classifaction

app.py

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+import streamlit as st
+from transformers import pipeline
+
+print("Loading the model...")
+
+# Title and Description
+st.title("Sentiment Analysis Web App")
+st.write("""
+### Powered by Hugging Face and Streamlit
+This app uses a pre-trained NLP model from Hugging Face to analyze the sentiment of the text you enter.  
+Try entering a sentence to see if it's positive, negative, or neutral!
+""")
+
+# Initialize Hugging Face Sentiment Analysis Pipeline
+@st.cache_resource
+def load_model():
+    print("before load model")
+    return pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+sentiment_analyzer = load_model()
+
+# Input Text from User
+user_input = st.text_area("Enter some text to analyze:", "Streamlit and Hugging Face make NLP fun!")
+
+# Analyze Sentiment
+if st.button("Analyze Sentiment"):
+    print("button click")
+    if user_input.strip():
+        result = sentiment_analyzer(user_input)[0]
+        sentiment = result['label']
+        score = result['score']
+
+        # Display the Result
+        st.subheader("Sentiment Analysis Result")
+        st.write(f"**Sentiment:** {sentiment}")
+        st.write(f"**Confidence Score:** {score:.2f}")
+    else:
+        st.warning("Please enter some text to analyze!")
+
+# Sidebar with About Information
+st.sidebar.title("About")
+st.sidebar.info("""
+This app demonstrates the use of Hugging Face's NLP models with Streamlit.  
+It uses the distilbert-base-uncased-finetuned-sst-2-english model for sentiment analysis.
+""")
+
+print('after')

requirements.txt

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+streamlit==1.41.1
+transformers
+torch