Update app.py

app.py

@@ -1,36 +1,43 @@
-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.doc_processing as processing
-from utils.uploadAndExample import add_upload
-from PIL import Image
 import streamlit as st
+import json
+import os
+# shifted from below - this must be the first streamlit call; otherwise: problems
+st.set_page_config(page_title = 'Climate Policy Analysis Assistant', 
+                   initial_sidebar_state='expanded', layout="wide")
+
+import logging
+logging.getLogger().setLevel(logging.INFO)
+
+
+
+
+
+
+
+import pkg_resources
+installed_packages = pkg_resources.working_set
 
-st.set_page_config(page_title = 'Climate Policy Intelligence', 
-                   initial_sidebar_state='expanded', layout="wide") 
 
 with st.sidebar:
     # upload and example doc
+    
     choice = st.sidebar.radio(label = 'Select the Document',
                             help = 'You can upload the document \
                             or else you can try a example document', 
                             options = ('Upload Document', 'Try Example'), 
                             horizontal = True)
-    add_upload(choice) 
+    with(open('docStore/sample/files.json','r')) as json_file:
+            files = json.load(json_file)
+    add_upload(choice, files) 
 
 with st.container():
-        st.markdown("<h2 style='text-align: center; color: black;'> Climate Policy Understanding App </h2>", unsafe_allow_html=True)
+        st.markdown("<h2 style='text-align: center; color: black;'> Climate Policy Analysis Assistant: CPo_droid </h2>", unsafe_allow_html=True)
         st.write(' ')
 
 with st.expander("ℹ️ - About this app", expanded=False):
     st.write(
         """
-        Climate Policy Understanding App is an open-source\
+        CPo_droid is an open-source\
         digital tool which aims to assist policy analysts and \
         other users in extracting and filtering relevant \
         information from public documents.
@@ -47,17 +54,11 @@ with st.expander("ℹ️ - About this app", expanded=False):
                 not at specific Sector level but are applicable at economic \
                 wide scale.
             - **Netzero**: Identifies if its Netzero Target or not.
-                - 'NET-ZERO': target_labels = ['T_Netzero','T_Netzero_C']
-                - 'Non Netzero Target': target_labels_neg = ['T_Economy_C',
-                  'T_Economy_Unc','T_Adaptation_C','T_Adaptation_Unc','T_Transport_C',
-                  'T_Transport_O_C','T_Transport_O_Unc','T_Transport_Unc']
-                - 'Others': Other Targets beside covered above
+                - 'NET-ZERO target_labels' = ['T_Netzero','T_Netzero_C']
             - **GHG Target**: GHG targets refer to contributions framed as targeted \
                               outcomes in GHG terms.
-                - 'GHG': target_labels_ghg_yes = ['T_Transport_Unc','T_Transport_C']
-                - 'NON GHG TRANSPORT TARGET': target_labels_ghg_no = ['T_Adaptation_Unc',\
-                   'T_Adaptation_C', 'T_Transport_O_Unc', 'T_Transport_O_C']
-                - 'OTHERS': Other Targets beside covered above.
+                - 'GHG': ['T_Transport_Unc','T_Transport_C','T_Economy_C','T_Economy_Unc','T_Energy_C','T_Energy_Unc']
+                - 'NON GHG TARGET': ['T_Adaptation_Unc','T_Adaptation_C', 'T_Transport_O_Unc', 'T_Transport_O_C']
             - **Conditionality**: An “unconditional contribution” is what countries \
              could implement without any conditions and based on their own \
              resources and capabilities. A “conditional contribution” is one \
@@ -84,37 +85,17 @@ with st.expander("ℹ️ - About this app", expanded=False):
             - 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 \
+            - Step 2: The paragraphs are fed to **TAPP(Target/Action/Policy/Plan multilabel) Classifier** which detects if
+            the paragraph contains any *TAPP* related information or not.
+            - Step 3: The paragraphs which are detected containing some TAPP \
             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.
             """)
+        
+        list_ = ""
+        for package in installed_packages:
+            list_ = list_ + f"{package.key}=={package.version}\n"
+        st.download_button('Download Requirements', list_, file_name='requirements.txt')
                   
-    st.write("")
-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)
-
-    
-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)
+    st.write("")