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@@ -15,6 +15,30 @@ It uses different models (e.g., Naive Bayes, LSTM 🧠) trained on the **LIAR da
+
+## 🌍 Vision
+
+Build a more informed internet by making fact‑checking fast, accessible, and trustworthy for everyone.
+
+
+
+## 🎯 Mission
+
+- ⚡ Speed: deliver quick, reliable fake‑vs‑real assessments
+- 🔎 Clarity: present results with confidence and clear cues
+- 🧠 Learning: support multiple models and continuous improvement
+- 🌐 Access: keep the app simple to use across devices
+
+
+
+## 🤔 Why QuickFactChecker?
+
+- 🧭 One‑place check: paste text or URL and get a verdict fast
+- 🧮 Multiple models: Naive Bayes, Logistic Regression, Random Forest, LSTM
+- 📊 Transparent output: result + confidence to judge reliability
+
+
+
## 🧭 Project Flowchart
```mermaid
@@ -165,6 +189,16 @@ python scripts/setup_nltk.py
+## �️ Troubleshooting
+
+- ⛔ NLTK resource errors: run `python scripts/setup_nltk.py` again; check internet connection
+- 📦 Import errors: ensure virtualenv is active and `pip install -r requirements.txt` ran without errors
+- 🌐 CORS or fetch failures for URLs: verify the target site is reachable; try plain text input
+- 🧪 Notebook issues: update Jupyter and restart kernel; ensure correct interpreter (venv)
+- 🔌 Port in use: stop prior app instance or use a different port
+
+
+
## 📊 Baseline Model Comparison
We evaluated three models on the LIAR dataset using TF-IDF features. Example results 📈 (accuracy & precision):