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import streamlit as st
import wna_googlenews as wna
import pandas as pd
from transformers import pipeline
st.set_page_config(layout="wide",page_title="News Inferno",page_icon="🌍")
st.title("Google News LLM")
# Store the initial value of widgets in session state
if "placeholder" not in st.session_state:
st.session_state.placeholder = "Enter your search query here"
# Display the text input widget with dynamic placeholder
query = st.text_input("Search for news",
placeholder=st.session_state.placeholder)
models = [
"j-hartmann/emotion-english-distilroberta-base",
"SamLowe/roberta-base-go_emotions",
"yymYYM/llama-3-8b-NewsLLM-phase2final",
"distilbert/distilbert-base-uncased-finetuned-sst-2-english"
]
settings = {
"langregion": "en/US",
"period": "1d",
"model": models[0],
"number_of_pages": 5
}
with st.sidebar:
st.title("Settings")
# add language and country parameters
# st.header("Language and Country")
# settings["langregion"] = st.selectbox("Select Language", ["en/US", "fr/FR"])
# input field for number of pages
st.header("Number of Pages")
settings["number_of_pages"] = st.number_input("Enter Number of Pages", min_value=1, max_value=10)
settings["region"] = settings["langregion"].split("/")[0]
settings["lang"] = settings["langregion"].split("/")[1]
# add period parameter
st.header("Period")
settings["period"] = st.selectbox("Select Period", ["1d", "7d", "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)
# display a loading progress
with st.spinner("Loading last news ..."):
allnews = wna.get_news(settings, query)
st.dataframe(allnews)
with st.spinner("Processing received news ..."):
df = pd.DataFrame(columns=["sentence", "date","best","second"])
# loop on each sentence and call classifier
for curnews in allnews:
#st.write(curnews)
cur_sentence = curnews["title"]
cur_date = curnews["date"]
model_outputs = classifier(cur_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, cur_date, str1, str2]
# write info on the output
st.write("Number of sentences:", len(df))
st.dataframe(df)
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