
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#DATA_FILENAME = "paralist.json"
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#DATA_FILE = os.path.join("data", DATA_FILENAME)
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# cloning the dataset repo
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#repo = Repository( local_dir="data", clone_from=DATASET_REPO_URL, repo_type="dataset", use_auth_token= auth_token)
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# Data file name
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#file_name = 'paralist.json'
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# reading the json
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#with open('data/{}'.format(file_name), 'r', encoding="utf8") as json_file:
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# paraList = json.load(json_file)
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# getting outer level keys in json
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#keys = paraList.keys()
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#data = pd.read_csv("test.csv")
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# sidebar with info and drop down to select from the keys
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#st.sidebar.markdown(
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# """
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# Data Annotation Demo
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#This app is demo how to use the space to provide user interface for the data annotation/tagging. The data resides in repo_type 'dataset'.
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#"""
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#)
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#topic = None
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#if keys is not None:
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# topic = st.sidebar.selectbox(
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# label="Choose dataset topic to load", options=keys )
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# st.write(line)
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#st.write(paraList)
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#if topic is not None:
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# subtopics = list(paraList[topic].keys())
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#st.write(subtopics)
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# val = np.random.randint(0,len(subtopics)-1)
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# choice = subtopics[val]
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# st.write(choice)
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#if np.random.randint(0,1) == 0:
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# choice = "Gender"
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#else:
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# choice = "Women Empowernment"
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# idx = np.random.randint(0,3)
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# st.write(idx)
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#c1, c2, c3 = st.columns([3, 1, 1])
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#with c1:
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# st.header('Text')
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#st.write(paraList[topic][choice][idx]['textsegment'])
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#with c2:
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# st.header('Tag')
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#st.text(choice)
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# with c3:
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#st.header('Feedback')
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#feedback = None
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# feedback = st.selectbox('0 If Tag is not a good keyword for text, 5 for prefect match',(0,1,2,3,4,5))
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# if feedback:
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# if st.button('Submit'):
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# paraList[topic][choice][idx]['annotation'].append(feedback)
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# with open('data/{}'.format(file_name), 'r', encoding="utf8") as json_file:
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# json.dump(paraList,json_file, ensure_ascii = True)
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# repo.push_to_hub('added new annotation')
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# st.write('Succcess')
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import streamlit as st
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session_state = st.sessionstate.get(col1=False, col2=False, col3=False)
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col1, col2, col3 = st.columns(3)
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col1_one = col1.button("CARTE", key="1")
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col2_one = col2.button("TABLEAU", key="2")
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col3_one = col3.button("SYNTHÈSE", key="3")
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if col1_one or session_state.col1:
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session_state.col1 = True
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session_state.col2 = False
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session_state.col3 = False
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sel_Map = st.selectbox("Choose Map type :", options=['Hello1', 'Hello2'], index=1)
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if sel_Map == 'Hello1':
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st.write("Hello world! 1")
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elif sel_Map == 'Hello2':
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st.write("Hello world! 2")
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if col2_one or session_state.col2:
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session_state.col1 = False
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session_state.col2 = True
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session_state.col3 = False
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st.write("Hello world! 3")
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if col3_one or session_state.col3:
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session_state.col1 = False
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session_state.col2 = False
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session_state.col3 = True
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st.write("Hello world! 4")
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