main

app.py

@@ -1,11 +1,29 @@
 import streamlit as st
+from transformers import pipeline
 
-def load_model():
-    print("loading heavy model")
-    return"model"
+print("Loading the model...")
+
+# Title and Description
 
-print("before slider")
-x = st.slider('Select a value')
+st.title("Sentiment Analysis Web App")
 
-st.write(x, 'squared is', x * x)
-print("after slider")
+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']

requirements.txt

@@ -0,0 +1,3 @@
+streamlit=1.14.1
+transformers
+torch