From e2b4e5734093561e5e10a61dc348dc60d44fcc0e Mon Sep 17 00:00:00 2001
From: carlotagomezr <carlotagro@gmail.com>
Date: Tue, 5 Mar 2024 18:05:15 +0100
Subject: [PATCH 1/7] setting app structure

---
 __pycache__/eda_view.cpython-311.pyc        | Bin 0 -> 2882 bytes
 __pycache__/prediction_view.cpython-311.pyc | Bin 0 -> 1452 bytes
 eda_view.py                                 |  38 +++++++++++++++++
 prediction_view.py                          |  23 +++++++++++
 streamlit_app.py                            |  43 ++++----------------
 5 files changed, 70 insertions(+), 34 deletions(-)
 create mode 100644 __pycache__/eda_view.cpython-311.pyc
 create mode 100644 __pycache__/prediction_view.cpython-311.pyc
 create mode 100644 eda_view.py
 create mode 100644 prediction_view.py

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diff --git a/eda_view.py b/eda_view.py
new file mode 100644
index 000000000000..88b7f3c32ebf
--- /dev/null
+++ b/eda_view.py
@@ -0,0 +1,38 @@
+import altair as alt
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import streamlit as st
+import seaborn as sns
+import plotly.express as px
+
+def run():  
+    df = pd.read_excel('/Users/carlotapersonal/Library/CloudStorage/OneDrive-UFV/CURSO_5/PFG/Code/proyecto-fin-de-grado-2024-2-carlotagomezr/data-analysis/eda/dataset_post_EDA.xlsx')
+
+    # TITULO -----------------------------------------------------------------------------------
+    st.title('Gestión de incidencias - EDA')
+    # Mostrar el número total de incidencias
+    total_incidencias = df['task_id'].nunique()  
+    st.write(f'Número total de incidencias: {total_incidencias}')
+
+    # GRAFICO DE BARRAS, ANALISIS TEMPORAL ---------------------------------------------------------------------------------
+    df['end_time'] = pd.to_datetime(df['end_time'])  
+    df.set_index('end_time', inplace=True) 
+    variable = st.selectbox('Seleccione la variable para agrupar:', ['priority', 'has_breached', 'assignment_group', 'company', 'contact_type'])
+
+    # Agrupar por la variable seleccionada y resample por mes
+    grouped = df.groupby(variable).resample('M')['task_id'].nunique().reset_index()
+
+    # Crear un gráfico de barras apilado con colores basados en la variable seleccionada
+    fig = px.bar(grouped, x='end_time', y='task_id', color=variable, title='Número de incidencias por mes', labels={'task_id':'Número de incidencias'}, height=400)
+    fig.update_layout(barmode='stack')
+    fig.update_xaxes(title_text='Mes y año de finalización')
+    st.plotly_chart(fig)
+
+
+    # GRAFICO DE BARRAS CON PRIORITY VS TIME_WORKED ---------------------------------------------------------------------------
+    fig2 = px.bar(x='time_worked', y='priority', color=variable)
+    fig2.set_title('Relación entre la prioridad y el tiempo trabajado')
+    fig2.set_ylabel('Prioridad')
+    fig2.set_xlabel('Tiempo trabajado')
+    st.pyplot(fig2)
\ No newline at end of file
diff --git a/prediction_view.py b/prediction_view.py
new file mode 100644
index 000000000000..cd97ff68d1e1
--- /dev/null
+++ b/prediction_view.py
@@ -0,0 +1,23 @@
+import altair as alt
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import streamlit as st
+
+
+def run():
+    # boton en el centro para crear una nueva incidencia que abra un formulario con campos basicos
+    if st.button('Crear una nueva incidencia'):
+        with st.form(key='my_form'):
+            st.write('Cree una nueva incidencia:')
+
+            # Aquí puedes agregar los campos que necesites para tu formulario
+            campo1 = st.text_input(label='Campo 1')
+            campo2 = st.text_input(label='Campo 2')
+            campo3 = st.text_input(label='Campo 3')
+
+            # Botón de envío del formulario
+            submit_button = st.form_submit_button(label='Enviar')
+
+            # botón para cancelr el formulario
+            cancel_button = st.form_submit_button(label='Cancelar')
diff --git a/streamlit_app.py b/streamlit_app.py
index 7a0ed1272052..69b4e41718d7 100644
--- a/streamlit_app.py
+++ b/streamlit_app.py
@@ -2,39 +2,14 @@
 import numpy as np
 import pandas as pd
 import streamlit as st
+import eda_view as view1
+import prediction_view as view2
 
-"""
-# Welcome to Streamlit!
+st.sidebar.title('Selecciona una vista')
+options = ['Gestión de incidencias - Análisis exploratorio de datos', 'Nueva incidencia - predicciones y LLM']
+menu = st.sidebar.selectbox('Select a view', options)
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if menu == options[0]:
+    view1.run()
+elif menu == options[1]:
+    view2.run()

From 06c74fa2591b3adb842ad9b956f0a2ba66078893 Mon Sep 17 00:00:00 2001
From: carlotagomezr <carlotagro@gmail.com>
Date: Wed, 6 Mar 2024 08:58:51 +0100
Subject: [PATCH 2/7] view 1 update

---
 __pycache__/eda_view.cpython-311.pyc | Bin 2882 -> 6889 bytes
 eda_view.py                          |  70 +++++++++++++++++++++++++--
 streamlit_app.py                     |   6 +--
 3 files changed, 68 insertions(+), 8 deletions(-)

diff --git a/__pycache__/eda_view.cpython-311.pyc b/__pycache__/eda_view.cpython-311.pyc
index 113625bdcc88bc4b6579d30d1db1bcf2d1784477..8edf1b43f5fbbf4ab12700fef57178fe788d0a95 100644
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diff --git a/eda_view.py b/eda_view.py
index 88b7f3c32ebf..fa6bd7cac6a0 100644
--- a/eda_view.py
+++ b/eda_view.py
@@ -15,6 +15,7 @@ def run():
     total_incidencias = df['task_id'].nunique()  
     st.write(f'Número total de incidencias: {total_incidencias}')
 
+
     # GRAFICO DE BARRAS, ANALISIS TEMPORAL ---------------------------------------------------------------------------------
     df['end_time'] = pd.to_datetime(df['end_time'])  
     df.set_index('end_time', inplace=True) 
@@ -31,8 +32,67 @@ def run():
 
 
     # GRAFICO DE BARRAS CON PRIORITY VS TIME_WORKED ---------------------------------------------------------------------------
-    fig2 = px.bar(x='time_worked', y='priority', color=variable)
-    fig2.set_title('Relación entre la prioridad y el tiempo trabajado')
-    fig2.set_ylabel('Prioridad')
-    fig2.set_xlabel('Tiempo trabajado')
-    st.pyplot(fig2)
\ No newline at end of file
+    fig2 = px.bar(df, x='time_worked', y='priority', color=variable)
+    fig2.update_layout(
+        title='Relación entre la prioridad y el tiempo trabajado',
+        xaxis_title='Tiempo trabajado',
+        yaxis_title='Prioridad'
+    )
+    st.plotly_chart(fig2)
+
+    # TABLA SUMMARY BY GROUP, COMPANY AND USER TYPE ---------------------------------------------------------------------------
+    variable_select = st.selectbox('Seleccione la variable para agrupar:', ['assignment_group', 'company', 'contact_type'])
+   
+    priority_counts = df.groupby([variable_select, 'priority']).size().reset_index(name='counts')
+    total_counts = df.groupby(variable_select).size()
+    priority_counts['percentage'] = priority_counts.apply(lambda row: (row['counts'] / total_counts[row[variable_select]]) * 100, axis=1)
+    priority_counts['percentage'] = priority_counts['percentage'].round(2)
+    priority_counts['percentage'] = priority_counts['percentage'].astype(str) + '%' 
+    pivot_table = priority_counts.pivot(index=variable_select, columns='priority', values='percentage')
+    pivot_columns = pivot_table.columns.tolist()
+
+    print(pivot_table)
+    column_names = [variable_select, '% de incidencias', 'Media business duration', 
+                    '% de incumplimiento SLA', 'Critical', 'High', 'Moderate', 'Low','Planning'] #+ pivot_columns    
+
+    # tabla para representar la info
+    grouped_table = df.groupby(variable_select).agg({'task_id': lambda x: x.count() / df['task_id'].count() * 100,
+                                                    'business_duration': 'mean',
+                                                    'has_breached': lambda x: x.sum() / x.count() * 100})
+    grouped_table = pd.merge(grouped_table, pivot_table, on=variable_select, how='left')
+   
+    # has_brached = yes -> % de incidencias que han incumplido el SLA
+    grouped_table['task_id'] = grouped_table['task_id'].round(2)
+    grouped_table['task_id']  = grouped_table['task_id'].astype(str) + '%'
+    grouped_table['business_duration'] = grouped_table['business_duration'].round(0).astype(int)
+    grouped_table['has_breached'] = grouped_table['has_breached'].round(2)
+    grouped_table['has_breached'] = grouped_table['has_breached'].astype(str) + '%' # ponerlo en modo de barra si se puede
+
+    
+
+
+    grouped_table = grouped_table.reset_index()
+    grouped_table.columns = column_names
+
+    grouped_table.set_index(variable_select, inplace=True)
+    grouped_table['% de incumplimiento SLA'] = grouped_table['% de incumplimiento SLA'].apply(add_warning_icon)
+   
+    
+    grouped_table = grouped_table.style.set_properties(**{'text-align': 'center'}).set_table_styles([dict(selector='th',
+                                                                                                           props=[('background', '#54B4CE'), 
+                                                                                                                  ('color', 'white')])])
+    st.table(grouped_table)
+
+   
+  
+
+    
+
+
+def add_warning_icon(val):
+    if isinstance(val, str) and val.endswith('%'):
+        percentage = float(val.rstrip('%'))
+        if percentage > 10:
+            return f'{val} ⚠️'
+    return val
+
diff --git a/streamlit_app.py b/streamlit_app.py
index 69b4e41718d7..765a33a6d6b5 100644
--- a/streamlit_app.py
+++ b/streamlit_app.py
@@ -5,9 +5,9 @@
 import eda_view as view1
 import prediction_view as view2
 
-st.sidebar.title('Selecciona una vista')
-options = ['Gestión de incidencias - Análisis exploratorio de datos', 'Nueva incidencia - predicciones y LLM']
-menu = st.sidebar.selectbox('Select a view', options)
+# st.sidebar.title('Selecciona una vista')
+options = ['Gestión de incidencias EDA', 'Nueva incidencia']
+menu = st.sidebar.selectbox('Selecciona una vista', options)
 
 if menu == options[0]:
     view1.run()

From bce8b966a4634322912cc775dc8d6bf4244278af Mon Sep 17 00:00:00 2001
From: carlotagomezr <carlotagro@gmail.com>
Date: Thu, 7 Mar 2024 09:00:17 +0100
Subject: [PATCH 3/7] eda view

---
 __pycache__/eda_view.cpython-311.pyc | Bin 6889 -> 6817 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/__pycache__/eda_view.cpython-311.pyc b/__pycache__/eda_view.cpython-311.pyc
index 8edf1b43f5fbbf4ab12700fef57178fe788d0a95..9d4d88fe0245745487078e32d417e110a17b7f81 100644
GIT binary patch
delta 863
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From 53ff26e6bd820525ab84db73e838aa903ae1c4c6 Mon Sep 17 00:00:00 2001
From: carlotagomezr <carlotagro@gmail.com>
Date: Thu, 7 Mar 2024 09:05:03 +0100
Subject: [PATCH 4/7] saving

---
 __pycache__/eda_view.cpython-311.pyc | Bin 6817 -> 6892 bytes
 eda_view.py                          |   3 +++
 2 files changed, 3 insertions(+)

diff --git a/__pycache__/eda_view.cpython-311.pyc b/__pycache__/eda_view.cpython-311.pyc
index 9d4d88fe0245745487078e32d417e110a17b7f81..da00b4dff009660d7793f2b170da89e9a0ee95b8 100644
GIT binary patch
delta 273
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diff --git a/eda_view.py b/eda_view.py
index fa6bd7cac6a0..72be69f57d18 100644
--- a/eda_view.py
+++ b/eda_view.py
@@ -76,6 +76,9 @@ def run():
 
     grouped_table.set_index(variable_select, inplace=True)
     grouped_table['% de incumplimiento SLA'] = grouped_table['% de incumplimiento SLA'].apply(add_warning_icon)
+
+    # if  'Critical', 'High', 'Moderate', 'Low','Planning' == nana -> 0%
+    grouped_table = grouped_table.fillna('0%')
    
     
     grouped_table = grouped_table.style.set_properties(**{'text-align': 'center'}).set_table_styles([dict(selector='th',

From 554923b230f1b4f18a83e25ef62793e275e0fd90 Mon Sep 17 00:00:00 2001
From: carlotagomezr <carlotagro@gmail.com>
Date: Thu, 7 Mar 2024 10:54:29 +0100
Subject: [PATCH 5/7] changed menu format

---
 __pycache__/eda_view.cpython-311.pyc        | Bin 6892 -> 8760 bytes
 __pycache__/prediction_view.cpython-311.pyc | Bin 1452 -> 1448 bytes
 eda_view.py                                 | 109 +++++++++++++++-----
 sentiment-analysis-view.py                  |  18 ++++
 streamlit_app.py                            |  32 ++++--
 5 files changed, 130 insertions(+), 29 deletions(-)
 create mode 100644 sentiment-analysis-view.py

diff --git a/__pycache__/eda_view.cpython-311.pyc b/__pycache__/eda_view.cpython-311.pyc
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GIT binary patch
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diff --git a/__pycache__/prediction_view.cpython-311.pyc b/__pycache__/prediction_view.cpython-311.pyc
index a3347da92ac56beb03bf8802774ccfbc80973607..828d1221ec0a336fe9231e8922578dcf552095b9 100644
GIT binary patch
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diff --git a/eda_view.py b/eda_view.py
index 72be69f57d18..2559fd81794f 100644
--- a/eda_view.py
+++ b/eda_view.py
@@ -6,32 +6,43 @@
 import seaborn as sns
 import plotly.express as px
 
+
+
 def run():  
-    df = pd.read_excel('/Users/carlotapersonal/Library/CloudStorage/OneDrive-UFV/CURSO_5/PFG/Code/proyecto-fin-de-grado-2024-2-carlotagomezr/data-analysis/eda/dataset_post_EDA.xlsx')
 
-    # TITULO -----------------------------------------------------------------------------------
+    set_page_layout('80%', 'center')
+
+
+    #df = pd.read_excel('/Users/carlotapersonal/Library/CloudStorage/OneDrive-UFV/CURSO_5/PFG/Code/proyecto-fin-de-grado-2024-2-carlotagomezr/data-analysis/eda/dataset_post_EDA.xlsx')
+    df = pd.read_excel('/Users/carlotro/Desktop/Escritorio/Personal-Carlota/UFV/PFG/APP-REPO/dataset_post_EDA.xlsx')
+    print(df.columns.tolist())
+    
+    # 1) TITULO -----------------------------------------------------------------------------------
     st.title('Gestión de incidencias - EDA')
-    # Mostrar el número total de incidencias
-    total_incidencias = df['task_id'].nunique()  
+
+    total_incidencias = df['task_id'].nunique()  # Mostrar el número total de incidencias
     st.write(f'Número total de incidencias: {total_incidencias}')
 
 
-    # GRAFICO DE BARRAS, ANALISIS TEMPORAL ---------------------------------------------------------------------------------
+    # 2) GRAFICO DE BARRAS, ANALISIS TEMPORAL ---------------------------------------------------------------------------------
     df['end_time'] = pd.to_datetime(df['end_time'])  
     df.set_index('end_time', inplace=True) 
-    variable = st.selectbox('Seleccione la variable para agrupar:', ['priority', 'has_breached', 'assignment_group', 'company', 'contact_type'])
+    variable = st.selectbox('Seleccione la variable para agrupar:', ['priority', 'has_breached', 'assignment_group', 'company', 'contact_type','impact'])
 
     # Agrupar por la variable seleccionada y resample por mes
     grouped = df.groupby(variable).resample('M')['task_id'].nunique().reset_index()
 
     # Crear un gráfico de barras apilado con colores basados en la variable seleccionada
-    fig = px.bar(grouped, x='end_time', y='task_id', color=variable, title='Número de incidencias por mes', labels={'task_id':'Número de incidencias'}, height=400)
+    fig = px.bar(grouped, x='end_time', y='task_id', color=variable, title='Número de incidencias por mes', 
+                 labels={'task_id':'Número de incidencias'}, height=400)
     fig.update_layout(barmode='stack')
     fig.update_xaxes(title_text='Mes y año de finalización')
     st.plotly_chart(fig)
 
+  
+
 
-    # GRAFICO DE BARRAS CON PRIORITY VS TIME_WORKED ---------------------------------------------------------------------------
+    # 3) GRAFICO DE BARRAS CON PRIORITY VS TIME_WORKED ---------------------------------------------------------------------------
     fig2 = px.bar(df, x='time_worked', y='priority', color=variable)
     fig2.update_layout(
         title='Relación entre la prioridad y el tiempo trabajado',
@@ -39,8 +50,22 @@ def run():
         yaxis_title='Prioridad'
     )
     st.plotly_chart(fig2)
+    
+    # 4) TABLA FORMAS DE CONTACTO -------------------------------------------------------------------------------------------
+    var_select = st.selectbox('Seleccione la variable para agrupar:', ['assignment_group', 'company', 'priority'])
+    fig3 = px.bar(df, x=var_select, y='task_id', color='contact_type', labels={'task_id':'Número de incidencias'})
+    fig3.update_layout(
+        title= f'{var_select} vs tipo de contacto',
+        xaxis_title=var_select,
+        yaxis_title='Número de incidencias'
+    )
+    ax = plt.gca()  # get current axes
+    ax.set_yticklabels([]) 
+    fig3.update_layout(barmode='stack')
+    st.plotly_chart(fig3)
+
 
-    # TABLA SUMMARY BY GROUP, COMPANY AND USER TYPE ---------------------------------------------------------------------------
+    # 5) TABLA SUMMARY BY GROUP, COMPANY AND USER TYPE ---------------------------------------------------------------------------
     variable_select = st.selectbox('Seleccione la variable para agrupar:', ['assignment_group', 'company', 'contact_type'])
    
     priority_counts = df.groupby([variable_select, 'priority']).size().reset_index(name='counts')
@@ -49,11 +74,19 @@ def run():
     priority_counts['percentage'] = priority_counts['percentage'].round(2)
     priority_counts['percentage'] = priority_counts['percentage'].astype(str) + '%' 
     pivot_table = priority_counts.pivot(index=variable_select, columns='priority', values='percentage')
-    pivot_columns = pivot_table.columns.tolist()
+    #pivot_columns = pivot_table.columns.tolist()
 
     print(pivot_table)
     column_names = [variable_select, '% de incidencias', 'Media business duration', 
-                    '% de incumplimiento SLA', 'Critical', 'High', 'Moderate', 'Low','Planning'] #+ pivot_columns    
+                    '% incumplimiento SLA', 'Critical', 'High', 'Moderate', 'Low','Planning'] #+ pivot_columns    
+
+    # column_names = pd.MultiIndex.from_tuples([(None, variable_select),
+    #                                         (None,'% de incidencias'),
+    #                                         (None, 'Media business duration'),
+    #                                         (None, '% infracción SLA'),
+    #                                         ('Priority', 'Critical'), ('Priority', 'High'), 
+    #                                         ('Priority', 'Moderate'), ('Priority', 'Low'), ('Priority', 'Planning')])
+
 
     # tabla para representar la info
     grouped_table = df.groupby(variable_select).agg({'task_id': lambda x: x.count() / df['task_id'].count() * 100,
@@ -68,29 +101,35 @@ def run():
     grouped_table['has_breached'] = grouped_table['has_breached'].round(2)
     grouped_table['has_breached'] = grouped_table['has_breached'].astype(str) + '%' # ponerlo en modo de barra si se puede
 
-    
-
-
     grouped_table = grouped_table.reset_index()
     grouped_table.columns = column_names
 
     grouped_table.set_index(variable_select, inplace=True)
-    grouped_table['% de incumplimiento SLA'] = grouped_table['% de incumplimiento SLA'].apply(add_warning_icon)
+    grouped_table['% incumplimiento SLA'] = grouped_table['% incumplimiento SLA'].apply(add_warning_icon)
+    grouped_table = grouped_table.fillna('0%') # if  'Critical', 'High', 'Moderate', 'Low','Planning' == nana -> 0%
 
-    # if  'Critical', 'High', 'Moderate', 'Low','Planning' == nana -> 0%
-    grouped_table = grouped_table.fillna('0%')
-   
-    
-    grouped_table = grouped_table.style.set_properties(**{'text-align': 'center'}).set_table_styles([dict(selector='th',
-                                                                                                           props=[('background', '#54B4CE'), 
-                                                                                                                  ('color', 'white')])])
+    # apply styles
+    grouped_table = grouped_table.style.applymap(color_cell_priority).set_properties(**{'text-align': 'center'}).set_table_styles([dict(selector='th', props=[('background', '#54B4CE'), ('color', 'white')])])
     st.table(grouped_table)
 
+
+    
+    
+    
+    # 6) GRAFICO RELACION ---------------------------------------------------------------------------------
+    # quiero hacer un grafico --> Reassignment Count, 24_7, and Contact Type'
+    # plt.figure(figsize=(10, 6))
+    # sns.scatterplot(x='reassignment_count', y='u_24_7', hue='contact_type', data=df)
+    # plt.title('Relationship between Reassignment Count, Resolution Time, and Contact Type')
+    # plt.show()
+
+
+
    
   
 
     
-
+# FUNCIONES ---------------------------------------------------------------------------------
 
 def add_warning_icon(val):
     if isinstance(val, str) and val.endswith('%'):
@@ -99,3 +138,27 @@ def add_warning_icon(val):
             return f'{val} ⚠️'
     return val
 
+def color_cell_priority(val):
+    priority = ['Critical', 'High', 'Moderate', 'Low', 'Planning']
+    if isinstance(val, str):
+        if val in priority:
+            percentage = float(val.rstrip('%'))
+            if percentage > 50:
+                return 'background-color: yellow'
+    return ''
+
+def set_page_layout(page_width='70%', page_align='center'):
+    st.markdown(
+        f"""
+        <style>
+            .reportview-container .main .block-container{{
+                max-width: {page_width};
+                padding-top: 1rem;
+                padding-right: 1rem;
+                padding-left: 1rem;
+                margin-{page_align}-auto;
+            }}
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
diff --git a/sentiment-analysis-view.py b/sentiment-analysis-view.py
new file mode 100644
index 000000000000..967918b72df6
--- /dev/null
+++ b/sentiment-analysis-view.py
@@ -0,0 +1,18 @@
+import altair as alt
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+mpl.use('TkAgg')
+import streamlit as st
+import seaborn as sns
+import plotly.express as px
+
+
+def run():
+
+    #df = pd.read_excel('/Users/carlotapersonal/Library/CloudStorage/OneDrive-UFV/CURSO_5/PFG/Code/proyecto-fin-de-grado-2024-2-carlotagomezr/data-analysis/eda/dataset_post_EDA.xlsx')
+    df = pd.read_excel('/Users/carlotro/Desktop/Escritorio/Personal-Carlota/UFV/PFG/APP-REPO/dataset_post_EDA.xlsx')
+
+    
+   
\ No newline at end of file
diff --git a/streamlit_app.py b/streamlit_app.py
index 765a33a6d6b5..09fffb905c3d 100644
--- a/streamlit_app.py
+++ b/streamlit_app.py
@@ -4,12 +4,32 @@
 import streamlit as st
 import eda_view as view1
 import prediction_view as view2
+import streamlit_option_menu
+from streamlit_option_menu import option_menu
 
-# st.sidebar.title('Selecciona una vista')
-options = ['Gestión de incidencias EDA', 'Nueva incidencia']
-menu = st.sidebar.selectbox('Selecciona una vista', options)
+# # st.sidebar.title('Selecciona una vista')
+# options = ['Gestión de incidencias EDA', 'Nueva incidencia']
+# menu = st.sidebar.selectbox('Selecciona una vista', options)
 
-if menu == options[0]:
+# if menu == options[0]:
+#     view1.run()
+# elif menu == options[1]:
+#     view2.run()
+
+
+
+with st.sidebar:
+    selected = option_menu(
+    menu_title = "Menu",
+    options = ['Gestión de incidencias EDA', 'Nueva incidencia'],
+    icons = ["house","gear"],
+    menu_icon = "cast",
+    default_index = 0,
+    #orientation = "horizontal",
+)
+    
+
+if selected == 'Gestión de incidencias EDA':
     view1.run()
-elif menu == options[1]:
-    view2.run()
+elif selected == 'Nueva incidencia':
+    view2.run() 
\ No newline at end of file

From 4cc37da380d18e19eb55f1f2382d8e6979791edd Mon Sep 17 00:00:00 2001
From: carlotagomezr <carlotagro@gmail.com>
Date: Thu, 7 Mar 2024 12:43:17 +0100
Subject: [PATCH 6/7] changing layout dashbaord

---
 __pycache__/eda_view.cpython-311.pyc | Bin 8760 -> 14902 bytes
 eda_view.py                          | 186 ++++++++++++++++++++-------
 streamlit_app.py                     |  27 ++--
 3 files changed, 148 insertions(+), 65 deletions(-)

diff --git a/__pycache__/eda_view.cpython-311.pyc b/__pycache__/eda_view.cpython-311.pyc
index 85635a188868812e55feaa45039ea01e11102541..93ac427f868b88b95e4216bc041071bb377f557f 100644
GIT binary patch
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diff --git a/eda_view.py b/eda_view.py
index 2559fd81794f..2e0c1386b032 100644
--- a/eda_view.py
+++ b/eda_view.py
@@ -5,67 +5,83 @@
 import streamlit as st
 import seaborn as sns
 import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
 
 
 
-def run():  
+def run(df):  
+    col = st.columns((4,4), gap='medium')
 
-    set_page_layout('80%', 'center')
+    # Primera fila con una sola columna
+    row1 = st.container()
 
+    # Segunda fila con dos columnas
+    col1, col2 = st.columns(2)
+
+    # Tercera fila con dos columnas
+    col3, col4 = st.columns(2)
+
+    # Cuarta fila con una sola columna
+    row2 = st.container()
+
+    # Ahora puedes usar row1, col1, col2, col3, col4, y row2 para agregar contenido a tu dashboard
+    # Por ejemplo:
+    row1.header("Este es el encabezado de la primera fila")
+    col1.write("Este es el contenido de la segunda fila, primera columna")
+    col2.write("Este es el contenido de la segunda fila, segunda columna")
+    col3.write("Este es el contenido de la tercera fila, primera columna")
+    col4.write("Este es el contenido de la tercera fila, segunda columna")
+    row2.header("Este es el encabezado de la cuarta fila")
 
-    #df = pd.read_excel('/Users/carlotapersonal/Library/CloudStorage/OneDrive-UFV/CURSO_5/PFG/Code/proyecto-fin-de-grado-2024-2-carlotagomezr/data-analysis/eda/dataset_post_EDA.xlsx')
-    df = pd.read_excel('/Users/carlotro/Desktop/Escritorio/Personal-Carlota/UFV/PFG/APP-REPO/dataset_post_EDA.xlsx')
-    print(df.columns.tolist())
-    
-    # 1) TITULO -----------------------------------------------------------------------------------
-    st.title('Gestión de incidencias - EDA')
+
+    st.title('Gestión de incidencias - Overview')
 
     total_incidencias = df['task_id'].nunique()  # Mostrar el número total de incidencias
     st.write(f'Número total de incidencias: {total_incidencias}')
 
+    # Insertar graficos
+    variable = st.selectbox('Seleccione la variable para agrupar:', ['priority', 'has_breached', 'assignment_group', 'company', 'contact_type','impact'])
 
-    # 2) GRAFICO DE BARRAS, ANALISIS TEMPORAL ---------------------------------------------------------------------------------
+    incidencias_timeline(df, variable) # grafico de barras, analisis temporal
+    tiempo_trabajado(df, variable) # priority vs time worked
+    
+    # contact_type(df)
+    # pie_charts(df)
+    # tabla(df) # summary table
+    # pie_chart_menos_24h(df)
+    # pie_chart_mas_24h(df)
+    # pie_chart_reasigned(df)
+    # pie_chart_not_reasigned(df)
+    
+    
+    
+# FUNCIONES GRAFICOS ---------------------------------------------------------------------------------
+def incidencias_timeline(df, variable):
     df['end_time'] = pd.to_datetime(df['end_time'])  
     df.set_index('end_time', inplace=True) 
-    variable = st.selectbox('Seleccione la variable para agrupar:', ['priority', 'has_breached', 'assignment_group', 'company', 'contact_type','impact'])
-
-    # Agrupar por la variable seleccionada y resample por mes
+    
     grouped = df.groupby(variable).resample('M')['task_id'].nunique().reset_index()
 
     # Crear un gráfico de barras apilado con colores basados en la variable seleccionada
-    fig = px.bar(grouped, x='end_time', y='task_id', color=variable, title='Número de incidencias por mes', 
+    incidencias_timeline_fig = px.bar(grouped, x='end_time', y='task_id', color=variable, title='Número de incidencias por mes', 
                  labels={'task_id':'Número de incidencias'}, height=400)
-    fig.update_layout(barmode='stack')
-    fig.update_xaxes(title_text='Mes y año de finalización')
-    st.plotly_chart(fig)
-
-  
-
-
-    # 3) GRAFICO DE BARRAS CON PRIORITY VS TIME_WORKED ---------------------------------------------------------------------------
-    fig2 = px.bar(df, x='time_worked', y='priority', color=variable)
-    fig2.update_layout(
+    incidencias_timeline_fig.update_layout(barmode='stack')
+    incidencias_timeline_fig.update_xaxes(title_text='Mes y año de finalización')
+    st.plotly_chart(incidencias_timeline_fig)
+    return incidencias_timeline_fig
+
+def tiempo_trabajado(df, variable):
+    tiempo_trabajado_fig = px.bar(df, y='time_worked', x='priority', color=variable)
+    tiempo_trabajado_fig.update_layout(
         title='Relación entre la prioridad y el tiempo trabajado',
-        xaxis_title='Tiempo trabajado',
-        yaxis_title='Prioridad'
+        yaxis_title='Tiempo trabajado',
+        xaxis_title='Prioridad'
     )
-    st.plotly_chart(fig2)
-    
-    # 4) TABLA FORMAS DE CONTACTO -------------------------------------------------------------------------------------------
-    var_select = st.selectbox('Seleccione la variable para agrupar:', ['assignment_group', 'company', 'priority'])
-    fig3 = px.bar(df, x=var_select, y='task_id', color='contact_type', labels={'task_id':'Número de incidencias'})
-    fig3.update_layout(
-        title= f'{var_select} vs tipo de contacto',
-        xaxis_title=var_select,
-        yaxis_title='Número de incidencias'
-    )
-    ax = plt.gca()  # get current axes
-    ax.set_yticklabels([]) 
-    fig3.update_layout(barmode='stack')
-    st.plotly_chart(fig3)
+    #st.plotly_chart(tiempo_trabajado_fig)   
+    return tiempo_trabajado_fig 
 
-
-    # 5) TABLA SUMMARY BY GROUP, COMPANY AND USER TYPE ---------------------------------------------------------------------------
+def tabla(df):
     variable_select = st.selectbox('Seleccione la variable para agrupar:', ['assignment_group', 'company', 'contact_type'])
    
     priority_counts = df.groupby([variable_select, 'priority']).size().reset_index(name='counts')
@@ -112,24 +128,94 @@ def run():
     grouped_table = grouped_table.style.applymap(color_cell_priority).set_properties(**{'text-align': 'center'}).set_table_styles([dict(selector='th', props=[('background', '#54B4CE'), ('color', 'white')])])
     st.table(grouped_table)
 
+def contact_type(df):
+    var_select = st.selectbox('Seleccione la variable para agrupar:', ['assignment_group', 'company', 'priority'])
+    fig3 = px.bar(df, x=var_select, y='task_id', color='contact_type', labels={'task_id':'Número de incidencias'})
+    fig3.update_layout(
+        title= f'{var_select} vs tipo de contacto',
+        xaxis_title=var_select,
+        yaxis_title='Número de incidencias'
+    )
+    ax = plt.gca()  # get current axes
+    ax.set_yticklabels([]) 
+    fig3.update_layout(barmode='stack')
+    st.plotly_chart(fig3)
 
+def pie_charts(df):
+    selection_variable = st.selectbox('Seleccione la variable para agrupar:', ['reassingment_count_bool','u_24_7'])
+    contact_types = df['contact_type'].unique()
     
+    fig6 = make_subplots(rows=1, cols=len(contact_types), subplot_titles=contact_types, specs=[[{'type':'domain'}]*len(contact_types)])
+
+    for i, contact_type in enumerate(contact_types):
+        data = df[df['contact_type'] == contact_type][selection_variable].value_counts()
+        fig6.add_trace(go.Pie(labels=data.index, values=data, name=contact_type), 1, i+1)
+
+    fig6.update_layout(height=600, width=900, title_text=f'Tipo de contacto vs {selection_variable}',
+                       legend_title_text=f'{selection_variable}', title_x=0.5, title_y=0.95)
+    st.plotly_chart(fig6)
+
+def pie_chart_menos_24h(df):
     
+    df['less_24h'] = df['u_24_7'].apply(lambda x: 1 if x == True else 0) # 1 si ha sido resuelta en menos de 24h
+
+    # guardar en una variable el % de incidencias que han sido resueltas en menos de 24h
+    porcentaje_less = df['less_24h'].sum() / df['less_24h'].count() * 100
     
-    # 6) GRAFICO RELACION ---------------------------------------------------------------------------------
-    # quiero hacer un grafico --> Reassignment Count, 24_7, and Contact Type'
-    # plt.figure(figsize=(10, 6))
-    # sns.scatterplot(x='reassignment_count', y='u_24_7', hue='contact_type', data=df)
-    # plt.title('Relationship between Reassignment Count, Resolution Time, and Contact Type')
-    # plt.show()
+    df_filtered = df[df['less_24h'] == 1] # solo aquellas incidencias que han sido resueltas en menos de 24h 
 
+    # un único pie chart coloreado por tipo de contacto, indicando el % de incidencias que han sido resueltas en menos de 24h
+    pie = px.pie(df_filtered, names='contact_type', title=f'Incidencias resueltas en menos de 24h - {porcentaje_less:.2f}%', 
+                 labels={'contact_type':'Tipo de contacto'}, height=400)
 
+    st.plotly_chart(pie)
 
-   
-  
+def pie_chart_mas_24h(df):
+    
+    df['more_24h'] = df['u_24_7'].apply(lambda x: 1 if x == False else 0) # 1 si ha sido resuelta en menos de 24h
 
+    # guardar en una variable el % de incidencias que han sido resueltas en menos de 24h
+    porcentaje_more = df['more_24h'].sum() / df['less_24h'].count() * 100
     
-# FUNCIONES ---------------------------------------------------------------------------------
+    df_filtered = df[df['more_24h'] == 1] # solo aquellas incidencias que han sido resueltas en menos de 24h 
+
+    # un único pie chart coloreado por tipo de contacto, indicando el % de incidencias que han sido resueltas en menos de 24h
+    pie = px.pie(df_filtered, names='contact_type', title=f'Incidencias resueltas en más de 24h - {porcentaje_more:.2f}%', 
+                 labels={'contact_type':'Tipo de contacto'}, height=400)
+
+    st.plotly_chart(pie)
+
+def pie_chart_reasigned(df):
+    
+    
+    # guardar en una variable el % de incidencias que han sido resueltas en menos de 24h
+    porcentaje_reasigned = df['reassingment_count_bool'].sum() / df['reassingment_count_bool'].count() * 100
+    
+    df_filtered = df[df['reassingment_count_bool'] == 1] # solo aquellas incidencias que han sido resueltas en menos de 24h 
+
+    # un único pie chart coloreado por tipo de contacto, indicando el % de incidencias que han sido resueltas en menos de 24h
+    pie = px.pie(df_filtered, names='contact_type', title=f'Incidencias reasignadas - {porcentaje_reasigned:.2f}%', 
+                 labels={'contact_type':'Tipo de contacto'}, height=400)
+
+    st.plotly_chart(pie)
+
+def pie_chart_not_reasigned(df):
+    # cambiar los 1 por 0 y los 0 por 1
+    df['reassingment_count_bool'] = df['reassingment_count_bool'].apply(lambda x: 0 if x == 1 else 1)
+    # guardar en una variable el % de incidencias que han sido resueltas en menos de 24h
+    porcentaje_not_reasigned = df['reassingment_count_bool'].sum() / df['reassingment_count_bool'].count() * 100
+    
+    df_filtered = df[df['reassingment_count_bool'] == 1] # solo aquellas incidencias que han sido resueltas en menos de 24h 
+
+    # un único pie chart coloreado por tipo de contacto, indicando el % de incidencias que han sido resueltas en menos de 24h
+    pie = px.pie(df_filtered, names='contact_type', title=f'Incidencias no reasignadas - {porcentaje_not_reasigned:.2f}%', 
+                 labels={'contact_type':'Tipo de contacto'}, height=400)
+
+    st.plotly_chart(pie)
+
+
+
+# FUNCIONES AUXILIARES ---------------------------------------------------------------------------------
 
 def add_warning_icon(val):
     if isinstance(val, str) and val.endswith('%'):
diff --git a/streamlit_app.py b/streamlit_app.py
index 09fffb905c3d..928792878d7b 100644
--- a/streamlit_app.py
+++ b/streamlit_app.py
@@ -7,29 +7,26 @@
 import streamlit_option_menu
 from streamlit_option_menu import option_menu
 
-# # st.sidebar.title('Selecciona una vista')
-# options = ['Gestión de incidencias EDA', 'Nueva incidencia']
-# menu = st.sidebar.selectbox('Selecciona una vista', options)
-
-# if menu == options[0]:
-#     view1.run()
-# elif menu == options[1]:
-#     view2.run()
-
-
+st.set_page_config(layout="wide",
+                   initial_sidebar_state="expanded")
+alt.themes.enable("dark")
 
 with st.sidebar:
+    st.title("Gestión de incidencias")
     selected = option_menu(
     menu_title = "Menu",
-    options = ['Gestión de incidencias EDA', 'Nueva incidencia'],
+    options = ['Overview', 'Nueva incidencia'],
     icons = ["house","gear"],
     menu_icon = "cast",
     default_index = 0,
-    #orientation = "horizontal",
 )
     
+#df = pd.read_excel('/Users/carlotapersonal/Library/CloudStorage/OneDrive-UFV/CURSO_5/PFG/Code/proyecto-fin-de-grado-2024-2-carlotagomezr/data-analysis/eda/dataset_post_EDA.xlsx')
+df = pd.read_excel('/Users/carlotro/Desktop/Escritorio/Personal-Carlota/UFV/PFG/APP-REPO/dataset_post_EDA.xlsx')
+df['reassingment_count_bool'] = df['reassignment_count'].apply(lambda x: 1 if x > 0 else 0) # indicar si ha habido reasignacion o no
+ 
 
-if selected == 'Gestión de incidencias EDA':
-    view1.run()
+if selected == 'Overview':
+    view1.run(df)
 elif selected == 'Nueva incidencia':
-    view2.run() 
\ No newline at end of file
+    view2.run(df) 
\ No newline at end of file

From f2f6de75d8dace97abcee545f193efa0cb6800ee Mon Sep 17 00:00:00 2001
From: carlotagomezr <carlotagro@gmail.com>
Date: Thu, 7 Mar 2024 13:26:16 +0100
Subject: [PATCH 7/7] eda view is ok, still improve design

---
 __pycache__/eda_view.cpython-311.pyc | Bin 14902 -> 13845 bytes
 eda_view.py                          |  95 ++++++++++++++-------------
 sentiment-analysis-view.py           |   6 --
 3 files changed, 48 insertions(+), 53 deletions(-)

diff --git a/__pycache__/eda_view.cpython-311.pyc b/__pycache__/eda_view.cpython-311.pyc
index 93ac427f868b88b95e4216bc041071bb377f557f..5b5dbf3c9ba105d5cfd5fc0c1088caa192054089 100644
GIT binary patch
delta 5591
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diff --git a/eda_view.py b/eda_view.py
index 2e0c1386b032..69ce74af607d 100644
--- a/eda_view.py
+++ b/eda_view.py
@@ -11,50 +11,51 @@
 
 
 def run(df):  
-    col = st.columns((4,4), gap='medium')
-
+    # LAYOUT DEL DASHBOARD
     # Primera fila con una sola columna
-    row1 = st.container()
-
+    row1,row12 = st.columns((2,1), gap='medium')
     # Segunda fila con dos columnas
     col1, col2 = st.columns(2)
-
     # Tercera fila con dos columnas
-    col3, col4 = st.columns(2)
-
-    # Cuarta fila con una sola columna
+    col3, col4, col5, col6 = st.columns(4)
+    # quarta fila con una sola columna
+    row21 = st.container()
+    # Quinta fila con una sola columna
     row2 = st.container()
-
-    # Ahora puedes usar row1, col1, col2, col3, col4, y row2 para agregar contenido a tu dashboard
-    # Por ejemplo:
-    row1.header("Este es el encabezado de la primera fila")
-    col1.write("Este es el contenido de la segunda fila, primera columna")
-    col2.write("Este es el contenido de la segunda fila, segunda columna")
-    col3.write("Este es el contenido de la tercera fila, primera columna")
-    col4.write("Este es el contenido de la tercera fila, segunda columna")
-    row2.header("Este es el encabezado de la cuarta fila")
-
-
-    st.title('Gestión de incidencias - Overview')
+  
+    # Añadimos contenido
+    row1.header('Gestión de incidencias - Overview')
 
     total_incidencias = df['task_id'].nunique()  # Mostrar el número total de incidencias
-    st.write(f'Número total de incidencias: {total_incidencias}')
+    row12.markdown(f'<div style="border:1px solid black; padding:10px;">Número total de incidencias: {total_incidencias}</div>', unsafe_allow_html=True)
 
     # Insertar graficos
-    variable = st.selectbox('Seleccione la variable para agrupar:', ['priority', 'has_breached', 'assignment_group', 'company', 'contact_type','impact'])
-
-    incidencias_timeline(df, variable) # grafico de barras, analisis temporal
-    tiempo_trabajado(df, variable) # priority vs time worked
-    
-    # contact_type(df)
-    # pie_charts(df)
-    # tabla(df) # summary table
-    # pie_chart_menos_24h(df)
-    # pie_chart_mas_24h(df)
-    # pie_chart_reasigned(df)
-    # pie_chart_not_reasigned(df)
-    
+    variable = col1.selectbox('Seleccione la variable para agrupar:', ['priority', 'has_breached', 'assignment_group', 'company', 'contact_type','impact'])
+    incidencias_timeline_fig = incidencias_timeline(df, variable) # grafico de barras, analisis temporal
+    tiempo_trabajado_fig = tiempo_trabajado(df, variable) # priority vs time worked
+    col1.plotly_chart(incidencias_timeline_fig)
+    col2.plotly_chart(tiempo_trabajado_fig)
+
+    # Pie charts 
+    pie1 = pie_chart_menos_24h(df)
+    pie2 = pie_chart_mas_24h(df)
+    pie3 = pie_chart_reasigned(df)
+    pie4 = pie_chart_not_reasigned(df)
+
+    col3.plotly_chart(pie1)
+    col4.plotly_chart(pie2)
+    col5.plotly_chart(pie3)
+    col6.plotly_chart(pie4)
+
+
+    var_select = col4.selectbox('Seleccione la variable para agrupar:', ['assignment_group', 'company', 'priority'])
+    contact_type_fig = contact_type(df, var_select)
+    row21.plotly_chart(contact_type_fig)
     
+    variable_select = row2.selectbox('Seleccione la variable para agrupar:', ['assignment_group', 'company', 'contact_type'])
+    table = tabla(df, variable_select)
+    row2.table(table)
+
     
 # FUNCIONES GRAFICOS ---------------------------------------------------------------------------------
 def incidencias_timeline(df, variable):
@@ -68,7 +69,7 @@ def incidencias_timeline(df, variable):
                  labels={'task_id':'Número de incidencias'}, height=400)
     incidencias_timeline_fig.update_layout(barmode='stack')
     incidencias_timeline_fig.update_xaxes(title_text='Mes y año de finalización')
-    st.plotly_chart(incidencias_timeline_fig)
+    #st.plotly_chart(incidencias_timeline_fig)
     return incidencias_timeline_fig
 
 def tiempo_trabajado(df, variable):
@@ -81,9 +82,7 @@ def tiempo_trabajado(df, variable):
     #st.plotly_chart(tiempo_trabajado_fig)   
     return tiempo_trabajado_fig 
 
-def tabla(df):
-    variable_select = st.selectbox('Seleccione la variable para agrupar:', ['assignment_group', 'company', 'contact_type'])
-   
+def tabla(df, variable_select):
     priority_counts = df.groupby([variable_select, 'priority']).size().reset_index(name='counts')
     total_counts = df.groupby(variable_select).size()
     priority_counts['percentage'] = priority_counts.apply(lambda row: (row['counts'] / total_counts[row[variable_select]]) * 100, axis=1)
@@ -126,10 +125,10 @@ def tabla(df):
 
     # apply styles
     grouped_table = grouped_table.style.applymap(color_cell_priority).set_properties(**{'text-align': 'center'}).set_table_styles([dict(selector='th', props=[('background', '#54B4CE'), ('color', 'white')])])
-    st.table(grouped_table)
+    #st.table(grouped_table)
+    return grouped_table
 
-def contact_type(df):
-    var_select = st.selectbox('Seleccione la variable para agrupar:', ['assignment_group', 'company', 'priority'])
+def contact_type(df, var_select):
     fig3 = px.bar(df, x=var_select, y='task_id', color='contact_type', labels={'task_id':'Número de incidencias'})
     fig3.update_layout(
         title= f'{var_select} vs tipo de contacto',
@@ -139,9 +138,10 @@ def contact_type(df):
     ax = plt.gca()  # get current axes
     ax.set_yticklabels([]) 
     fig3.update_layout(barmode='stack')
-    st.plotly_chart(fig3)
+    #st.plotly_chart(fig3)
+    return fig3
 
-def pie_charts(df):
+#def pie_charts(df):
     selection_variable = st.selectbox('Seleccione la variable para agrupar:', ['reassingment_count_bool','u_24_7'])
     contact_types = df['contact_type'].unique()
     
@@ -168,7 +168,8 @@ def pie_chart_menos_24h(df):
     pie = px.pie(df_filtered, names='contact_type', title=f'Incidencias resueltas en menos de 24h - {porcentaje_less:.2f}%', 
                  labels={'contact_type':'Tipo de contacto'}, height=400)
 
-    st.plotly_chart(pie)
+    #st.plotly_chart(pie)
+    return pie
 
 def pie_chart_mas_24h(df):
     
@@ -183,7 +184,7 @@ def pie_chart_mas_24h(df):
     pie = px.pie(df_filtered, names='contact_type', title=f'Incidencias resueltas en más de 24h - {porcentaje_more:.2f}%', 
                  labels={'contact_type':'Tipo de contacto'}, height=400)
 
-    st.plotly_chart(pie)
+    return pie
 
 def pie_chart_reasigned(df):
     
@@ -197,7 +198,7 @@ def pie_chart_reasigned(df):
     pie = px.pie(df_filtered, names='contact_type', title=f'Incidencias reasignadas - {porcentaje_reasigned:.2f}%', 
                  labels={'contact_type':'Tipo de contacto'}, height=400)
 
-    st.plotly_chart(pie)
+    return pie
 
 def pie_chart_not_reasigned(df):
     # cambiar los 1 por 0 y los 0 por 1
@@ -211,7 +212,7 @@ def pie_chart_not_reasigned(df):
     pie = px.pie(df_filtered, names='contact_type', title=f'Incidencias no reasignadas - {porcentaje_not_reasigned:.2f}%', 
                  labels={'contact_type':'Tipo de contacto'}, height=400)
 
-    st.plotly_chart(pie)
+    return pie
 
 
 
diff --git a/sentiment-analysis-view.py b/sentiment-analysis-view.py
index 967918b72df6..1408133611f6 100644
--- a/sentiment-analysis-view.py
+++ b/sentiment-analysis-view.py
@@ -9,10 +9,4 @@
 import plotly.express as px
 
 
-def run():
 
-    #df = pd.read_excel('/Users/carlotapersonal/Library/CloudStorage/OneDrive-UFV/CURSO_5/PFG/Code/proyecto-fin-de-grado-2024-2-carlotagomezr/data-analysis/eda/dataset_post_EDA.xlsx')
-    df = pd.read_excel('/Users/carlotro/Desktop/Escritorio/Personal-Carlota/UFV/PFG/APP-REPO/dataset_post_EDA.xlsx')
-
-    
-   
\ No newline at end of file