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fake_news_detection_api

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holcombzv commited on Sep 1, 2025

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Initial commit for model LFS.

Files changed (14) hide show

.gitattributes ADDED Viewed


1	+ *.keras filter=lfs diff=lfs merge=lfs -text
2	+ *.pickle filter=lfs diff=lfs merge=lfs -text

.gitignore ADDED Viewed

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+# Virtual environments
+venv/
+ENV/
+env/
+.venv/
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+# PyInstaller
+*.manifest
+*.spec
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+# Jupyter Notebook checkpoints
+.ipynb_checkpoints
+# IDE settings
+.vscode/
+.idea/
+*.sublime-workspace
+*.sublime-project
+# MyPy / type checker
+.mypy_cache/
+.dmypy.json
+dmypy.json
+# Pyre type checker
+.pyre/
+# Logs
+*.log
+# Local environment files
+.env
+.env.*

app.py ADDED Viewed

+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import tensorflow as tf
+import pickle
+from functions import *
+# Icon links for attribution: <a href="https://www.flaticon.com/free-icons/detective" title="detective icons">Detective icons created by Freepik - Flaticon</a>
+model = tf.keras.models.load_model('model/fact_checker_trained.keras')
+with open('model/fact_checker_tokenizer.pickle', 'rb') as handle:
+    tokenizer = pickle.load(handle)
+app = FastAPI()
+# Enable CORS for frontend/extension
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # or ["chrome-extension://<your-extension-id>"]
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+# Input from frontend
+class TextRequest(BaseModel):
+    text: str
+# Model output
+class PredictionResponse(BaseModel):
+    score: float
+    label: str
+@app.post("/predict", response_model=PredictionResponse)
+async def predict(request: TextRequest):
+    text_clean = clean_text(request.text)
+    score = evaluate_text(text_clean, model, tokenizer)
+    label = 'Real' if score > .5 else 'Fake'
+    return {
+        "score": score,
+        'label': label
+    }

extension/background.js ADDED Viewed

+chrome.runtime.onInstalled.addListener(() => {
+  chrome.contextMenus.create({
+    id: "fakeNewsCheck",
+    title: "Check Fake News",
+    contexts: ["selection"] // only show when text is selected
+  });
+});
+chrome.contextMenus.onClicked.addListener((info, tab) => {
+  if (info.menuItemId === "fakeNewsCheck" && info.selectionText) {
+    // Simulate fake prediction
+    const fakeScore = Math.random();
+    const verdict = fakeScore > 0.5 ? "Fake" : "Real";
+    // Send result to content script to display
+    chrome.scripting.executeScript({
+      target: { tabId: tab.id },
+      func: (score, verdict) => {
+        alert(`Score: ${(score * 100).toFixed(1)}%\nVerdict: ${verdict}`);
+      },
+      args: [fakeScore, verdict]
+    });
+  }
+});

extension/icons/icon128.png ADDED Viewed

extension/icons/icon16.png ADDED Viewed

extension/icons/icon48.png ADDED Viewed

extension/manifest.json ADDED Viewed

+{
+  "manifest_version": 3,
+  "name": "Fake News Lie Detector",
+  "version": "0.0",
+  "description": "Check if an article or snippet might be fake news",
+  "background": {
+    "service_worker": "background.js"
+  },
+  "action": {
+    "default_popup": "popup.html",
+    "default_title": "Fake News Checker",
+    "default_icon": {
+      "16": "icons/icon16.png",
+      "48": "icons/icon48.png",
+      "128": "icons/icon128.png"
+    }
+  },
+  "permissions": ["contextMenus", "scripting", "activeTab"]
+}

extension/popup.html ADDED Viewed

+<!DOCTYPE html>
+<html>
+  <head>
+    <title>Fake News Checker</title>
+    <style>
+      body { font-family: sans-serif; width: 250px; padding: 10px; }
+      textarea { width: 100%; height: 100px; }
+      button { margin-top: 10px; width: 100%; }
+      #result { margin-top: 10px; font-weight: bold; }
+    </style>
+  </head>
+  <body>
+    <h3>Fake News Checker</h3>
+    <textarea id="inputText" placeholder="Paste article text here..."></textarea>
+    <button id="checkButton">Check</button>
+    <div id="result"></div>
+    <script src="popup.js"></script>
+  </body>
+</html>

extension/popup.js ADDED Viewed

+document.getElementById("checkButton").addEventListener("click", () => {
+  const text = document.getElementById("inputText").value;
+  if (!text.trim()) {
+    document.getElementById("result").innerText = "Please enter some text.";
+    return;
+  }
+  // Simulate fake prediction result for now
+  const fakeScore = Math.random(); // placeholder
+  document.getElementById("result").innerText =
+    `Score: ${(fakeScore).toFixed(1)}%`;
+    `Verdict: Real`;
+});

functions.py ADDED Viewed

+import tensorflow as tf
+def clean_text(text):
+    text = text.lower()
+    if '-' in text:
+        text = text.split('-', 1)[1].strip()
+    text = ''.join(char for char in text if char.isalnum() or char.isspace())
+# Use model on scraped text
+def evaluate_text(text, model, tokenizer, max_len=1000):
+    sequence = tokenizer.texts_to_sequences([text])  # Convert text to sequence
+    padded_sequence = tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=max_len)  # Pad to max_len
+    result = float(model.predict(padded_sequence))
+    return result

model/fact_checker_tokenizer.pickle ADDED Viewed

+version https://git-lfs.github.com/spec/v1
+oid sha256:1ae2140f53084a4ee054aa2c085dd56d3acafc87b7ddd633ea1cdda2fa8520de
+size 9815919

model/fact_checker_trained.keras ADDED Viewed

+version https://git-lfs.github.com/spec/v1
+oid sha256:eac58e5fb21c6c80b1568d4f2b5c345faac6bab02e62eea2c470f78024c36cc5
+size 21633699

requirements.txt ADDED Viewed

Binary file (1.84 kB). View file