Update app.py

app.py

@@ -1,11 +1,4 @@
-#import appStore.target as target_extraction
-#import appStore.netzero as netzero
-#import appStore.sector as sector
-#import appStore.adapmit as adapmit
-#import appStore.ghg as ghg
-#import appStore.policyaction as policyaction
-#import appStore.conditional as conditional
-#import appStore.indicator as indicator
+#import appStore.identifier as target_identifier
 import appStore.doc_processing as processing
 from utils.uploadAndExample import add_upload
 from PIL import Image
@@ -48,107 +41,49 @@ with st.expander("ℹ️ - About this app", expanded=False):
             Place holder \ 
             Place holder
               """)
-    #c1, c2, c3 =  st.columns([12,1,10])
-    #with c1:
-        #image = Image.open('docStore/img/flow.jpg') 
-        #st.image(image)
-    #with c3:
-        #st.write("""
-        #    What happens in the background?
+   
+    st.write("""
+           What happens in the background?
             
-        #    - Step 1: Once the document is provided to app, it undergoes *Pre-processing*.\
-        #    In this step the document is broken into smaller paragraphs \
-        #    (based on word/sentence count).
-        #    - Step 2: The paragraphs are fed to **Target Classifier** which detects if
-        #    the paragraph contains any *Target* related information or not.
-        #    - Step 3: The paragraphs which are detected containing some target \
-        #    related information are then fed to multiple classifier to enrich the 
-        #    Information Extraction.
+           - Step 1: Once the document is provided to app, it undergoes *Pre-processing*.\
+           In this step the document is broken into smaller paragraphs \
+           (based on word/sentence count).
+           - Step 2: The paragraphs are fed to **Target Classifier** which detects if
+           the paragraph contains any *Target* related information or not.
+           - Step 3: The paragraphs which are detected containing some target \
+           related information are then fed to multiple classifier to enrich the 
+           Information Extraction.
     
-        #    The Step 2 and 3 are repated then similarly for Action and  Policies & Plans.
-        #    """)
+           The Step 2 and 3 are repated then similarly for Action and  Policies & Plans.
+           """)
                   
-    #st.write("")
+    st.write("")
 
 
-apps = [processing.app, target_extraction.app, netzero.app, ghg.app,
-        policyaction.app, conditional.app, sector.app, adapmit.app,indicator.app]
+# apps = [processing.app, target_extraction.app, netzero.app, ghg.app,
+#         policyaction.app, conditional.app, sector.app, adapmit.app,indicator.app]
 
-multiplier_val =1/len(apps)
-if st.button("Analyze Document"):
-    prg = st.progress(0.0)
-    for i,func in enumerate(apps):
-        func()
-        prg.progress((i+1)*multiplier_val)
+# multiplier_val =1/len(apps)
+# if st.button("Analyze Document"):
+#     prg = st.progress(0.0)
+#     for i,func in enumerate(apps):
+#         func()
+#         prg.progress((i+1)*multiplier_val)
 
     
-if 'key1' in st.session_state:
-    with st.sidebar:
-        topic = st.radio(
-                        "Which category you want to explore?",
-                        ('Target', 'Action', 'Policies/Plans'))
+# if 'key1' in st.session_state:
+#     with st.sidebar:
+#         topic = st.radio(
+#                         "Which category you want to explore?",
+#                         ('Target', 'Action', 'Policies/Plans'))
     
-    if topic == 'Target':
-        target_extraction.target_display()
-    elif topic == 'Action':
-        policyaction.action_display()
-    else: 
-        policyaction.policy_display()
-    # st.write(st.session_state.key1)
+#     if topic == 'Target':
+#         target_extraction.target_display()
+#     elif topic == 'Action':
+#         policyaction.action_display()
+#     else: 
+#         policyaction.policy_display()
+#     # st.write(st.session_state.key1)
 
-#st.title("Identify references to vulnerable groups.")
 
-#st.write("""Vulnerable groups encompass various communities and individuals who are disproportionately affected by the impacts of climate change
-#due to their socioeconomic status, geographical location, or inherent characteristics. By incorporating the needs and perspectives of these groups 
-#into national climate policies, governments can ensure equitable outcomes, promote social justice, and strive to build resilience within the most marginalized populations, 
-#fostering a more sustainable and inclusive society as we navigate the challenges posed by climate change.This app allows you to identify whether a text contains any 
-#references to vulnerable groups, for example when talking about policy documents.""")
-
-# Document upload
-#uploaded_file = st.file_uploader("Upload your file here")
-
-# Create text input box
-#input_text = st.text_area(label='Please enter your text here', value="This policy has been implemented to support women.")
-
-#st.write('Prediction:', model(input_text))
-
-######################################### Model #########################################################
-
-# Load the model
-#model = SetFitModel.from_pretrained("leavoigt/vulnerable_groups")
-
-# Define the classes
-#id2label = {
- #   0: 'Agricultural communities',
- #   1: 'Children and Youth',
- #   2: 'Coastal communities',
- #   3: 'Drought-prone regions',
- #   4: 'Economically disadvantaged communities',
- #   5: 'Elderly population',
- #   6: 'Ethnic minorities and indigenous people',
- #   7: 'Informal sector workers',
- #   8: 'Migrants and Refugees',
- #   9: 'Other',
- #   10: 'People with Disabilities',
- #   11: 'Rural populations',
- #   12: 'Sexual minorities (LGBTQI+)',
- #   13: 'Urban populations',
- #   14: 'Women'}
-
-
-### Process document to paragraphs 
-# Source: https://blog.jcharistech.com/2021/01/21/how-to-save-uploaded-files-to-directory-in-streamlit-apps/
-
-# Store uploaded file temporarily in directory to get file path (necessary for processing)
-# def save_uploadedfile(upl_file):
-#      with open(os.path.join("tempDir",upl_file.name),"wb") as f:
-#          f.write(upl_file.getbuffer())
-#      return st.success("Saved File:{} to tempDir".format(upl_file.name))
-
-# if uploaded_file is not None: 
-#     # Save the file 
-#     file_details = {"FileName": uploaded_file.name, "FileType": uploaded_file.type}
-#     save_uploadedfile(uploaded_file)
-
-#     #Get the file path