added code

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+hfTest/Lib/site-packages/numpy.libs/libscipy_openblas64_-43e11ff0749b8cbe0a615c9cf6737e0e.dll filter=lfs diff=lfs merge=lfs -text
+hfTest/Lib/site-packages/pyarrow/arrow.dll filter=lfs diff=lfs merge=lfs -text
+hfTest/Lib/site-packages/pyarrow/arrow_flight.dll filter=lfs diff=lfs merge=lfs -text
+hfTest/Lib/site-packages/pydeck/nbextension/static/index.js.map filter=lfs diff=lfs merge=lfs -text

.gitignore

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+hfTest/
+.vscode

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Application title
+st.title("Text Sentiment Analysis App")
+st.write("Analyze whether a given text is positive or negative using a Hugging Face model.")
+
+# Text input
+user_input = st.text_area("Enter your text:", placeholder="For example: I love this product!")
+
+# Initialize the model
+@st.cache_resource
+def load_sentiment_model():
+    return pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+# Load the model
+model = load_sentiment_model()
+
+# Perform analysis if input is provided
+if st.button("Analyze"):
+    if user_input.strip():
+        with st.spinner("Analyzing sentiment..."):
+            result = model(user_input)
+        st.success("Analysis complete!")
+        
+        # Extracting the label and score
+        label = result[0]['label']
+        score = result[0]['score']
+
+        # Display the result
+        st.write(f"**Label:** {label}")
+        st.write(f"**Confidence Score:** {score:.2f}")
+
+    else:
+        st.error("Please enter some text!")