app.py

import streamlit as st
import wna_googlenews as wna
import pandas as pd
from transformers import pipeline

st.set_page_config(layout="wide")

st.title("WNA Google News App")
st.subheader("Search for News and classify the headlines with sentiment analysis")

query = st.text_input("Enter Query")

models = [
          "SamLowe/roberta-base-go_emotions", 
          # "distilbert/distilbert-base-uncased-finetuned-sst-2-english"
        ]

settings = {
  "lang": "fr",
  "region": "FR",
  "period": "1d",
  "model": models[0],
}


with st.sidebar:
  st.title("Settings")
  # add language and country parameters
  st.header("Language and Country")
  settings["lang"] = st.selectbox("Select Language", ["en", "fr"])
  settings["region"] = st.selectbox("Select Country", ["US", "FR"])

  # add period parameter
  st.header("Period")
  settings["period"] = st.selectbox("Select Period", ["1d", "7", "30d"])
  # Add models parameters
  st.header("Models")
  settings["model"] = st.selectbox("Select Model", models)


if st.button("Search"):
  classifier = pipeline(task="text-classification", model=settings["model"], top_k=None)
  df = wna.get_news(settings, query)
  # st.dataframe(df)
  # get each title colums
  sentences = df["title"]
  # convert into array
  sentences = sentences.tolist()
  # st.write(sentences)
  # create new dataframe
  df = pd.DataFrame(columns=["sentence", "best","second"])
  # loop on each sentence and call classifier
  for sentence in sentences:
    cur_sentence = sentence
    model_outputs = classifier(sentence)
    cur_result = model_outputs[0]
    #st.write(cur_result)
    # get label 1
    label = cur_result[0]['label']
    score = cur_result[0]['score']
    percentage = round(score * 100, 2)
    str1 = label + " " + str(percentage)
    # get label 2
    label = cur_result[1]['label']
    score = cur_result[1]['score']
    percentage = round(score * 100, 2)
    str2 = label + " " + str(percentage)
    # insert cur_sentence and cur_result into dataframe
    df.loc[len(df.index)] = [cur_sentence, str1, str2]

  # write info on the output
  st.write("Number of sentences:", len(df))
  st.write("Language:", settings["lang"], "Country:", settings["region"])

  st.dataframe(df)