import os
import pandas as pd
import numpy as np
import easyocr
import streamlit as st
from annotated_text import annotated_text
from streamlit_option_menu import option_menu
from sentiment_analysis_v2 import SentimentAnalysis
from keyword_extraction import KeywordExtractor
from part_of_speech_tagging import POSTagging
from emotion_detection import EmotionDetection
from named_entity_recognition import NamedEntityRecognition
from Object_Detector import ObjectDetector
from OCR_Detector import OCRDetector
from detect_language import LanguageDetector
import PIL
from PIL import Image
from PIL import ImageColor
from PIL import ImageDraw
from PIL import ImageFont
import time
# Imports de Object Detection
import tensorflow as tf
import tensorflow_hub as hub
# Load compressed models from tensorflow_hub
os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'
import matplotlib.pyplot as plt
import matplotlib as mpl
# For drawing onto the image.
import numpy as np
from tensorflow.python.ops.numpy_ops import np_config
np_config.enable_numpy_behavior()
import torch
import librosa
from models import infere_speech_emotion, infere_text_emotion, infere_voice2text
from transformers import pipeline
def main():
st.set_page_config(layout="wide")
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
@st.cache_resource
def load_sentiment_model():
return SentimentAnalysis()
@st.cache_resource
def load_keyword_model():
return KeywordExtractor()
@st.cache_resource
def load_pos_model():
return POSTagging()
@st.cache_resource
def load_emotion_model():
return EmotionDetection()
@st.cache_resource
def load_ner_model():
return NamedEntityRecognition()
@st.cache_resource
def load_objectdetector_model():
return ObjectDetector()
@st.cache_resource
def load_ocrdetector_model():
return OCRDetector()
@st.cache_resource
def load_langdetector_model():
return LanguageDetector()
sentiment_analyzer = load_sentiment_model()
keyword_extractor = load_keyword_model()
pos_tagger = load_pos_model()
emotion_detector = load_emotion_model()
ner = load_ner_model()
objectdetector1 = load_objectdetector_model()
ocrdetector1 = load_ocrdetector_model()
langdetector1 = load_langdetector_model()
def rectangle(image, result):
draw = ImageDraw.Draw(image)
for res in result:
top_left = tuple(res[0][0]) # top left coordinates as tuple
bottom_right = tuple(res[0][2]) # bottom right coordinates as tuple
draw.rectangle((top_left, bottom_right), outline="blue", width=2)
st.image(image)
example_text = "My name is Daniel: The attention to detail, swift resolution, and accuracy demonstrated by ITACA Insurance Company in Spain in handling my claim were truly impressive. This undoubtedly reflects their commitment to being a customer-centric insurance provider."
with st.sidebar:
image = Image.open('./itaca_logo.png')
st.image(image,width=150) #use_column_width=True)
page = option_menu(menu_title='Menu',
menu_icon="robot",
options=["Sentiment Analysis",
"Keyword Extraction",
"Part of Speech Tagging",
"Emotion Detection",
"Named Entity Recognition",
"Speech & Text Emotion",
"Object Detector",
"OCR Detector"],
icons=["chat-dots",
"key",
"tag",
"emoji-heart-eyes",
"building",
"book",
"camera",
"list-task"],
default_index=0
)
st.title('ITACA Insurance Core AI Module')
# Replace '20px' with your desired font size
font_size = '20px'
if page == "Sentiment Analysis":
st.header('Sentiment Analysis')
# st.markdown("")
st.write(
"""
"""
)
text = st.text_area("Paste text here", value=example_text)
if st.button('🔥 Run!'):
with st.spinner("Loading..."):
o_lang = langdetector1.predict_language(text)
preds, html = sentiment_analyzer.run(text, o_lang)
st.success('All done!')
st.write("")
st.subheader("Sentiment Predictions")
st.bar_chart(data=preds, width=0, height=0, use_container_width=True)
st.write("")
st.subheader("Sentiment Justification")
raw_html = html._repr_html_()
st.components.v1.html(raw_html, height=500)
elif page == "Keyword Extraction":
st.header('Keyword Extraction')
# st.markdown("")
st.write(
"""
"""
)
text = st.text_area("Paste text here", value=example_text)
max_keywords = st.slider('# of Keywords Max Limit', min_value=1, max_value=10, value=5, step=1)
if st.button('🔥 Run!'):
with st.spinner("Loading..."):
annotation, keywords = keyword_extractor.generate(text, max_keywords)
st.success('All done!')
if annotation:
st.subheader("Keyword Annotation")
st.write("")
annotated_text(*annotation)
st.text("")
st.subheader("Extracted Keywords")
st.write("")
df = pd.DataFrame(keywords, columns=['Extracted Keywords'])
csv = df.to_csv(index=False).encode('utf-8')
st.download_button('Download Keywords to CSV', csv, file_name='news_intelligence_keywords.csv')
data_table = st.table(df)
elif page == "Part of Speech Tagging":
st.header('Part of Speech Tagging')
# st.markdown("")
st.write(
"""
"""
)
text = st.text_area("Paste text here", value=example_text)
if st.button('🔥 Run!'):
with st.spinner("Loading..."):
preds = pos_tagger.classify(text)
st.success('All done!')
st.write("")
st.subheader("Part of Speech tags")
annotated_text(*preds)
st.write("")
st.components.v1.iframe('https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html', height=1000)
elif page == "Emotion Detection":
st.header('Emotion Detection')
# st.markdown("")
st.write(
"""
"""
)
text = st.text_area("Paste text here", value=example_text)
if st.button('🔥 Run!'):
with st.spinner("Loading..."):
preds, html = emotion_detector.run(text)
st.success('All done!')
st.write("")
st.subheader("Emotion Predictions")
st.bar_chart(data=preds, width=0, height=0, use_container_width=True)
raw_html = html._repr_html_()
st.write("")
st.subheader("Emotion Justification")
st.components.v1.html(raw_html, height=500)
elif page == "Named Entity Recognition":
st.header('Named Entity Recognition')
# st.markdown("")
st.write(
"""
"""
)
text = st.text_area("Paste text here", value=example_text)
if st.button('🔥 Run!'):
with st.spinner("Loading..."):
preds, ner_annotation = ner.classify(text)
st.success('All done!')
st.write("")
st.subheader("NER Predictions")
annotated_text(*ner_annotation)
st.write("")
st.subheader("NER Prediction Metadata")
st.write(preds)
elif page == "Object Detector":
st.header('Object Detector')
st.write(
"""
"""
)
img_file_buffer = st.file_uploader("Load an image", type=["png", "jpg", "jpeg"])
if img_file_buffer is not None:
image = np.array(Image.open(img_file_buffer))
if st.button('🔥 Run!'):
with st.spinner("Loading..."):
img, primero = objectdetector1.run_detector(image)
st.success('The first image detected is: ' + primero)
st.image(img, caption="Imagen", use_column_width=True)
elif page == "OCR Detector":
st.header('OCR Detector')
st.write(
"""
"""
)
file = st.file_uploader("Load an image", type=["png", "jpg", "jpeg"])
#read the csv file and display the dataframe
if file is not None:
image = Image.open(file) # read image with PIL library
if st.button('🔥 Run!'):
with st.spinner("Loading..."):
result = ocrdetector1.reader.readtext(np.array(image)) # turn image to numpy array
# collect the results in dictionary:
textdic_easyocr = {}
for idx in range(len(result)):
pred_coor = result[idx][0]
pred_text = result[idx][1]
pred_confidence = result[idx][2]
textdic_easyocr[pred_text] = {}
textdic_easyocr[pred_text]['pred_confidence'] = pred_confidence
# get boxes on the image
rectangle(image, result)
# create a dataframe which shows the predicted text and prediction confidence
df = pd.DataFrame.from_dict(textdic_easyocr).T
st.table(df)
elif page == "Speech & Text Emotion":
st.header('Speech & Text Emotion')
st.write(
"""
"""
)
uploaded_file = st.file_uploader("Choose an audio file", type=["mp3", "wav", "ogg"])
if uploaded_file is not None:
st.audio(uploaded_file, format='audio/' + uploaded_file.type.split('/')[1])
st.write("Audio file uploaded and playing.")
else:
st.write("Please upload an audio file.")
if st.button("Analysis"):
with st.spinner("Loading..."):
st.header('Results of the Audio & Text analysis:')
samples, sample_rate = librosa.load(uploaded_file, sr=16000)
p_voice2text = infere_voice2text (samples)
p_speechemotion = infere_speech_emotion(samples)
p_textemotion = infere_text_emotion(p_voice2text)
st.subheader("Text from the Audio:")
st.write(p_voice2text)
st.write("---")
st.subheader("Speech emotion:")
st.write(p_speechemotion)
st.write("---")
st.subheader("Text emotion:")
st.write(p_textemotion)
st.write("---")
try:
main()
except Exception as e:
st.sidebar.error(f"An error occurred: {e}")