diff --git a/file_download_upload.py b/file_download_upload.py
deleted file mode 100644
index f7e74f865e71c2af477ef23a3b1e536908d200ea..0000000000000000000000000000000000000000
--- a/file_download_upload.py
+++ /dev/null
@@ -1,308 +0,0 @@
-import streamlit as st
-import os
-
-
-# NLP Pkgs
-from textblob import TextBlob
-import spacy
-from gensim.summarization import summarize
-
-# Sumy Pkg
-from sumy.parsers.plaintext import PlaintextParser
-from sumy.nlp.tokenizers import Tokenizer
-from sumy.summarizers.lex_rank import LexRankSummarizer
-
-##
-# code to acknowledge and integrate from awesome streamlit
-##
-# Sumy Summarization
-def sumy_summarizer(docx):
- parser = PlaintextParser.from_string(docx, Tokenizer("english"))
- lex_summarizer = LexRankSummarizer()
- summary = lex_summarizer(parser.document, 3)
- summary_list = [str(sentence) for sentence in summary]
- result = " ".join(summary_list)
- return result
-
-
-# Function For Analysing Tokens and Lemma
-@st.cache
-def text_analyzer(my_text):
- nlp = spacy.load("en")
- docx = nlp(my_text)
- # tokens = [ token.text for token in docx]
- allData = [
- ('"Token":{},\n"Lemma":{}'.format(token.text, token.lemma_)) for token in docx
- ]
- return allData
-
-
-# Function For Extracting Entities
-@st.cache
-def entity_analyzer(my_text):
- nlp = spacy.load("en")
- docx = nlp(my_text)
- tokens = [token.text for token in docx]
- entities = [(entity.text, entity.label_) for entity in docx.ents]
- allData = ['"Token":{},\n"Entities":{}'.format(tokens, entities)]
- return allData
-
-
-def main():
- """ NLP Based App with Streamlit """
-
- # Title
- st.title("NLPiffy with Streamlit")
- st.subheader("Natural Language Processing On the Go..")
-
- # Tokenization
- if st.checkbox("Show Tokens and Lemma"):
- st.subheader("Tokenize Your Text")
-
- message = st.text_area("Enter Text", "Type Here ..")
- if st.button("Analyze"):
- nlp_result = text_analyzer(message)
- st.json(nlp_result)
-
- # Entity Extraction
- if st.checkbox("Show Named Entities"):
- st.subheader("Analyze Your Text")
-
- message = st.text_area("Enter Text", "Type Here ..")
- if st.button("Extract"):
- entity_result = entity_analyzer(message)
- st.json(entity_result)
-
- # Sentiment Analysis
- if st.checkbox("Show Sentiment Analysis"):
- st.subheader("Analyse Your Text")
-
- message = st.text_area("Enter Text", "Type Here ..")
- if st.button("Analyze"):
- blob = TextBlob(message)
- result_sentiment = blob.sentiment
- st.success(result_sentiment)
-
- # Summarization
- if st.checkbox("Show Text Summarization"):
- st.subheader("Summarize Your Text")
-
- message = st.text_area("Enter Text", "Type Here ..")
- summary_options = st.selectbox("Choose Summarizer", ["sumy", "gensim"])
- if st.button("Summarize"):
- if summary_options == "sumy":
- st.text("Using Sumy Summarizer ..")
- summary_result = sumy_summarizer(message)
- elif summary_options == "gensim":
- st.text("Using Gensim Summarizer ..")
- summary_result = summarize(rawtext)
- else:
- st.warning("Using Default Summarizer")
- st.text("Using Gensim Summarizer ..")
- summary_result = summarize(rawtext)
-
- st.success(summary_result)
-
- st.sidebar.subheader("About App")
- st.sidebar.text("NLPiffy App with Streamlit")
- st.sidebar.info("Cudos to the Streamlit Team")
-
- st.sidebar.subheader("By")
- st.sidebar.text("Jesse E.Agbe(JCharis)")
- st.sidebar.text("Jesus saves@JCharisTech")
-
-
-if __name__ == "__main__":
- main()
-
-
-import base64
-import os
-import json
-import pickle
-import uuid
-import re
-
-import streamlit as st
-import pandas as pd
-
-
-def download_button(
- object_to_download, download_filename, button_text, pickle_it=False
-):
- """
- Generates a link to download the given object_to_download.
- Params:
- ------
- object_to_download: The object to be downloaded.
- download_filename (str): filename and extension of file. e.g. mydata.csv,
- some_txt_output.txt download_link_text (str): Text to display for download
- link.
- button_text (str): Text to display on download button (e.g. 'click here to download file')
- pickle_it (bool): If True, pickle file.
- Returns:
- -------
- (str): the anchor tag to download object_to_download
- Examples:
- --------
- download_link(your_df, 'YOUR_DF.csv', 'Click to download data!')
- download_link(your_str, 'YOUR_STRING.txt', 'Click to download text!')
- """
- if pickle_it:
- try:
- object_to_download = pickle.dumps(object_to_download)
- except pickle.PicklingError as e:
- st.write(e)
- return None
-
- else:
- if isinstance(object_to_download, bytes):
- pass
-
- elif isinstance(object_to_download, pd.DataFrame):
- object_to_download = object_to_download.to_csv(index=False)
-
- # Try JSON encode for everything else
- else:
- object_to_download = json.dumps(object_to_download)
-
- try:
- # some strings <-> bytes conversions necessary here
- b64 = base64.b64encode(object_to_download.encode()).decode()
-
- except AttributeError as e:
- b64 = base64.b64encode(object_to_download).decode()
-
- button_uuid = str(uuid.uuid4()).replace("-", "")
- button_id = re.sub("\d+", "", button_uuid)
-
- custom_css = f"""
- <style>
- #{button_id} {{
- background-color: rgb(255, 255, 255);
- color: rgb(38, 39, 48);
- padding: 0.25em 0.38em;
- position: relative;
- text-decoration: none;
- border-radius: 4px;
- border-width: 1px;
- border-style: solid;
- border-color: rgb(230, 234, 241);
- border-image: initial;
- }}
- #{button_id}:hover {{
- border-color: rgb(246, 51, 102);
- color: rgb(246, 51, 102);
- }}
- #{button_id}:active {{
- box-shadow: none;
- background-color: rgb(246, 51, 102);
- color: white;
- }}
- </style> """
-
- dl_link = (
- custom_css
- + f'<a download="{download_filename}" id="{button_id}" href="data:file/txt;base64,{b64}">{button_text}</a><br></br>'
- )
-
- return dl_link
-
-
-def file_selector(folder_path="."):
- filenames = os.listdir(folder_path)
- selected_filename = st.selectbox("Select a file", filenames)
- return os.path.join(folder_path, selected_filename)
-
-
-def download_pickle():
-
- # ---------------------
- # Download from memory
- # ---------------------
- if st.checkbox("Download object from memory"):
- st.write(
- "~> Use if you want to save some data from memory (e.g. pd.DataFrame, dict, list, str, int)"
- )
-
- # Enter text for testing
- s = st.selectbox(
- "Select dtype",
- [
- "list", # TODO: Add more
- "str",
- "int",
- "float",
- "dict",
- "bool",
- "pd.DataFrame",
- ],
- )
-
- filename = st.text_input(
- "Enter output filename and ext (e.g. my-dataframe.csv, my-file.json, my-list.txt)",
- "my-file.json",
- )
-
- # Pickle Rick
- pickle_it = st.checkbox("Save as pickle file")
-
- sample_df = pd.DataFrame({"x": list(range(10)), "y": list(range(10))})
- sample_dtypes = {
- "list": [1, "a", [2, "c"], {"b": 2}],
- "str": "Hello Streamlit!",
- "int": 17,
- "float": 17.0,
- "dict": {1: "a", "x": [2, "c"], 2: {"b": 2}},
- "bool": True,
- "pd.DataFrame": sample_df,
- }
-
- # Display sample data
- st.write(f"#### Sample `{s}` to be saved to `{filename}`")
- st.code(sample_dtypes[s], language="python")
-
- # Download sample
- download_button_str = download_button(
- sample_dtypes[s],
- filename,
- f"Click here to download {filename}",
- pickle_it=pickle_it,
- )
- st.markdown(download_button_str, unsafe_allow_html=True)
-
- if st.checkbox("Show code example "):
- code_text = f"""
- s = {sample_dtypes[s]}
- download_button_str = download_button(s, '{filename}', 'Click here to download {filename}', pickle_it={pickle_it})
- st.markdown(download_button_str, unsafe_allow_html=True)"""
-
- st.code(code_text, language="python")
-
- # --------------------------
- # Select a file to download
- # --------------------------
- if st.checkbox("Select a file to download"):
- st.write("~> Use if you want to test uploading / downloading a certain file.")
-
- # Upload file for testing
- folder_path = st.text_input("Enter directory: deafult .", ".")
- filename = file_selector(folder_path=folder_path)
-
- # Load selected file
- with open(filename, "rb") as f:
- s = f.read()
-
- download_button_str = download_button(
- s, filename, f"Click here to download {filename}"
- )
- st.markdown(download_button_str, unsafe_allow_html=True)
-
- if st.checkbox("Show code example"):
- code_text = f"""
- with open('{filename}', 'rb') as f:
- s = f.read()
- download_button_str = download_button(s, '{filename}', 'Click here to download {filename}')
- st.markdown(download_button_str, unsafe_allow_html=True)"""
-
- st.code(code_text, language="python")