Upload 5 files

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tuda_logo.tif filter=lfs diff=lfs merge=lfs -text
+Voto_Logo_bpg-03.png filter=lfs diff=lfs merge=lfs -text

match_checker2000.py

@@ -0,0 +1,219 @@
+import streamlit as st
+import json
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+footer="""<style>
+a:link , a:visited{
+color: blue;
+background-color: transparent;
+text-decoration: underline;
+}
+
+a:hover,  a:active {
+color: red;
+background-color: transparent;
+text-decoration: underline;
+}
+
+.footer {
+position: fixed;
+left: 0;
+bottom: 0;
+width: 100%;
+background-color: cornflowerblue;
+color: black;
+text-align: center;
+}
+</style>
+<div class="footer">
+    <p>This app was developed by <a href="https://www.voto.vote" target="_blank">VOTO</a>.
+    Developer: <a href="https://linkedin.com/in/thilo-dieing-bba5b2253" target="_blank">Thilo I. Dieing, M.Sc.</a></p>
+</div>
+"""
+st.markdown(footer,unsafe_allow_html=True)
+def add_logo():
+    st.markdown(
+        """
+        <style>
+            
+            [data-testid="stSidebarNav"]::before {
+                content: "Uni Mannheim ASR Team Project";
+                margin-left: 20px;
+                margin-top: 20px;
+                font-size: 20px;
+                position: relative;
+                top: 100px;
+            }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+# functions  
+
+
+
+add_logo()
+col1, col2 = st.columns(2)
+with col1:
+    st.image("voto_purple.png", width=300)
+with col2:
+    st.image("tuda_logo.tif", width=300)
+
+# Title of the app
+st.title("VOTO party insights")
+
+# Allow the user to upload a file
+uploaded_file = st.file_uploader("Upload the JSON file with your VOTO party answers", type=["json"])
+
+# Check if a file is uploaded
+if uploaded_file is not None:
+    try:
+        # Try to load the uploaded file as JSON
+        file_content = uploaded_file.read()
+        json_data = json.loads(file_content)
+
+        # If the file is valid JSON, show a success message
+
+        # Convert the JSON data to a DataFrame
+        if isinstance(json_data, list):  # Expecting a list of dictionaries for a proper DataFrame
+            df = pd.DataFrame(json_data)
+            election=str(df["instance"].iloc[0])
+            statements = str(df["statement"].nunique())
+            statement_n = df["statement"].nunique()
+            parties = str(df["party_name"].nunique())
+            st.success("You have uploaded the party answers for the VOTO instance: "+election+" ("+statements+" statements; "+parties+" Parties)")
+            
+            expander1 = st.expander("Statement Selection")
+            pivot_df = df.pivot(index='statement', columns='party_name', values='valuation')
+            # std
+            df['std_deviation'] = df.groupby('statement')['valuation'].transform('std')
+            # how many differnet postions
+            df['unique_postions'] = df.groupby('statement')['valuation'].transform('nunique')
+            # how many neutrals
+            df['n_neutral'] = df.groupby('statement')['valuation'].transform(lambda x: (x == 50).sum())
+            # standalone party postions
+            value_counts = df.groupby(['statement', 'valuation']).size().reset_index(name='Count')
+            unique_values = value_counts[value_counts['Count'] == 1]
+            single_counts = unique_values.groupby('statement').size().reset_index(name='SingleCount')
+
+            df = df.merge(single_counts, on='statement', how='left')
+            df['SingleCount'] = df['SingleCount'].fillna(0).astype(int) 
+            #std without neutral
+            df_filtered = df[df['valuation'] != 50]
+            df_filtered['std_deviation2'] = df_filtered.groupby('statement')['valuation'].transform('std')
+            df2= df_filtered[["statement",'std_deviation2' ]].drop_duplicates()
+            df = df.merge(df2, on='statement', how='left', suffixes=('', '_new'))
+
+            sorted_single_counts = df[['statement', 'std_deviation']].drop_duplicates()
+            sorted_single_counts['AdjustedRank1'] = sorted_single_counts['std_deviation'].rank(method='dense', ascending=False).astype(int) - 1
+            df = df.merge(sorted_single_counts[['statement', 'AdjustedRank1']], on='statement', how='left', suffixes=('', '_new'))
+
+            sorted_single_counts = df[['statement', 'unique_postions']].drop_duplicates()
+            sorted_single_counts['AdjustedRank2'] = sorted_single_counts['unique_postions'].rank(method='dense', ascending=False).astype(int) - 1
+            df = df.merge(sorted_single_counts[['statement', 'AdjustedRank2']], on='statement', how='left', suffixes=('', '_new'))
+
+            sorted_single_counts = df[['statement', 'n_neutral']].drop_duplicates()
+            sorted_single_counts['AdjustedRank3'] = sorted_single_counts['n_neutral'].rank(method='dense').astype(int) - 1
+            df = df.merge(sorted_single_counts[['statement', 'AdjustedRank3']], on='statement', how='left', suffixes=('', '_new'))
+
+            sorted_single_counts = df[['statement', 'SingleCount']].drop_duplicates()
+            sorted_single_counts['AdjustedRank4'] = sorted_single_counts['SingleCount'].rank(method='dense', ascending=False).astype(int) - 1
+            df = df.merge(sorted_single_counts[['statement', 'AdjustedRank4']], on='statement', how='left', suffixes=('', '_new'))
+
+            sorted_single_counts = df[['statement', 'std_deviation2']].drop_duplicates()
+            sorted_single_counts['AdjustedRank5'] = sorted_single_counts['std_deviation2'].rank(method='dense', ascending=False).astype(int) - 1
+            df = df.merge(sorted_single_counts[['statement', 'AdjustedRank5']], on='statement', how='left', suffixes=('', '_new'))
+
+            df["statement_importance"]= (df["AdjustedRank1"]+df["AdjustedRank2"]+df["AdjustedRank3"]+df["AdjustedRank4"]+df["AdjustedRank5"])/5
+            df_pres= df[['statement',"statement_importance"]].drop_duplicates()
+            df_sorted1 = df_pres.sort_values(by='statement_importance')
+            df_sorted1.reset_index(drop=True, inplace=True)
+
+            df_sorted = df.sort_values(by='statement_importance')
+            df_sorted.reset_index(drop=True, inplace=True)
+            statn = expander1.slider("How many statements do you want in your final VOTO? You currently have **"+statements+ "** statements." , 0, int(statements),0)
+            if statn !=0:
+                expander1.write("**Based on that our metric recommends keeping the following " +str(statn) +" statements:**")
+                lstat=df_sorted1['statement'].tolist()
+                lstat1=lstat[:statn]
+                i=0
+                for l in lstat1:
+                    i=i+1
+                    expander1.markdown("<span style='color: green;'>"+str(i)+": "+l+"</span>", unsafe_allow_html=True)
+                expander1.write("**While the metric recommends dropping "+str(statement_n-statn) +" statements due to a lack of difference:**")
+                lstat=df_sorted1['statement'].tolist()
+                lstat1=lstat[statn:]
+                i=statn
+                for l in lstat1:
+                    i=i+1
+                    expander1.markdown("<span style='color: red;'>"+str(i)+": "+l+"</span>", unsafe_allow_html=True)
+            if expander1.button("Additional information on individual metric scores"):
+                expander1.dataframe(df_sorted)
+            
+            expander2 = st.expander("Party Positions")
+            df = pd.DataFrame(json_data)
+            unique_party_names = df['party_name'].unique()
+            unique_party_names = [""] + list(unique_party_names)  # Add an empty option
+            selected_party = expander2.selectbox("Select a Party", unique_party_names)
+            contains_25_or_75 = (25 in df['valuation'].values) or (75 in df['valuation'].values)
+            if contains_25_or_75:
+                y_ticks = [0, 25, 50, 75, 100]  # Set y-ticks for scale 1 to 5
+                y_tick_labels = ['Strong Disagreement', 'Disagreement', 'Neutral', 'Agreement', 'Strong Agreement']  # Custom labels
+            else:
+                y_ticks = [0, 50, 100]  # Set y-ticks for scale 1 to 3
+                y_tick_labels = ['Disagreement', 'Neutral', 'Agreement']
+            if selected_party:  # Check if a party has been selected
+                df['short_text'] = df['statement'].apply(lambda x: ' '.join(x.split()[:11]))
+                filtered_df = df[df['party_name'] == selected_party]
+                fig, ax = plt.subplots()
+                ax.scatter(filtered_df['valuation'], filtered_df['short_text'], color='blue', s=100)  # s is the size of points
+                ax.set_xlabel('Valuation')
+                ax.set_ylabel('Statements')
+                ax.set_title('Valuation by Statement')
+                ax.set_xticks(y_ticks)  # Set y-ticks dynamically based on condition
+                ax.set_xticklabels(y_tick_labels)
+                plt.xticks(rotation=45) 
+                ax.grid(True)
+                expander2.pyplot(fig)
+            expander3 = st.expander("Unique Party Positions")
+            df = pd.DataFrame(json_data)
+            unique_party_names = df['party_name'].unique()
+            unique_party_names = [""] + list(unique_party_names)  # Add an empty option
+            selected_party = expander3.selectbox("Select a Party", unique_party_names, key="2")
+            contains_25_or_75 = (25 in df['valuation'].values) or (75 in df['valuation'].values)
+            if contains_25_or_75:
+                y_ticks = [0, 25, 50, 75, 100]  # Set y-ticks for scale 1 to 5
+                y_tick_labels = ['Strong Disagreement', 'Disagreement', 'Neutral', 'Agreement', 'Strong Agreement']  # Custom labels
+            else:
+                y_ticks = [0, 50, 100]  # Set y-ticks for scale 1 to 3
+                y_tick_labels = ['Disagreement', 'Neutral', 'Agreement']
+            if selected_party:  # Check if a party has been selected
+                value_counts = df.groupby(['statement', 'valuation']).size().reset_index(name='Count')
+                unique_values = value_counts[value_counts['Count'] == 1]
+                unique_parties = df.merge(unique_values[['statement', 'valuation']], on=['statement', 'valuation'])
+                unique_parties_result = unique_parties[['statement', 'party_name', 'valuation']]
+                unique_parties_result['short_text'] = unique_parties_result['statement'].apply(lambda x: ' '.join(x.split()[:11]))
+                filtered_unique_parties_result = unique_parties_result[unique_parties_result['party_name'] == selected_party]
+                fig, ax = plt.subplots()
+                ax.scatter(filtered_unique_parties_result['valuation'], filtered_unique_parties_result['short_text'], color='blue', s=100)  # s is the size of points
+                ax.set_xlabel('Valuation')
+                ax.set_ylabel('Statements')
+                ax.set_title('Valuation by Statement')
+                ax.set_xticks(y_ticks)  # Set y-ticks dynamically based on condition
+                ax.set_xticklabels(y_tick_labels)
+                plt.xticks(rotation=45) 
+                ax.grid(True)
+                expander3.pyplot(fig)
+
+
+
+        else:
+            st.warning("The JSON file structure is not suitable for DataFrame conversion. It should be a list of dictionaries.")
+
+    except json.JSONDecodeError:
+        # If the file is not a valid JSON, show an error message
+        st.error("This is not a valid JSON file. Please upload a valid JSON.")
+else:
+    st.info("Please upload a JSON file to get started.")

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+jsonlib-python3
+pandas
+numpy
+matplotlib