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app.py

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+import streamlit as st
+import transformers
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("ikoghoemmanuell/finetuned_fake_news_bert")
+tokenizer = AutoTokenizer.from_pretrained("ikoghoemmanuell/finetuned_fake_news_bert")
+
+# Define the function for detecting fake news
+@st.cache_resource
+def detect_fake_news(text):
+    # Load the pipeline.
+    pipeline = transformers.pipeline("text-classification")
+
+    # Predict the sentiment.
+    prediction = pipeline(text)
+    sentiment = prediction[0]["label"]
+    score = prediction[0]["score"]
+
+    return sentiment, score
+
+# Setting the page configurations
+st.set_page_config(
+    page_title="Fake News Detection App",
+    page_icon=":smile:",
+    layout="wide",
+    initial_sidebar_state="auto",
+)
+
+# Add description and title
+st.write("""
+# Fake News Detection
+Enter some text and we'll tell you if it's likely to be fake news or not!
+""")
+
+# Add image
+image = st.image("https://docs.gato.txst.edu/78660/w/2000/a_1dzGZrL3bG/fake-fact.jpg", width=400)
+
+# Get user input
+text = st.text_input("Enter some text here:")
+
+# Define the CSS style for the app
+st.markdown(
+"""
+<style>
+body {
+    background-color: #f5f5f5;
+}
+h1 {
+    color: #4e79a7;
+}
+</style>
+""",
+unsafe_allow_html=True
+)
+
+# Show fake news detection output
+if text:
+    label, score = detect_fake_news(text)
+    if label == "Fake":
+        st.error(f"The text is likely to be fake news with a confidence score of {score*100:.2f}%!")
+    else:
+        st.success(f"The text is likely to be genuine with a confidence score of {score*100:.2f}%!")

requirements.txt

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+streamlit
+# pandas==1.5.3
+# numpy==1.24.2
+# pillow 
+# requests 
+# bokeh 
+# scikit-learn==1.2.2
+
+# for huggingface app
+torch
+transformers