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Unverified Commit 669c0f17 authored by Russell Jarvis's avatar Russell Jarvis :speech_balloon: Committed by GitHub
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Delete file_download_upload.py

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      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")
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