init

.gitignore

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+venv

app.py

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+import os
+import warnings
+
+import streamlit as st
+from transformers import pipeline
+
+# Disable TensorFlow and CUDA logs
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Disable GPU usage
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"  # Suppress TensorFlow warnings
+
+# Suppress all warnings from Python
+warnings.filterwarnings("ignore")
+
+
+# Load sentiment-analysis pipeline from Hugging Face
+@st.cache_resource
+def load_model():
+    return pipeline("sentiment-analysis")
+
+
+# App title and description
+st.title("News Sentiment Classification 📰💡")
+st.write(
+    """
+    This app uses a pre-trained model from Hugging Face to classify the sentiment of news headlines or articles.
+    Enter your news content below, and the model will classify it as either 'POSITIVE' or 'NEGATIVE'.
+    """
+)
+
+# Input from the user
+news_input = st.text_area("Enter a news headline or article:", "")
+
+# Load the model (cached to avoid reloading every time)
+sentiment_classifier = load_model()
+
+# Classify the sentiment when the button is pressed
+if st.button("Classify Sentiment"):
+    if news_input:
+        result = sentiment_classifier(news_input)
+
+        # Display the sentiment and confidence score
+        sentiment = result[0]["label"]
+        confidence = result[0]["score"]
+
+        st.subheader(f"Sentiment: {sentiment}")
+        st.write(f"Confidence: {confidence:.2%}")
+    else:
+        st.error("Please enter a news headline or article for classification.")
+        st.error("Please enter a news headline or article for classification.")

requirements copy.txt

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+streamlit==1.38.0
+transformers==4.45.0
+tensorflow==2.17.0
+tf-keras==2.17.0

requirements.txt

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+streamlit==1.38.0
+transformers==4.45.0
+tensorflow==2.17.0
+tf-keras==2.17.0