app.py

import gradio as gr
import transformers
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer

# Download the VADER lexicon
nltk.download('vader_lexicon')

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="rohanphadke/roberta-finetuned-adjusted-triplebottomline", top_k=None)

def greet(news):
    sia = SentimentIntensityAnalyzer()
    def convert_to_dict(predictions):
        result_dict = {}
        # Assuming predictions is a list of lists of dictionaries
        for prediction_list in predictions:
            for item in prediction_list:
                label = item['label']
                score = item['score']
                result_dict[label] = score
        return result_dict

    tbl_dict = convert_to_dict(pipe(news))
    sia_score = sia.polarity_scores(news)
    return str(tbl_dict['people']), str(tbl_dict['planet']), str(tbl_dict['profit']), str(sia_score['compound'])

demo = gr.Interface(fn=greet, inputs="text", outputs=["text", "text", "text", "text"])
demo.launch()