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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) | |