Create app.py

app.py

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+import streamlit as st
+
+import re
+import nltk
+from nltk.corpus import stopwords
+from nltk.stem.porter import PorterStemmer
+from sklearn.feature_extraction.text import TfidfVectorizer
+import pickle
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+st.set_page_config(page_title="News Prediction", page_icon=":earth_africa:")
+
+
+tokenizer = AutoTokenizer.from_pretrained("hamzab/roberta-fake-news-classification")
+
+model = AutoModelForSequenceClassification.from_pretrained("hamzab/roberta-fake-news-classification")
+
+def predict_fake(title,text):
+    input_str = "<title>" + title + "<content>" +  text + "<end>"
+    input_ids = tokenizer.encode_plus(input_str, max_length=512, padding="max_length", truncation=True, return_tensors="pt")
+    device =  'cuda' if torch.cuda.is_available() else 'cpu'
+    model.to(device)
+    with torch.no_grad():
+        output = model(input_ids["input_ids"].to(device), attention_mask=input_ids["attention_mask"].to(device))
+    return dict(zip(["Fake","Real"], [x.item() for x in list(torch.nn.Softmax()(output.logits)[0])] ))
+    
+
+
+# Load the model
+# news_model = pickle.load(open("fake_news_predictor_model.pkl", "rb"))
+# vectorizer = pickle.load(open("fakeNews_tfidf_vectorizer.pkl", "rb"))
+
+# Function for preprocessing input text
+# def preProcessing(author, title, text):
+#     input_corpus = author +" " + title + " " + text
+#     input_corpus = re.sub('[^a-zA-Z]', ' ', input_corpus)
+#     input_corpus = input_corpus.lower()
+#     input_corpus = input_corpus.split()
+#     ps = PorterStemmer()
+#     input_corpus = [ps.stem(word) for word in input_corpus if not word in set(stopwords.words('english'))]
+#     input_corpus = ' '.join(input_corpus)
+#     return input_corpus
+
+# # Function to convert text into numerical vector using TF-IDF
+# def convertIntoVector(X):
+#     # Now converting the textual data into numerical vectors using the initialized TF-IDF vectorizer
+#     X = vectorizer.transform(X)
+#     return X
+
+def main():
+    
+    
+    # TO remove streamlit branding and other running animation
+    hide_st_style = """
+                <style>
+                #MainMenu {visibility: hidden;}
+                footer {visibility: hidden;}
+                </style>
+    """
+    st.markdown(hide_st_style, unsafe_allow_html=True)
+
+    # Spinners
+    bar = st.progress(0)
+    for i in range(101):
+        bar.progress(i)
+        # time.sleep(0.02)  # Adjust the sleep time for the desired speed
+
+    # st.balloons()
+
+    # Web content starts
+    # Navbar starts
+        # Create the Streamlit app
+    col1, col2 = st.columns([1, 10])
+    with col1:
+        st.header("	:globe_with_meridians:")
+    with col2:
+        st.header("Fake News Prediction App")
+        
+
+    # Initialize NLTK resources
+    nltk.download('stopwords')
+
+    # Create sidebar section for app description and links
+    st.sidebar.title("Find the fake :mag_right:")
+    st.sidebar.write("Welcome the NLP based fake news detector :male-detective:")
+    st.sidebar.write("""
+
+                This web app predicts whether a given news article is real or fake using a logistic regression model trained on a dataset containing 20,000 sample news articles with an impressive accuracy of 96%. The app employs TF-IDF vectorization and NLTK library preprocessing techniques, including lowercase conversion, regular expressions, tokenization, stemming, and merging textual data.
+
+                Skills Enhanced:
+
+                💬 NLP
+                💻 ML
+                🐍 Python
+                📊 Data Analysis
+                🤖 Transformers
+                🤗 Hugging face
+                
+                    
+\nSteps:   
+                         
+    1. Data Acquisition: Obtained a dataset of 20,000 news articles from various sources.\n
+    2. Data Preprocessing: Handled missing values, tokenization, lowercase conversion, stemming, and unified text data.\n
+    3. Data Visualization: Used Matplotlib for heatmaps, correlation, and confusion matrices.\n
+    4. Model Creation: Trained a logistic regression model with TF-IDF vectorization for classification.\n
+    5. Evaluation: Evaluated model performance with accuracy analysis.\n
+
+By leveraging NLP and ML, this app helps identify false information in news articles, aiding in the fight against misinformation and promoting media literacy.
+        
+**Credits** 🌟\n
+Coder: Aniket Panchal
+GitHub: https://github.com/Aniket2021448
+
+**Contact** 📧\n
+For any inquiries or feedback, please contact [email protected]
+    
+    """)
+    st.sidebar.write("Feel free to check out my other apps:")
+
+
+    with st.sidebar.form("app_selection_form"):
+        st.write("Feel free to explore my other apps :eyes:")
+        app_links = {
+            "Movie-mind": "https://movie-mind.streamlit.app/",
+            "Comment-Feel": "https://huggingface.co/spaces/GoodML/Comment-Feel"
+        }
+        selected_app = st.selectbox("Choose an App", list(app_links.keys()))
+
+        submitted_button = st.form_submit_button("Go to App")
+
+    # Handle form submission
+    if submitted_button:
+        selected_app_url = app_links.get(selected_app)
+        if selected_app_url:
+            st.sidebar.success("Redirected successfully!")
+            st.markdown(f'<meta http-equiv="refresh" content="0;URL={selected_app_url}">', unsafe_allow_html=True)
+
+    
+    # Dropdown menu for other app links
+
+    st.sidebar.write("In case the apps are down, because of less usage")
+    st.sidebar.write("Kindly reach out to me @ [email protected]")
+    
+
+    # Create the form
+    with st.form("news_form"):
+        st.subheader("Enter News Details")
+        # author = st.text_input("Author Name")
+        title = st.text_input("Title")
+        text = st.text_area("Text")
+        submit_button = st.form_submit_button("Submit")
+
+    # Process form submission and make prediction
+    if submit_button:
+
+        # input_text = preProcessing(title, text) 
+        # numerical_data = convertIntoVector([input_text])
+        prediction = predict_fake(title, text)
+        # prediction = news_model.predict(numerical_data)
+        
+        st.subheader(":loudspeaker:Prediction:")
+        # st.write("Prediction: ", prediction)
+        # st.write("Prediction[0]: ", prediction[0])
+        if prediction[0] == 1:
+            st.write("This news is predicted to be **real**.:muscle:")
+        else:
+            st.write("This news is predicted to be **fake**.:shit:")
+
+
+
+if __name__ == "__main__":
+    main()
+
+
+
+