Initial commit of Sentiment Analysis App

README.md

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+---
+title: Sentiment Analysis App
+emoji: 🤖
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.28.0
+app_file: app.py
+pinned: false
+---
+
+# Sentiment Analysis App
+
+This is a simple Sentiment Analysis application built with [Streamlit](https://streamlit.io) and [Hugging Face Transformers](https://huggingface.co/docs/transformers).
+
+It uses the default `distilbert-base-uncased-finetuned-sst-2-english` model to classify text as **POSITIVE** or **NEGATIVE**.

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Set page title and header
+st.set_page_config(page_title="Sentiment Analysis App", page_icon="🤖")
+st.title("🤖 Sentiment Analysis with Hugging Face")
+
+st.markdown("""
+This app uses a pre-trained machine learning model from Hugging Face Transformers to analyze the sentiment of your text.
+""")
+
+# Load the pipeline (cached to avoid reloading on every interaction)
+@st.cache_resource
+def load_sentiment_pipeline():
+    return pipeline("sentiment-analysis")
+
+classifier = load_sentiment_pipeline()
+
+# User input
+text_input = st.text_area("Enter some text here:", height=150, placeholder="I love building cool AI apps!")
+
+if st.button("Analyze Sentiment"):
+    if text_input.strip():
+        with st.spinner("Analyzing..."):
+            result = classifier(text_input)[0]
+            label = result['label']
+            score = result['score']
+            
+            if label == 'POSITIVE':
+                st.success(f"**Sentiment:** {label} 😊")
+            else:
+                st.error(f"**Sentiment:** {label} 😔")
+            
+            st.metric("Confidence Score", f"{score:.4f}")
+    else:
+        st.warning("Please enter some text to analyze.")

requirements.txt

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+streamlit
+transformers
+torch