Spaces:
Runtime error
Runtime error
import streamlit as st | |
from PIL import Image | |
# from pdf2image import convert_from_path | |
import pandas as pd | |
import yake | |
import fitz | |
import nltk | |
from gtts import gTTS | |
nltk.download('punkt') | |
nltk.download('wordnet') | |
nltk.download('omw-1.4') | |
from sklearn.feature_extraction.text import TfidfVectorizer | |
from sklearn.metrics.pairwise import cosine_similarity | |
import string | |
import os | |
import re | |
os.system('sudo apt-get install tesseract-ocr') | |
os.system('pip install -q pytesseract') | |
import pytesseract | |
st.title("Extract info from Files") | |
st.sidebar.title('Hyper Params') | |
menu = ["Image","Dataset","DocumentFiles","About"] | |
choice = st.sidebar.selectbox("Select the type of data", menu) | |
no_of_keys = st.sidebar.slider('Select the no of keywords', 1, 20, 2, 2) | |
output = 'response' | |
output = st.selectbox('Select the type of output', ('keys', 'response')) | |
# pre processing the images | |
filters = ['Gaussian', 'Low pass', 'High Pass', 'System defined'] | |
filter = st.sidebar.selectbox("Select the type of filter to preprocess the image", filters) | |
tes = 'C:\\Program Files\\Tesseract-OCR\\tesseract.exe' | |
pytesseract.pytesseract.tesseract_cmd = tes | |
extractor = yake.KeywordExtractor() | |
language = 'en' | |
max_ngram_size = st.sidebar.slider('Select the parameter for ngram', 1, 20, 3, 2) | |
deduplication_threshold = st.sidebar.slider('Select the parameter for DD threshold', 1, 10, 9, 1) | |
deduplication_threshold = deduplication_threshold/10 | |
numOfKeywords = 100 | |
custom_kw_extractor = yake.KeywordExtractor(lan=language, n=max_ngram_size, dedupLim=deduplication_threshold, top=numOfKeywords, features=None) | |
lemmer = nltk.stem.WordNetLemmatizer() | |
def LemTokens(tokens): | |
return [lemmer.lemmatize(token) for token in tokens] | |
remove_punct_dict= dict((ord(punct), None) for punct in string.punctuation) | |
def LemNormalize(text): | |
return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict))) | |
def rees(glo_text, keys): | |
for key in keys[:no_of_keys]: | |
# st.write(type(glo_text)) | |
sent_tokens = nltk.sent_tokenize(glo_text) | |
word_tokens = nltk.word_tokenize(glo_text) | |
sent_tokens.append(key) | |
word_tokens = word_tokens + nltk.word_tokenize(key) | |
TfidfVec = TfidfVectorizer(tokenizer = LemNormalize, stop_words='english') | |
tfidf = TfidfVec.fit_transform(sent_tokens) | |
vals = cosine_similarity(tfidf[-1], tfidf) | |
idx = vals.argsort()[0][-2] | |
response = sent_tokens[idx] | |
if(output == 'response'): | |
st.write(' - ' + key + ':' + response) | |
else: | |
st.write(' - ' + key) | |
response = re.sub("[^a-zA-Z0-9]","",response) | |
myobj = gTTS(text=response, lang=language, slow=False) | |
myobj.save("audio.mp3") | |
st.audio("audio.mp3", format='audio/ogg') | |
os.remove("audio.mp3") | |
def load_image(image_file): | |
img = Image.open(image_file) | |
st.image(img, width=250) | |
text = pytesseract.image_to_string(img) | |
img.close() | |
return text | |
# text = pytesseract.image_to_string(img) | |
def load_pdf(data_file): | |
doc = fitz.open(stream=data_file.read(), filetype="pdf") | |
text = "" | |
glo_text = '' | |
for page in doc: | |
text = text + page.get_text() | |
glo_text += text | |
keywords = custom_kw_extractor.extract_keywords(text) | |
for kw in keywords[::-1]: | |
if(kw[1] > 0.1): | |
keys.append(kw[0]) | |
# st.write(keys) | |
doc.close() | |
return glo_text, keys | |
keys = [] | |
def tes_image(image_file): | |
if image_file != None: | |
# add filters if time permits | |
glo_text = '' | |
# text = pytesseract.image_to_string(load_image(image_file)) # can add a specific language to detect the text on the screen | |
# st.image(load_image(image_file),width=250) | |
# st.write(text) | |
text = load_image(image_file) | |
glo_text += text | |
keywords = custom_kw_extractor.extract_keywords(text) | |
for kw in keywords[::-1]: | |
if(kw[1] > 0.1): | |
keys.append(kw[0]) | |
# st.write(keys) | |
return glo_text, keys | |
def tes_doc(data_file): | |
if data_file != None: | |
tup = load_pdf(data_file) | |
return tup | |
def convert_df_to_text(df): | |
pass # implement key to text here using key2text package | |
if choice == "Image": | |
st.subheader("Image") | |
image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"]) | |
if image_file != None: | |
file_details = {"filename":image_file.name, "filetype":image_file.type, "filesize":image_file.size} | |
st.write(file_details) | |
glo_text, keys = tes_image(image_file) | |
rees(glo_text, keys) | |
elif choice == "Dataset": | |
st.subheader("Dataset") | |
data_file = st.file_uploader("Upload CSV",type=["csv"]) | |
if data_file != None: | |
file_details = {"filename":data_file, "filetype":data_file.type, "filesize":data_file.size} | |
st.write(file_details) | |
df = pd.read_csv(data_file) | |
st.write(df) | |
convert_df_to_text(df) | |
elif choice == "DocumentFiles": | |
st.subheader("DocumentFiles") | |
docx_file = st.file_uploader("Upload Document", type=["pdf","docx","txt"]) | |
if st.button("Process"): | |
if docx_file is not None: | |
file_details = {"filename":docx_file.name, "filetype":docx_file.type, "filesize":docx_file.size} | |
st.write(file_details) | |
glo_text, keys = tes_doc(docx_file) | |
rees(glo_text, keys) |