<<<<<<< HEAD

So'rovnoma Tahlil Tizimi

Diplom loyihasi – Saidov Suhrob Alisherovich, TDTU 178-22

---

O'rnatish va ishga tushirish

1. Python va kutubxonalar

# Python 3.10+ talab qilinadi
pip install -r requirements.txt

2. Ilovani ishga tushirish

python app.py

3. Brauzerda ochish

http://localhost:5000

4. Login

• Foydalanuvchi nomi: admin
• Parol: admin123

---

Foydalanish

1. Fayl yuklash → CSV yoki Excel faylni yuklang
2. Maqsad ustun tanlab (ixtiyoriy, klassifikatsiya uchun)
3. Tahlilni boshlash → tugmasini bosing
4. Natijalar sahifasida ko'ring:
   • Random Forest klassifikatsiya natijalari
   • K-means klaster taqsimoti
   • VADER sentiment tahlili
   • Interaktiv grafiklar

---

Deployment

Bu loyiha Flask backend bilan to‘liq to‘plamdir. Netlify faqat statik saytlarni bevosita joylashtiradi, shuning uchun to‘liq ilova uchun quyidagi xizmatlardan birini tanlang:

• Render: https://render.com
• Railway: https://railway.app
• Fly.io: https://fly.io

Render uchun tayyorlash

1. requirements.txt faylini saqlang.
2. Procfile va runtime.txt qo‘shildi.
3. Render-da yangi Python Web Service yarating va GitHub reponi ulang.

Agar faqat frontend sahifani Netlify-ga joylashtirmoqchi bo‘lsangiz, backend API uchun boshqa xizmatdan URL oling va index.html ichidagi fetch('/api/...') chaqiruvlarini to‘g‘ri domenga yo‘naltiring.

---

Texnologiyalar

Qatlam	Texnologiya
Backend	Python 3.10, Flask 2.3
ML	scikit-learn (Random Forest, K-means)
NLP	vaderSentiment 3.3
Frontend	HTML5, Bootstrap 5.3, Chart.js 4.4
DB	SQLite

---

Loyiha tuzilmasi

survey_app/
├── app.py                  ← Asosiy Flask ilovasi
├── requirements.txt
├── survey.db               ← SQLite (avtomatik yaratiladi)
├── models/
│   ├── classifier.py       ← Random Forest
│   ├── clustering.py       ← K-means
│   └── sentiment.py        ← VADER
├── utils/
│   └── preprocessor.py     ← Ma'lumotlar tayyorlash
├── database/
│   └── db_manager.py       ← SQLite manager
└── templates/
    └── index.html          ← Frontend (SPA)