| # Finacial Sentiment Analysis Using Huggingface App | |
| # Team Name :- Free Thinkers | |
| # Authors:- Lalit Chaudhary and Khushter Kaifi | |
| # 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%}") | |