Update app.py

app.py

@@ -8,38 +8,82 @@ from predict_function import detect_fake_news
 model_name = AutoModelForSequenceClassification.from_pretrained("ikoghoemmanuell/finetuned_fake_news_roberta")
 tokenizer_name = AutoTokenizer.from_pretrained("ikoghoemmanuell/finetuned_fake_news_roberta")
 
-# # Set Page Configurations
-# st.set_page_config(page_title="Fake News Detection App", page_icon="fas fa-exclamation-triangle", layout="wide", initial_sidebar_state="auto")
 
-# # Loading GIF
-# gif_url = "https://raw.githubusercontent.com/Gilbert-B/Forecasting-Sales/main/app/salesgif.gif"
+# Loading GIF
+gif_url = "https://raw.githubusercontent.com/Gilbert-B/Forecasting-Sales/main/app/salesgif.gif"
+"https://docs.gato.txst.edu/78660/w/2000/a_1dzGZrL3bG/fake-fact.jpg"
 
-# # Set up sidebar
-# st.sidebar.header('Navigation')
-# menu = ['Home', 'About']
-# choice = st.sidebar.selectbox("Select an option", menu)
+# Set up sidebar
+st.sidebar.header('Navigation')
+menu = ['Home', 'About']
+choice = st.sidebar.selectbox("Select an option", menu)
 
+# Define the function for detecting fake news
+@st.cache_resource
+def detect_fake_news(text):
+    # Load the pipeline.
+    pipeline = transformers.pipeline("text-classification", model=model_name, tokenizer=tokenizer_name)
 
-# Setting the page configurations
-st.set_page_config(
-    page_title="Fake News Detection App",
-    page_icon=":smile:",
-    layout="wide",
-    initial_sidebar_state="auto",
-)
+    # Predict the sentiment.
+    prediction = pipeline(text)
+    sentiment = prediction[0]["label"]
+    score = prediction[0]["score"]
 
-# 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!
-""")
+    return sentiment, score
 
-# 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:")
 
+
+        
+# Home section
+if choice == 'Home':
+    st.image(gif_url, 
+             use_column_width=True,
+            width=400)
+    st.markdown("<h1 style='text-align: center;'>Welcome</h1>", unsafe_allow_html=True)
+    st.markdown("<p style='text-align: center;'>This is a Fake News Detection App.</p>", unsafe_allow_html=True)
+    
+    # Set Page Title
+    st.title('TRUTH- A fake news detection app')
+    st.markdown('Enter some text and we'll tell you if it's likely to be fake news or not!')
+
+    
+    
+    if st.button('Predict'):
+        # Show fake news detection output
+        if text:
+            with st.spinner('Checking if news is Fake...'):
+                label, score = detect_fake_news(text)
+                print(label, score)
+                if label == "LABEL_1":
+                    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}%!")
+        else:
+            with st.spinner('Checking if news is Fake...'):
+                sales_data = pd.DataFrame({})
+                try:
+                    st.success(f"")
+                except ValueError as e:
+                    st.error(str(e))
+
+
+
+
+
+
+        
+
+
+
+
+
+                    
+# Setting the page configurations
+st.set_page_config(page_title="Fake News Detection App", page_icon="fas fa-exclamation-triangle", layout="wide", initial_sidebar_state="auto")
+
 # Define the CSS style for the app
 st.markdown(
 """
@@ -55,11 +99,22 @@ h1 {
 unsafe_allow_html=True
 )
 
-# Show fake news detection output
-if text:
-    label, score = detect_fake_news(text)
-    print(label, score)
-    if label == "LABEL_1":
-        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}%!")
+# About section
+elif choice == 'About':
+    # Load the banner image
+    banner_image_url = "https://raw.githubusercontent.com/Gilbert-B/Forecasting-Sales/0d7b869515bniVmJZZxhyQ8Fee6m6SCLi64M8Ba72c/app/seer.png"
+    banner_image = Image.open(requests.get(banner_image_url, stream=True).raw)
+ 
+   # Display the banner image
+    st.image(banner_image, use_column_width=True)
+    st.markdown('''
+            <p style='font-size: 20px; font-style: italic;font-style: bold;'>
+                    TRUTH is a cutting-edge application specifically designed to combat the spread of fake news. 
+                    Using state-of-the-art algorithms and advanced deep learning techniques, 
+                    our app empowers users to detect and verify the authenticity of news articles. 
+                    TRUTH provides accurate assessments of the reliability of news content. 
+                    With its user-friendly interface and intuitive design, 
+                    the app enables users to easily navigate and obtain trustworthy information in real-time. 
+                    With TRUTH, you can take control of the news you consume and make informed decisions based on verified facts.
+            </p>
+            ''', unsafe_allow_html=True)