Create app.py

app.py

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+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 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
+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
+
+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()
+
+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()
+
+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("![Alt Text](https://media.giphy.com/media/XIqCQx02E1U9W/giphy.gif)")
+    st.write(
+        """
+        """
+    )
+
+    text = st.text_area("Paste text here", value=example_text)
+
+    if st.button('🔥 Run!'):
+        with st.spinner("Loading..."):
+            preds, html = sentiment_analyzer.run(text)
+            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("![Alt Text](https://media.giphy.com/media/xT9C25UNTwfZuk85WP/giphy-downsized-large.gif)")
+    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("![Alt Text](https://media.giphy.com/media/WoWm8YzFQJg5i/giphy.gif)")
+    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("![Alt Text](https://media.giphy.com/media/fU8X6ozSszyEw/giphy.gif)")
+    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("![Alt Text](https://media.giphy.com/media/lxO8wdWdu4tig/giphy.gif)")
+    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("---")
+    
+