import streamlit as st
import os.path
import pathlib
import pandas as pd
import numpy as np
import PyPDF2
from PyPDF2 import PdfReader
from os import walk
import nltk
import glob
import plotly.express as px
from wordcloud import WordCloud
import plotly.io as pio
from plotly.subplots import make_subplots
import plotly.graph_objs as go
import pandas as pd
import plotly.offline as pyo
import io
from io import StringIO
#@st.cache_resource()
@st.cache()
def get_nl():
return nltk.download('punkt')
get_nl()
from nltk.tokenize import sent_tokenize
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
# if os.path.exists("report.html"):
# os.remove("report.html")
#@st.cache_resource()
@st.cache(allow_output_mutation=True)
def get_sentiment_model():
tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")
return tokenizer,model
tokenizer_sentiment,model_sentiment = get_sentiment_model()
@st.cache(allow_output_mutation=True)
def get_emotion_model():
tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
return tokenizer,model
tokenizer_emotion,model_emotion = get_emotion_model()
@st.cache(allow_output_mutation=True)
def get_intent_model():
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-small')
return classifier
intent_classifier = get_intent_model()
def extract_text_from_pdf(path):
text=''
reader = PdfReader(path)
number_of_pages = len(reader.pages)
print(number_of_pages)
for i in range(number_of_pages):
page=reader.pages[i]
text = text + page.extract_text()
return text
# Create a button to download the HTML file
def download_html():
with st.spinner('Downloading HTML file...'):
# Get the HTML content
with open('report.html', "r") as f:
html = f.read()
f.close()
# Set the file name and content type
file_name = "report.html"
mime_type = "text/html"
# Use st.download_button() to create a download button
print('download button')
st.download_button(label="Download Report", data=html, file_name=file_name, mime=mime_type)
st.stop()
if 'filename_key' not in st.session_state:
st.session_state.filename_key = ''
st.write("""
# Dcoument Analysis Tool
""")
#uploaded_file = st.file_uploader("Choose a PDF file")
#uploaded_file = st.file_uploader("Choose a PDF file", accept_multiple_files=False, type=['pdf'])
uploaded_file = st.file_uploader("Choose a PDF file", accept_multiple_files=True, type=['pdf'])
#if uploaded_file is not None:
if len(uploaded_file)==0:
#print('none')
st.session_state.filename_key = ''
elif len(uploaded_file)>0:
import time
# Wait for 5 seconds
time.sleep(5)
pdf_reader = PyPDF2.PdfReader(uploaded_file[0])
num_pages = len(pdf_reader.pages)
file_name = uploaded_file[0].name
# st.write(st.session_state.filename_key)
# print(file_name)
# st.write("Filename:", file_name)
if num_pages > 20:
st.error("Pages in PDF file should be less than 20.")
# Check that only one file was uploaded
#elif isinstance(uploaded_file, list):
elif len(uploaded_file) > 1:
st.error("Please upload only one PDF file at a time.")
elif st.session_state.filename_key == file_name:
st.write("Report downloaded successfully")
else:
#uploaded_file = uploaded_file[0]
# Check that the file is a PDF
if uploaded_file[0].type != 'application/pdf':
st.error("Please upload a PDF file.")
else:
############################ 1. Extract text from PDF ############################
text=''
# return text from pdf
pdf_reader = PyPDF2.PdfReader(uploaded_file[0])
# Get the number of pages in the PDF file
num_pages = len(pdf_reader.pages)
# Display the number of pages in the PDF file
st.write(f"Number of pages in PDF file: {num_pages}")
for i in range(num_pages):
page=pdf_reader.pages[i]
text = text + page.extract_text()
############################ 2. Running models ############################
text = text.replace("\n", " " )
text = text.replace("$", "dollar " )
sentences = sent_tokenize(text)
title = sentences[0]
long_sentence=[]
small_sentence=[]
useful_sentence=[]
for i in sentences:
if len(i) > 510:
long_sentence.append(i)
elif len(i) < 50:
small_sentence.append(i)
else:
useful_sentence.append(i)
useful_sentence_len = len(useful_sentence)
del sentences
############################ 2.1 Sentiment Modeling ############################
placeholder1 = st.empty()
placeholder1.text('Performing Sentiment Analysis...')
#with st.empty():
my_bar = st.progress(0)
tokenizer = tokenizer_sentiment
model = model_sentiment
pipe = pipeline(model="ProsusAI/finbert")
classifier = pipeline(model="ProsusAI/finbert")
#output = classifier(useful_sentence)
output=[]
i=0
for temp in useful_sentence:
output.extend(classifier(temp))
i=i+1
my_bar.progress(int((i/useful_sentence_len)*100))
my_bar.empty()
df = pd.DataFrame.from_dict(output)
df['Sentence']= pd.Series(useful_sentence)
############################ 2.2 Emotion Modeling ############################
#placeholder2 = st.empty()
placeholder1.text('Performing Emotion Analysis...')
# with st.empty():
my_bar = st.progress(0)
tokenizer = tokenizer_emotion
model = model_emotion
classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=1)
output_emotion = []
i=0
for temp in useful_sentence:
output_emotion.extend(classifier(temp)[0])
i=i+1
my_bar.progress(int((i/useful_sentence_len)*100))
my_bar.empty()
placeholder1.text('Emotion Analysis Completed')
############################ 2.3 Intent Modeling ############################
placeholder1.text('Performing Intent Analysis...')
my_bar = st.progress(0)
candidate_labels = ['complaint', 'suggestion', 'query']
classifier = intent_classifier
# temp_intent = classifier(useful_sentence, candidate_labels)
# output_intent=[]
# for temp in temp_intent:
# output_intent.append({'label' : temp['labels'][0], 'score' : temp['scores'][0]})
output_intent=[]
i=0
for temp1 in useful_sentence:
temp = classifier(temp1, candidate_labels)
output_intent.append({'label' : temp['labels'][0], 'score' : temp['scores'][0]})
i=i+1
my_bar.progress(int((i/useful_sentence_len)*100))
df_intent = pd.DataFrame.from_dict(output_intent)
df_intent['Sentence']= pd.Series(useful_sentence)
my_bar.empty()
placeholder1.text('Processing Completed')
############################ 3. Processing ############################
############################ 3.1. Sentiment Analysis ############################
# labels = ['neutral', 'positive', 'negative']
# values = df.label.value_counts().to_list()
labels = ['neutral', 'positive', 'negative']
values = [df[df['label']=='neutral'].shape[0], df[df['label']=='positive'].shape[0], df[df['label']=='negative'].shape[0]]
# removing words
words_to_remove = ["s", "quarter", "thank", "million", "Thank", "quetion", 'wa', 'rate', 'firt',
"customer", "business", "last year", "year", 'lat', 'well', 'jut', 'thi', 'cutomer',
"will", "think", "higher", "question", "going"]
for word in words_to_remove:
text = text.replace(word, "")
wordcloud = WordCloud(background_color='white', width=800, height=400).generate(text)
image = wordcloud.to_image()
pos_df = df[df['label']=='positive']
pos_df = pos_df[['score', 'Sentence']]
pos_df = pos_df.sort_values('score', ascending=False)
pos_df_mean = pos_df.score.mean()
pos_df['score'] = pos_df['score'].round(4)
pos_df.rename(columns = {'Sentence':'Positive Sentences'}, inplace = True)
num_of_pos_sentences = pos_df.shape[0]
if num_of_pos_sentences == 0:
pos_df.loc[0] = [0.0, '-------No positive sentences found in report-------']
neg_df = df[df['label']=='negative']
neg_df = neg_df[['score', 'Sentence']]
neg_df = neg_df.sort_values('score', ascending=False)
neg_df_mean = neg_df.score.mean()
neg_df['score'] = neg_df['score'].round(4)
neg_df.rename(columns = {'Sentence':'Negative Sentences'}, inplace = True)
num_of_neg_sentences = neg_df.shape[0]
if num_of_neg_sentences == 0:
neg_df.loc[0] = [0.0, '-------No negative sentences found in report-------']
neu_df = df[df['label']=='neutral']
neu_df = neu_df[['score', 'Sentence']]
neu_df = neu_df.sort_values('score', ascending=False)
#neu_df_mean = neu_df.score.mean()
neu_df['score'] = neu_df['score'].round(4)
neu_df.rename(columns = {'Sentence':'Neutral Sentences'}, inplace = True)
num_of_neu_sentences = neu_df.shape[0]
if num_of_neu_sentences == 0:
neu_df.loc[0] = [0.0, '-------No neutral sentences found in report-------']
# df_temp = neg_df
# df_temp = df_temp['score'] * -1
# df_temp = pd.concat([df_temp, pos_df])
df_temp = neg_df
df_temp['score'] = df_temp['score'] * -1
df_temp_list = df_temp['score'].to_list() + pos_df['score'].to_list()
mean = sum(df_temp_list) / len(df_temp_list)
############################ 3.2. Emotion Analysis ############################
df_emotion = pd.DataFrame.from_dict(output_emotion)
df_emotion['Sentence']= pd.Series(useful_sentence)
df_joy = df_emotion[df_emotion['label']=='joy']
df_joy = df_joy[['score', 'Sentence']]
df_joy = df_joy.sort_values('score', ascending=False)
df_joy['score'] = df_joy['score'].round(4)
df_joy.rename(columns = {'Sentence':'Joy Sentences'}, inplace = True)
num_of_joy_sentences = df_joy.shape[0]
if num_of_joy_sentences == 0:
df_joy.loc[0] = [0.0, '-------No joy sentences found in report-------']
df_sadness = df_emotion[df_emotion['label']=='sadness']
df_sadness = df_sadness[['score', 'Sentence']]
df_sadness = df_sadness.sort_values('score', ascending=False)
df_sadness['score'] = df_sadness['score'].round(4)
df_sadness.rename(columns = {'Sentence':'Sad Sentences'}, inplace = True)
num_of_sad_sentences = df_sadness.shape[0]
if num_of_sad_sentences == 0:
df_sadness.loc[0] = [0.0, '-------No sad sentences found in report-------']
df_anger = df_emotion[df_emotion['label']=='anger']
df_anger = df_anger[['score', 'Sentence']]
df_anger = df_anger.sort_values('score', ascending=False)
df_anger['score'] = df_anger['score'].round(4)
df_anger.rename(columns = {'Sentence':'Angry Sentences'}, inplace = True)
num_of_anger_sentences = df_anger.shape[0]
if num_of_anger_sentences == 0:
df_anger.loc[0] = [0.0, '-------No angry sentences found in report-------']
df_surprise = df_emotion[df_emotion['label']=='surprise']
df_surprise = df_surprise[['score', 'Sentence']]
df_surprise = df_surprise.sort_values('score', ascending=False)
df_surprise['score'] = df_surprise['score'].round(4)
df_surprise.rename(columns = {'Sentence':'Surprised Sentences'}, inplace = True)
num_of_surprise_sentences = df_surprise.shape[0]
if num_of_surprise_sentences == 0:
df_surprise.loc[0] = [0.0, '-------No surprised sentences found in report-------']
# df_temp_emotion = df_sadness
# df_temp_emotion = pd.concat([df_sadness, df_anger])
# df_temp_emotion = df_temp_emotion['score'] * -1
# df_temp_emotion = pd.concat([df_temp_emotion, df_joy])
df_temp_emotion = df_sadness
df_temp_emotion['score'] = df_temp_emotion['score'] * -1
df_temp_emotion_list = df_temp_emotion['score'].to_list() + df_joy['score'].to_list()
emotion_mean = sum(df_temp_emotion_list) / len(df_temp_emotion_list)
# df_temp = neg_df
# df_temp['score'] = df_temp['score'] * -1
# df_temp_list = df_temp['score'].to_list() + pos_df['score'].to_list()
# mean = sum(df_temp_list) / len(df_temp_list)
############################ 3.3. Intent Analysis ############################
df_query = df_intent[df_intent['label']=='query']
df_query = df_query[['score', 'Sentence']]
df_query = df_query.sort_values('score', ascending=False)
df_query['score'] = df_query['score'].round(4)
df_query.rename(columns = {'Sentence':'Queries'}, inplace = True)
df_query = df_query[df_query['score']>0.5]
num_of_queries = df_query.shape[0]
if num_of_queries == 0:
df_query.loc[0] = [0.0, '-------No queries found in report-------']
df_complaint = df_intent[df_intent['label']=='complaint']
df_complaint = df_complaint[['score', 'Sentence']]
df_complaint = df_complaint.sort_values('score', ascending=False)
df_complaint['score'] = df_complaint['score'].round(4)
df_complaint.rename(columns = {'Sentence':'Complaints'}, inplace = True)
df_complaint = df_complaint[df_complaint['score']>0.5]
num_of_complaints = df_complaint.shape[0]
if num_of_complaints == 0:
df_complaint.loc[0] = [0.0, '-------No complaints found in report-------']
df_suggestion = df_intent[df_intent['label']=='suggestion']
df_suggestion = df_suggestion[['score', 'Sentence']]
df_suggestion = df_suggestion.sort_values('score', ascending=False)
df_suggestion['score'] = df_suggestion['score'].round(4)
df_suggestion.rename(columns = {'Sentence':'Suggestions'}, inplace = True)
df_suggestion = df_suggestion[df_suggestion['score']>0.5]
num_of_suggestions = df_suggestion.shape[0]
if num_of_suggestions == 0:
df_suggestion.loc[0] = [0.0, '-------No suggestions found in report-------']
total_num_of_intent = num_of_queries + num_of_complaints + num_of_suggestions
############################ 4. Plotting ############################
fig = make_subplots(
rows=62, cols=6,
specs=[ [None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, {"type": "indicator", "rowspan": 3, "colspan": 2}, None, None, None],
[None, None, None, None, None, None],
[{"type": "pie", "rowspan": 6, "colspan": 2}, None, {"type": "indicator", "rowspan": 6, "colspan": 2}, None, {"type": "indicator", "rowspan": 6, "colspan": 2}, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[{"type": "image", "rowspan": 5, "colspan": 3}, None, None, {"type": "table", "rowspan": 5, "colspan": 3}, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[{"type": "table", "rowspan": 5, "colspan": 3}, None, None, {"type": "table", "rowspan": 5, "colspan": 3}, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, {"type": "indicator", "rowspan": 3, "colspan": 2}, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[{"type": "bar", "rowspan": 6, "colspan": 6}, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[{"type": "table", "rowspan": 2, "colspan": 3}, None, None, {"type": "table", "rowspan": 2, "colspan": 3}, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[{"type": "table", "rowspan": 2, "colspan": 3}, None, None, {"type": "table", "rowspan": 2, "colspan": 3}, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, {"type": "indicator", "rowspan": 3, "colspan": 2}, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, {"type": "indicator", "rowspan": 2, "colspan": 5}, None, None, None, None],#first bullet
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, {"type": "indicator", "rowspan": 2, "colspan": 5}, None, None, None, None], #2nd bullet
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, {"type": "indicator", "rowspan": 2, "colspan": 5}, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[{"type": "table", "rowspan": 4, "colspan": 2}, None, {"type": "table", "rowspan": 4, "colspan": 2}, None, {"type": "table", "rowspan": 4, "colspan": 2}, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
[None, None, None, None, None, None],
],
)
############################ 4.1. Sentiment Analysis ############################
fig.add_trace(go.Indicator(
mode = "number",
value = int(mean*100),
number = {"suffix": "%"},
title = {"text": "<span style='font-size:1.5em'>Sentiment Analysis</span><br><span style='font-size:0.8em;color:gray'>Positivity Score</span>"}
), row=4, col=3)
colors = px.colors.diverging.Portland#RdBu
fig.add_trace(go.Pie(labels=labels, values=values, hole = 0.5,
title = 'Count by label',
marker=dict(colors=colors,
line=dict(width=2, color='white'))),
row=6, col=1)
fig.add_trace(go.Indicator(
mode = "number",
value = len(df.label.values.tolist()),
title = {"text": "Count of Sentence"}), row=6, col=3)
#fig.update_traces(title_text="Sentiment Analysis", selector=dict(type='indicator'), row=6, col=3)
fig.add_trace(go.Indicator(
mode = "gauge+number",
value = mean,
domain = {'x': [0, 1], 'y': [0, 1]},
title = {'text': "Average of Score", 'font': {'size': 16}},
gauge = {
'axis': {'range': [-1, 1], 'tickwidth': 1, 'tickcolor': "darkblue"},
'bar': {'color': "darkblue"},
'steps': [
{'range': [-0.29, 0.29], 'color': 'white'},
{'range': [0.3, 1], 'color': 'green'},
{'range': [-1, -0.3], 'color': 'red'}
],
'threshold': {
'line': {'color': "black", 'width': 4},
'thickness': 0.75,
'value': abs((pos_df_mean - neg_df_mean))
}
}
), row=6, col=5)
if mean < -0.29:
fig.update_traces(title_text="Cummulative Sentiment Negative", selector=dict(type='indicator'), row=6, col=5)
elif mean < 0.29:
fig.update_traces(title_text="Cummulative Sentiment Neutral", selector=dict(type='indicator'), row=6, col=5)
else:
fig.update_traces(title_text="Cummulative Sentiment Positive", selector=dict(type='indicator'), row=6, col=5)
fig.add_trace(go.Image(z=image), row=13, col=1)
fig.update_xaxes(visible=False, row=13, col=1)
fig.update_yaxes(visible=False, row=13, col=1)
table_trace1 = go.Table(
header=dict(values=list(pos_df.columns), fill_color='lightgray', align='left'),
cells=dict(values=[pos_df[name] for name in pos_df.columns], fill_color='white', align='left'),
columnwidth=[1, 4]
)
fig.add_trace(table_trace1, row=13, col=4)
table_trace2 = go.Table(
header=dict(values=list(neg_df.columns), fill_color='lightgray', align='left'),
cells=dict(values=[neg_df[name] for name in neg_df.columns], fill_color='white', align='left'),
columnwidth=[1, 4]
)
fig.add_trace(table_trace2, row=18, col=4)
table_trace2 = go.Table(
header=dict(values=list(neu_df.columns), fill_color='lightgray', align='left'),
cells=dict(values=[neu_df[name] for name in neu_df.columns], fill_color='white', align='left'),
columnwidth=[1, 4]
)
fig.add_trace(table_trace2, row=18, col=1)
########################### 4.2. Emotion Analysis ###########################
fig.add_trace(go.Indicator(
mode = "number",
value = int(emotion_mean*100),
number = {"suffix": "%"},
title = {"text": "<span style='font-size:1.5em'>Emotion Analysis</span><br><span style='font-size:0.8em;color:gray'>Happiness Score</span>"}
), row=26, col=3)
# Add bar chart
colors_emotions = ['#174ecf', '#cfc517', '#940625', '#17cfcb']
emotion_bar_xlabels = ['Joy', 'Sadness', 'Anger', 'Surprise']
emotion_bar_ylabels = [num_of_joy_sentences,
num_of_sad_sentences,
num_of_anger_sentences,
num_of_surprise_sentences]
#annotations = [dict(x=x, y=y, text='😀', showarrow=False) for x, y in zip(emotion_bar_xlabels, emotion_bar_ylabels)]
annotations = ['😀', '😞', '😡', '😯']
fig.add_trace(
go.Bar(x=emotion_bar_xlabels, y= emotion_bar_ylabels,
showlegend=True,
marker_color=colors_emotions,
text=annotations,
textfont=dict(size=40)),
row=29, col=1)
fig.update_xaxes(title_text='Emotions', title_font=dict(size=16), row=29, col=1)
fig.update_yaxes(title_text='Number of sentences', title_font=dict(size=16), row=29, col=1)
# df_anger.loc[0] = [0.0, 'None']
# df_anger
################## happiness table
table_trace2 = go.Table(
header=dict(values=list(df_joy.columns), fill_color='lightgray', align='left'),
cells=dict(values=[df_joy[name] for name in df_joy.columns], fill_color='white', align='left'),
columnwidth=[1, 4]
)
fig.add_trace(table_trace2, row=36, col=1)
################## sadness table
table_trace2 = go.Table(
header=dict(values=list(df_sadness.columns), fill_color='lightgray', align='left'),
cells=dict(values=[df_sadness[name] for name in df_sadness.columns], fill_color='white', align='left'),
columnwidth=[1, 4]
)
fig.add_trace(table_trace2, row=36, col=4)
################## surprise table
table_trace2 = go.Table(
header=dict(values=list(df_surprise.columns), fill_color='lightgray', align='left'),
cells=dict(values=[df_surprise[name] for name in df_surprise.columns], fill_color='white', align='left'),
columnwidth=[1, 4]
)
fig.add_trace(table_trace2, row=39, col=1)
################## anger table
table_trace2 = go.Table(
header=dict(values=list(df_anger.columns), fill_color='lightgray', align='left'),
cells=dict(values=[df_anger[name] for name in df_anger.columns], fill_color='white', align='left'),
columnwidth=[1, 4]
)
fig.add_trace(table_trace2, row=39, col=4)
########################### 4.3. Intent Analysis ###########################
fig.add_trace(go.Indicator(
mode = "number",
value = round(num_of_suggestions/max(num_of_complaints,0), 2),
number = {"suffix": ""},
title = {"text": "<span style='font-size:1.5em'>Intent Analysis</span><br><span style='font-size:0.8em;color:gray'>Suggestion/Complaint Ratio</span>"}
), row=44, col=3)
fig.add_trace(go.Indicator(
mode = "number+gauge",
gauge = {'shape': "bullet", 'axis': {'range': [None, total_num_of_intent]}, 'bar': {'color': "blue"}},
#delta = {'reference': 300},
value = num_of_queries,
#domain = {'x': [0.5, 1], 'y': [0.3, 0.9]},
title = {'text': "Queries"}), row=47, col=2)
fig.add_trace(go.Indicator(
mode = "number+gauge",
gauge = {'shape': "bullet", 'axis': {'range': [None, total_num_of_intent]},},
#delta = {'reference': 300},
value = num_of_suggestions,
#domain = {'x': [0.5, 1], 'y': [0.3, 0.9]},
title = {'text': "Suggestions"}), row=50, col=2)
fig.add_trace(go.Indicator(
mode = "number+gauge",
gauge = {'shape': "bullet", 'axis': {'range': [None, total_num_of_intent]}, 'bar': {'color': "red"}},
#delta = {'reference': 300},
value = num_of_complaints,
#domain = {'x': [0.5, 1], 'y': [0.3, 0.9]},
title = {'text': "Complaints"}), row=53, col=2)
############ query table
table_trace2 = go.Table(
header=dict(values=list(df_query.columns), fill_color='lightgray', align='left'),
cells=dict(values=[df_query[name] for name in df_query.columns], fill_color='white', align='left'),
columnwidth=[1, 4]
)
fig.add_trace(table_trace2, row=56, col=1)
############ complaints table
table_trace2 = go.Table(
header=dict(values=list(df_complaint.columns), fill_color='lightgray', align='left'),
cells=dict(values=[df_complaint[name] for name in df_complaint.columns], fill_color='white', align='left'),
columnwidth=[1, 4]
)
fig.add_trace(table_trace2, row=56, col=3)
############ suggestions table
table_trace2 = go.Table(
header=dict(values=list(df_suggestion.columns), fill_color='lightgray', align='left'),
cells=dict(values=[df_suggestion[name] for name in df_suggestion.columns], fill_color='white', align='left'),
columnwidth=[1, 4]
)
fig.add_trace(table_trace2, row=56, col=5)
import textwrap
if len(title) > 120:
title = title[:120] + '...'
wrapped_title = "\n".join(textwrap.wrap(title, width=50))
# Add HTML tags to force line breaks in the title text
wrapped_title = "<br>".join(wrapped_title.split("\n"))
fig.update_layout(height=4000, showlegend=False, title={'text': f"<b>{wrapped_title} - Text Analysis Report</b>", 'x': 0.5, 'xanchor': 'center','font': {'size': 32}})
#pyo.plot(fig, filename='report.html')
############################## 5. Download Report ##############################
buffer = io.StringIO()
fig.write_html(buffer, include_plotlyjs='cdn')
html_bytes = buffer.getvalue().encode()
st.download_button(
label='Download Report',
data=html_bytes,
file_name='report.html',
mime='text/html'
)
st.session_state.filename_key = file_name