Update README.md

README.md

@@ -1,19 +1,83 @@
 ---
-title: Production Data Analysis
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
+title: Production Data Analysis with AI
+emoji: 🏭
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: 1.28.0
+app_file: app.py
 pinned: false
-short_description: Streamlit template space
+license: mit
 ---
 
-# Welcome to Streamlit!
+# Production Data Analysis Dashboard with AI Assistant
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+A comprehensive Streamlit application for analyzing production data with integrated Google Gemini AI assistant for intelligent insights.
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+## ✨ Features
+
+### 📊 Data Analysis
+- **Production Overview**: Total production, daily averages, and trends
+- **Material Analysis**: Breakdown by material types with detailed statistics
+- **Time Patterns**: Weekly and monthly production patterns
+- **Anomaly Detection**: Automatic detection of production outliers
+- **Interactive Visualizations**: Plotly charts for deep data exploration
+
+### 🤖 AI Assistant
+- **Intelligent Q&A**: Ask questions about your production data in natural language
+- **Quick Insights**: Pre-built questions for common analysis needs
+- **Data-Driven Recommendations**: AI-powered optimization suggestions
+- **Context-Aware**: AI understands your specific production data context
+
+## 🚀 Quick Start
+
+1. **Upload Data**: Upload your production CSV file
+2. **View Analysis**: Explore automated charts and statistics
+3. **Ask AI**: Use the AI assistant for deeper insights
+4. **Get Recommendations**: Receive optimization suggestions
+
+## 📋 Data Format
+
+Your CSV file should contain:
+- `date`: Date in MM/DD/YYYY format
+- `weight_kg`: Production weight in kilograms
+- `material_type`: Type of material (liquid, solid, waste_water, etc.)
+- `shift`: Shift number (optional)
+
+File should be tab-separated (TSV format with .csv extension).
+
+## 🔧 Setup for Development
+
+1. Clone the repository
+2. Install dependencies: `pip install -r requirements.txt`
+3. Add your Google API key to `.streamlit/secrets.toml`
+4. Run: `streamlit run app.py`
+
+## 🔑 API Configuration
+
+To enable AI features, add your Google Gemini API key:
+
+1. Get API key from [Google AI Studio](https://makersuite.google.com/app/apikey)
+2. In Hugging Face Spaces: Go to Settings → Secrets → Add `GOOGLE_API_KEY`
+3. For local development: Add to `.streamlit/secrets.toml`
+
+## 🛠️ Technology Stack
+
+- **Frontend**: Streamlit
+- **Data Processing**: Pandas, NumPy
+- **Visualizations**: Plotly
+- **AI Integration**: Google Gemini 1.5
+- **Deployment**: Hugging Face Spaces
+
+## 📈 Sample Insights
+
+The AI assistant can help you understand:
+- Production efficiency patterns
+- Material type correlations
+- Seasonal trends and anomalies
+- Optimization opportunities
+- Quality control recommendations
+
+## 🤝 Contributing
+
+Feel free to submit issues and enhancement requests!