Create app.py

app.py

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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%}")