Add files via upload

.devcontainer/devcontainer.json

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+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "import streamlit as st.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run import streamlit as st.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

streamlit_app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+import os
+import nltk
+import re
+import string
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from nltk.stem import WordNetLemmatizer
+
+# 🔧 Ensure required NLTK resources are downloaded
+nltk_data_path = os.path.expanduser("~/nltk_data")
+nltk.data.path.append(nltk_data_path)
+
+def ensure_nltk_resource(resource):
+    try:
+        nltk.data.find(resource)
+    except LookupError:
+        nltk.download(resource, download_dir=nltk_data_path)
+
+# ✅ Check & download only if necessary
+ensure_nltk_resource("tokenizers/punkt")
+ensure_nltk_resource("corpora/stopwords")
+ensure_nltk_resource("corpora/wordnet")
+ensure_nltk_resource("corpora/omw-1.4")
+ensure_nltk_resource("taggers/averaged_perceptron_tagger")
+
+# ✅ Load NLTK components
+stop_words = set(stopwords.words("english"))
+lemmatizer = WordNetLemmatizer()
+
+# ✅ Load Model & Vectorizer from GitHub repository instead of Google Drive
+try:
+    model = joblib.load("model.pkl")
+    vectorizer = joblib.load("tfidf_vectorizer.pkl")
+except FileNotFoundError:
+    st.error("🚨 Model or vectorizer file not found! Make sure they are uploaded to GitHub.")
+
+# ✅ Load Dataset
+DATA_FILE_ID = "1AsdUWNsA981I0GXty9r345IBC4Ly_D1X"  # Your Google Drive dataset file ID
+
+@st.cache_data
+def download_from_gdrive(file_id, output_path):
+    url = f"https://drive.google.com/uc?id={file_id}"
+    import gdown
+    gdown.download(url, output_path, quiet=False)
+    return output_path
+
+# Check if data.csv exists
+data_path = download_from_gdrive(DATA_FILE_ID, "data.csv")
+
+if os.path.exists(data_path):
+    df = pd.read_csv(data_path)
+else:
+    st.error("🚨 Dataset file not found! Check Google Drive file ID or manually upload it.")
+    df = pd.DataFrame()  # Prevents errors by creating an empty DataFrame
+
+# 🔎 Preprocessing Function (Same as Used in Training)
+def clean_text(text):
+    text = text.lower()
+    text = re.sub(r"\d+", "", text)  # Remove numbers
+    text = re.sub(r"[^\w\s]", "", text)  # Remove punctuation
+    text = re.sub(r"\s+", " ", text).strip()  # Remove extra spaces
+    tokens = word_tokenize(text)
+    tokens = [word for word in tokens if word not in stop_words]
+    tokens = [lemmatizer.lemmatize(word) for word in tokens]
+    return " ".join(tokens)
+
+# 🌐 Streamlit App UI
+st.title("📰 Fake News Detection App")
+
+st.write("### Dataset Overview:")
+if not df.empty:
+    st.write(df.head())  # Show first rows of dataset
+else:
+    st.write("No dataset available.")
+
+# 📝 User Input
+user_input = st.text_area("Enter a news headline or article:")
+
+if st.button("Check News"):
+    if not user_input.strip():
+        st.warning("⚠️ Please enter a news headline or article.")
+    else:
+        cleaned_input = clean_text(user_input)  # Clean input text
+        input_vector = vectorizer.transform([cleaned_input])  # Convert text to TF-IDF
+
+        prediction = model.predict(input_vector)[0]  # Predict
+
+        # 🎯 Show result
+        st.write("### Prediction:")
+        st.success("✅ Real News") if prediction == 1 else st.error("🚨 Fake News")