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🚀 FULL README (COPY-PASTE)

# 🤖 QA Reporting Framework with AI Insights & Chatbot

A Streamlit-based QA Dashboard that analyzes test execution and defect data using SQLite and AI-powered summaries.

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## 📌 Features

### 📊 QA Dashboard
- Test Execution Overview
- Defect Analytics (Status, Severity, Cycle)
- Data Management module
- KPI-based QA reporting

### 🤖 AI Features
- AI Executive Summary of QA metrics
- QA Chatbot (ask questions about defects, severity, cycles)
- Smart insights based on test data

### 🗄️ Database
- SQLite-based local database
- Auto schema creation
- Sample QA data generator

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## 🏗️ Project Structure

qa_reporting_framework/ │ ├── app.py ├── config.py ├── requirements.txt │ ├── pages/ │ ├── 1_Executive_Overview.py │ ├── 2_Test_Execution.py │ ├── 3_Defect_Analytics.py │ ├── 4_Data_Management.py │ └── 5_AI_Insights_Chat.py │ ├── services/ │ ├── metrics_service.py │ └── openai_service.py │ ├── database/ │ ├── db.py │ ├── schema.sql │ └── qa_reporting.db

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## ⚙️ Installation & Setup

### 1. Clone the repository
```bash
git clone https://github.com/Ashupido/qa_reporting_framework.git

cd qa_reporting_framework

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2. Create virtual environment

python -m venv venv
venv\Scripts\activate   # Windows

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3. Install dependencies

pip install -r requirements.txt

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4. Run the application

streamlit run app.py

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🤖 AI Features (Optional Setup)

If using OpenAI API:

Set API Key (Windows)

set OPENAI_API_KEY=your_api_key_here

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📊 Example Use Cases

• QA Test Reporting Dashboard
• Sprint Test Analysis
• Defect Trend Analysis
• QA Executive Reporting
• AI-based QA assistant chatbot

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🧠 Future Improvements

• Excel upload for test/defect data
• Jira integration
• Power BI style charts
• Real-time API integration
• Advanced AI QA recommendations

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