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# Finacial Sentiment Analysis Using Huggingface App
# Team Name :- Free Thinkers
# Authors:- Lalit Chaudhary and Khushter Kaifi
# Update On- 2 Jan 2024
# streamlit is a Python library used for creating web applications with minimal effort.
# pipeline is a class from the Hugging Face Transformers library that allows you to easily use pre-trained models for various natural language processing (NLP) tasks
import streamlit as st
from transformers import pipeline
# This line creates a sentiment analysis pipeline using the Hugging Face Transformers library.
# The pipeline is pre-configured to perform sentiment analysis on input text.
# # Load sentiment analysis pipeline
sentiment_pipeline = pipeline("sentiment-analysis")
# Sets the title of the Streamlit web application
st.title("Financial Sentiment Analysis Using HuggingFace 😎 \n Team Name:- Free Thinkers")
# Displays a text input box where the user can enter a sentence for sentiment analysis.
st.write("Enter a Sentence to Analyze the Sentiment:")
user_input = st.text_input("")
st.write("Press the Enter key")
# Performing Sentiment Analysis:
# Checks if the user has entered some text. If yes,
# it uses the sentiment_pipeline to analyze the sentiment of the input text and stores the result in the result variable.
if user_input:
result = sentiment_pipeline(user_input)
sentiment = result[0]["label"]
confidence = result[0]["score"]
# Displaying Results:
#If there is user input, it displays the sentiment and confidence score.
# The sentiment is extracted from the "label" field in the result, and the confidence score is extracted from the "score" field.
st.write(f"Sentiment: {sentiment}")
st.write(f"Confidence: {confidence:.2%}")
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