📊 Customer Feedback Analyzer

🚀 A full-stack web app built with Flask, Bootstrap, and SQLite, powered by Hugging Face Transformers, that collects customer feedback, analyzes sentiment, summarizes pain points and praises, and generates actionable recommendations for product & service teams.

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✨ Features

• 📝 Submit Feedback – Users can add their name, feedback text, and rating (1–5 ⭐).
• 💬 Auto-Scrolling Feedback Carousel – Displays feedback cards with star ratings and classification (Positive / Neutral / Negative).
• 🤖 AI Insights powered by Hugging Face:
  • Sentiment classification
  • Summarization of pain points & praises
  • Actionable product recommendations
• 💾 Persistent Storage – Feedback stored in SQLite database.
• 🎨 Attractive UI – Dark theme, Bootstrap , animated feedback cards, badges for sentiment.

🛠️ Tech Stack

Frontend: HTML, CSS, Bootstrap
Backend: Python (Flask)
Database: SQLite
AI Models: Hugging Face Transformers

• Sentiment → nlptown/bert-base-multilingual-uncased-sentiment
• Summarization → sshleifer/distilbart-cnn-12-6

🚀 Getting Started

1️⃣ Clone the repo

git clone https://github.com/swalhasakeer/Customer_Feedback_Analyzer.git
cd Customer_Feedback_Analyzer

2️⃣ Create virtual environment

python -m venv venv
# Activate it
source venv/bin/activate    # macOS/Linux
venv\Scripts\activate       # Windows

3️⃣ Install dependencies

pip install -r requirements.txt

4️⃣ Run the Flask app

python app.py

5️⃣ Open in browser

👉 http://127.0.0.1:5000

📂 Project Structure

Customer_Feedback_Analyzer/
│
├── app.py              # Flask backend (routes, DB, APIs)
├── llm_models.py       # Hugging Face sentiment/summarization models
├── templates/
│   └── index.html      # Frontend 
├── feedback.db         # SQLite database 
├── requirements.txt    # Python dependencies
└── README.md           # Project documentation

📊 Example Workflow

• User submits feedback with rating ⭐ (1–5).

• Feedback saved in SQLite database.

• Feedback displayed in animated cards with stars + sentiment badge.

• Clicking Analyze All Feedback →

• AI sentiment classification

• Key pain points & praises summarized

• Actionable recommendation generated

🧠 Example AI Insights

Classifications:

• ✅ Positive → “This product has superb quality.”

• ⚠️ Neutral → “The app works okay but could use more features.”

• ❌ Negative → “The app crashes frequently and is very slow.”

Key Pain Points:

• Slow loading times

• Frequent crashes

• Too many ads

Key Praises:

• Intuitive UI

• Responsive customer support

• High product quality

Actionable Recommendation:

• “Focus on improving speed and stability, reducing intrusive ads, and ensuring affordability while maintaining strong UI design and responsive support.”

📌 Future Enhancements

• 🌐 Deployment on Heroku / Render / AWS

• 🔑 User authentication (admin panel for insights)

• 🌍 Multi-language support

• 📑 Export insights as PDF / CSV

• 📊 Dashboard with charts

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