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| import requests | |
| import gradio as gr | |
| import pandas as pd | |
| import os | |
| from newsapi import NewsApiClient | |
| from datetime import date, timedelta | |
| from transformers import pipeline | |
| HF_TOKEN = os.environ["newsapi"] | |
| # Initialization | |
| newsapi = NewsApiClient(api_key=HF_TOKEN) | |
| classifier = pipeline(model="cardiffnlp/twitter-roberta-base-sentiment") | |
| today = str(date.today()) | |
| print("******** Outside Inference function ********") | |
| print(f"HF_TOKEN is - {HF_TOKEN}") | |
| #top-headlines | |
| all_top_headlines = newsapi.get_top_headlines(country='in') | |
| sentiment_tophead = ['Negative' if classifier(entry['content'])[0]['label'] == 'LABEL_0' else 'Neutral' if classifier(entry['content'])[0]['label'] == 'LABEL_1' else 'Positive' for entry in all_top_headlines['articles']] | |
| print(f"sentiment_tophead length is {len(sentiment_tophead)}") | |
| print(f"all_top_headlines length is {len(all_top_headlines['articles'])}") | |
| print("************** sentiment start ****************") | |
| print(sentiment_tophead) | |
| print("************** sentiment end ****************") | |
| #times of india | |
| all_articles_toi = newsapi.get_everything(sources='the-times-of-india', | |
| domains= 'http://timesofindia.indiatimes.com', #'timesofindia.indiatimes.com', | |
| from_param=today, | |
| to=today, | |
| language='en', | |
| sort_by='relevancy',) | |
| sentiment_toi = ['Negative' if classifier(entry['content'])[0]['label'] == 'LABEL_0' else 'Neutral' if classifier(entry['content'])[0]['label'] == 'LABEL_1' else 'Positive' for entry in all_articles_toi['articles']] | |
| print(f"sentiment_toi length is {len(sentiment_toi)}") | |
| print(f"all_articles_toi length is {len(all_articles_toi['articles'])}") | |
| #Driver positive | |
| def inference_pos(newssource): #, date): | |
| if newssource == "Times Of India": | |
| sentiment = sentiment_toi | |
| all_articles = all_articles_toi | |
| elif newssource == "Top Headlines": | |
| sentiment = sentiment_tophead | |
| all_articles = all_top_headlines | |
| #"<a href=" + "url" + "></a>"link text</a> | |
| description = [entry['description'] for entry in all_articles['articles']] | |
| content = [entry['content'] for entry in all_articles['articles']] | |
| url = ["<a href=" + entry['url'] + ' target="_blank">Click here for the original news article</a>' for entry in all_articles['articles']] | |
| urlToImage = ["<img src= " + entry['urlToImage']+">" for entry in all_articles['articles']] | |
| print("********************* Positive News **************************") | |
| print(f"Newssource is - {newssource}") | |
| print(f"description length is - {len(description)}") | |
| print(f"content length is - {len(content)}") | |
| print(f"url length is - {len(url)}") | |
| print(f"urlToImage length is - {len(urlToImage)}") | |
| print(f"sentiment length is - {len(sentiment)}") | |
| dictnews = { 'description' : description, 'content' : content, 'url' : url, 'urlToImage' : urlToImage, 'sentiment' : sentiment} | |
| df = pd.DataFrame.from_dict(dictnews) | |
| df = df.loc[df['sentiment'] == 'Positive'] | |
| print(f"dataframe shape is :,{df.shape}") | |
| return df | |
| #Driver - negative | |
| def inference_neg(newssource): #, date): | |
| if newssource == "Times Of India": | |
| sentiment = sentiment_toi | |
| all_articles = all_articles_toi | |
| elif newssource == "Top Headlines": | |
| sentiment = sentiment_tophead | |
| all_articles = all_top_headlines | |
| description = [entry['description'] for entry in all_articles['articles']] | |
| content = [entry['content'] for entry in all_articles['articles']] | |
| url = ["<a href=" + entry['url'] + ' target="_blank">Click here for the original news article</a>' for entry in all_articles['articles']] | |
| urlToImage = ["<img src= " + entry['urlToImage']+">" for entry in all_articles['articles']] | |
| print("********************* Negative News ***********************") | |
| print(f"Newssource is - {newssource}") | |
| print(f"description length is - {len(description)}") | |
| print(f"content length is - {len(content)}") | |
| print(f"url length is - {len(url)}") | |
| print(f"urlToImage length is - {len(urlToImage)}") | |
| print(f"sentiment length is - {len(sentiment)}") | |
| dictnews = { 'description' : description, 'content' : content, 'url' : url, 'urlToImage' : urlToImage, 'sentiment' : sentiment} | |
| df = pd.DataFrame.from_dict(dictnews) | |
| df = df.loc[df['sentiment'] == 'Negative'] | |
| print(f"dataframe shape is :,{df.shape}") | |
| return df | |
| #Driver - neutral | |
| def inference_neut(newssource): #, date): | |
| if newssource == "Times Of India": | |
| sentiment = sentiment_toi | |
| all_articles = all_articles_toi | |
| elif newssource == "Top Headlines": | |
| sentiment = sentiment_tophead | |
| all_articles = all_top_headlines | |
| description = [entry['description'] for entry in all_articles['articles']] | |
| content = [entry['content'] for entry in all_articles['articles']] | |
| url = ["<a href=" + entry['url'] + ' target="_blank">Click here for the original news article</a>' for entry in all_articles['articles']] | |
| urlToImage = ["<img src= " + entry['urlToImage']+">" for entry in all_articles['articles']] | |
| print("********************* Neutral News ***********************") | |
| print(f"Newssource is - {newssource}") | |
| print(f"description length is - {len(description)}") | |
| print(f"content length is - {len(content)}") | |
| print(f"url length is - {len(url)}") | |
| print(f"urlToImage length is - {len(urlToImage)}") | |
| print(f"sentiment length is - {len(sentiment)}") | |
| dictnews = { 'description' : description, 'content' : content, 'url' : url, 'urlToImage' : urlToImage, 'sentiment' : sentiment} | |
| df = pd.DataFrame.from_dict(dictnews) | |
| df = df.loc[df['sentiment'] == 'Neutral'] | |
| print(f"dataframe shape is :,{df.shape}") | |
| return df | |
| #Gradio Blocks | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| in_newssource = gr.Dropdown(["Times Of India", "Top Headlines"], label='Choose a News Outlet') | |
| #in_date = gr.Textbox(visible = False, value = today) | |
| with gr.Row(): | |
| b1 = gr.Button("Get Positive News") | |
| b2 = gr.Button("Get Negative News") | |
| b3 = gr.Button("Get Neutral News") | |
| with gr.Row(): | |
| #sample | |
| #out_news = gr.HTML(label="First News Link", show_label=True) | |
| out_dataframe = gr.Dataframe(wrap=True, datatype = ["str", "str", "markdown", "markdown", "str"]) | |
| b1.click(fn=inference_pos, inputs=in_newssource, outputs=out_dataframe) #, out_news]) | |
| b2.click(fn=inference_neg, inputs=in_newssource, outputs=out_dataframe) #, out_news]) | |
| b3.click(fn=inference_neut, inputs=in_newssource, outputs=out_dataframe) #, out_news]) | |
| demo.launch(debug=True, show_error=True) |