adding blog + viz fullscreen

app.py

@@ -37,6 +37,7 @@ from src.agents.retrievers.retrievers import *
 from src.managers.ai_manager import AI_Manager
 from src.managers.session_manager import SessionManager
 from src.routes.analytics_routes import router as analytics_router
+from src.routes.blog_routes import router as blog_router
 from src.routes.chat_routes import router as chat_router
 from src.routes.code_routes import router as code_router
 from src.routes.feedback_routes import router as feedback_router
@@ -1583,6 +1584,7 @@ app.include_router(session_router)
 app.include_router(feedback_router)
 app.include_router(deep_analysis_router)
 app.include_router(templates_router)
+app.include_router(blog_router)
 
 if __name__ == "__main__":
     port = int(os.environ.get("PORT", 8000))

src/routes/blog_routes.py

@@ -0,0 +1,96 @@
+import os
+import json
+from fastapi import APIRouter, HTTPException
+from typing import List
+from pydantic import BaseModel
+
+router = APIRouter()
+
+class BlogPost(BaseModel):
+    id: str
+    title: str
+    excerpt: str
+    content: str
+    author: str
+    publishedAt: str
+    tags: List[str]
+    featured: bool
+    readTime: str
+
+@router.get("/api/blog/posts", response_model=List[BlogPost])
+async def get_blog_posts():
+    """Get all blog posts"""
+    try:
+        # Get the path to the utils/data directory
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        utils_dir = os.path.join(current_dir, '..', 'utils', 'data')
+        json_path = os.path.join(utils_dir, 'sample-posts.json')
+        
+        # Normalize the path
+        json_path = os.path.normpath(json_path)
+        
+        if not os.path.exists(json_path):
+            raise HTTPException(status_code=404, detail=f"Blog posts data file not found at: {json_path}")
+        
+        with open(json_path, 'r', encoding='utf-8') as f:
+            posts = json.load(f)
+        
+        return posts
+        
+    except FileNotFoundError:
+        raise HTTPException(status_code=404, detail=f"Blog posts data file not found at: {json_path}")
+    except json.JSONDecodeError:
+        raise HTTPException(status_code=500, detail="Invalid JSON format in blog posts data")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error loading blog posts: {str(e)}")
+
+@router.get("/api/blog/posts/{post_id}", response_model=BlogPost)
+async def get_blog_post(post_id: str):
+    """Get a specific blog post by ID"""
+    try:
+        posts = await get_blog_posts()
+        
+        for post in posts:
+            if post['id'] == post_id:
+                return post
+        
+        raise HTTPException(status_code=404, detail="Blog post not found")
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error loading blog post: {str(e)}")
+
+@router.get("/api/blog/posts/featured", response_model=BlogPost)
+async def get_featured_post():
+    """Get the featured blog post"""
+    try:
+        posts = await get_blog_posts()
+        
+        for post in posts:
+            if post.get('featured', False):
+                return post
+        
+        raise HTTPException(status_code=404, detail="No featured post found")
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error loading featured post: {str(e)}")
+
+@router.get("/api/blog/tags")
+async def get_blog_tags():
+    """Get all unique tags from blog posts"""
+    try:
+        posts = await get_blog_posts()
+        
+        all_tags = set()
+        for post in posts:
+            all_tags.update(post.get('tags', []))
+        
+        return list(all_tags)
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error loading blog tags: {str(e)}")

src/utils/data/sample-posts.json

@@ -0,0 +1,38 @@
+[
+  {
+    "id": "1",
+    "title": "Getting Started with Auto-Analyst: A Complete Guide",
+    "excerpt": "Learn how to set up and use Auto-Analyst for your data analysis needs. This comprehensive guide covers everything from installation to advanced features.",
+    "content": "# Getting Started with Auto-Analyst: A Complete Guide\n\nWelcome to Auto-Analyst! This comprehensive guide will help you get started with our AI-powered analytics platform.\n\n## What is Auto-Analyst?\n\nAuto-Analyst is an AI-powered analytics platform that helps you analyze data, create visualizations, and generate insights automatically. Our platform combines the power of multiple AI models to provide comprehensive data analysis capabilities.\n\n## Key Features\n\n- **AI-Powered Analysis**: Multiple AI models including GPT-4, Claude, and more\n- **Interactive Visualizations**: Create beautiful charts and graphs\n- **Natural Language Queries**: Ask questions in plain English\n- **Multiple Data Sources**: Connect to various data sources\n- **Collaborative Features**: Share insights with your team\n\n## Getting Started\n\n### 1. Sign Up\nFirst, create your account at [Auto-Analyst](https://auto-analyst.com).\n\n### 2. Upload Your Data\nYou can upload data in various formats:\n- CSV files\n- Excel spreadsheets\n- JSON data\n- Direct database connections\n\n### 3. Start Analyzing\nOnce your data is uploaded, you can start asking questions:\n\n```python\n# Example: Analyze sales data\n\"What are the top-selling products this quarter?\"\n```\n\n## Advanced Features\n\n### Custom Agents\nCreate custom analysis agents for specific use cases:\n\n![Auto-Analyst Dashboard](https://via.placeholder.com/600x300/FF7F7F/FFFFFF?text=Auto-Analyst+Dashboard)\n\n### Deep Analysis\nUse our deep analysis feature for complex insights:\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/dQw4w9WgXcQ\" frameborder=\"0\" allowfullscreen></iframe>\n\n## Best Practices\n\n1. **Start Simple**: Begin with basic questions and gradually increase complexity\n2. **Use Clear Language**: Be specific in your queries\n3. **Validate Results**: Always verify AI-generated insights\n4. **Iterate**: Refine your analysis based on initial results\n\n## Support\n\nNeed help? Check out our:\n- [Documentation](https://docs.auto-analyst.com)\n- [Community Forum](https://community.auto-analyst.com)\n- [Contact Support](mailto:support@auto-analyst.com)\n\nHappy analyzing! 🚀",
+    "author": "Auto-Analyst Team",
+    "publishedAt": "2024-01-15",
+    "tags": ["tutorial", "getting-started", "guide"],
+    "featured": true,
+    "readTime": "8 min read"
+  },
+  {
+    "id": "2",
+    "title": "Advanced Data Visualization Techniques",
+    "excerpt": "Discover advanced techniques for creating compelling data visualizations using Auto-Analyst's powerful visualization tools.",
+    "content": "# Advanced Data Visualization Techniques\n\nData visualization is a crucial part of data analysis. In this post, we'll explore advanced techniques for creating compelling visualizations.\n\n## Choosing the Right Chart Type\n\nDifferent data types require different visualization approaches:\n\n### Time Series Data\n- Line charts for trends\n- Area charts for cumulative data\n- Candlestick charts for financial data\n\n### Categorical Data\n- Bar charts for comparisons\n- Pie charts for proportions\n- Treemaps for hierarchical data\n\n### Correlation Analysis\n- Scatter plots for relationships\n- Heatmaps for correlation matrices\n- Bubble charts for multi-dimensional data\n\n## Color Theory in Data Visualization\n\nColors play a crucial role in data visualization:\n\n### Color Palettes\n- **Sequential**: For ordered data (light to dark)\n- **Diverging**: For data with a meaningful center\n- **Qualitative**: For categorical data\n\n### Accessibility\nEnsure your visualizations are accessible:\n- Use colorblind-friendly palettes\n- Provide alternative text descriptions\n- Maintain sufficient contrast ratios\n\n## Interactive Features\n\nMake your visualizations interactive:\n\n```javascript\n// Example: Interactive chart configuration\n{\n  \"type\": \"scatter\",\n  \"data\": {\n    \"x\": [1, 2, 3, 4, 5],\n    \"y\": [2, 4, 6, 8, 10]\n  },\n  \"options\": {\n    \"interactive\": true,\n    \"zoom\": true,\n    \"pan\": true\n  }\n}\n```\n\n## Best Practices\n\n1. **Keep it Simple**: Don't overcomplicate your visualizations\n2. **Tell a Story**: Use visualizations to support your narrative\n3. **Test with Users**: Get feedback on your visualizations\n4. **Mobile Responsive**: Ensure visualizations work on all devices\n\n## Conclusion\n\nEffective data visualization requires both technical skill and design sensibility. By following these techniques, you can create visualizations that effectively communicate your data insights.",
+    "author": "Sarah Johnson",
+    "publishedAt": "2024-01-10",
+    "tags": ["visualization", "design", "best-practices"],
+    "featured": false,
+    "readTime": "6 min read"
+  },
+  {
+    "id": "3",
+    "title": "AI Model Comparison: Which One Should You Use?",
+    "excerpt": "Compare different AI models available in Auto-Analyst and learn when to use each one for optimal results.",
+    "content": "# AI Model Comparison: Which One Should You Use?\n\nAuto-Analyst supports multiple AI models, each with its own strengths and use cases. Let's compare them:\n\n## Available Models\n\n### GPT-4\n- **Best for**: General analysis, creative tasks\n- **Strengths**: Excellent reasoning, creative problem-solving\n- **Use cases**: Exploratory analysis, hypothesis generation\n\n### Claude (Anthropic)\n- **Best for**: Detailed analysis, code generation\n- **Strengths**: Strong reasoning, helpful responses\n- **Use cases**: Technical analysis, documentation\n\n### Gemini (Google)\n- **Best for**: Multimodal analysis, large datasets\n- **Strengths**: Fast processing, good with images\n- **Use cases**: Quick insights, image analysis\n\n### Groq\n- **Best for**: High-speed processing\n- **Strengths**: Very fast inference\n- **Use cases**: Real-time analysis, rapid prototyping\n\n## Performance Comparison\n\n| Model | Speed | Accuracy | Cost | Best Use Case |\n|-------|-------|----------|------|---------------|\n| GPT-4 | Medium | High | High | General analysis |\n| Claude | Medium | High | Medium | Technical tasks |\n| Gemini | Fast | Medium | Low | Quick insights |\n| Groq | Very Fast | Medium | Low | Real-time analysis |\n\n## Choosing the Right Model\n\n### For Beginners\nStart with **Gemini** or **Groq** for fast, cost-effective analysis.\n\n### For Complex Analysis\nUse **GPT-4** or **Claude** for detailed, nuanced insights.\n\n### For Real-time Applications\nChoose **Groq** for high-speed processing.\n\n## Tips for Model Selection\n\n1. **Start Simple**: Begin with faster, cheaper models\n2. **Iterate**: Try different models for the same task\n3. **Compare Results**: Evaluate outputs from multiple models\n4. **Consider Cost**: Balance performance with budget\n\n## Conclusion\n\nThe best model depends on your specific needs. Experiment with different models to find what works best for your use case.",
+    "author": "Mike Chen",
+    "publishedAt": "2024-01-05",
+    "tags": ["ai", "models", "comparison"],
+    "featured": false,
+    "readTime": "5 min read"
+  }
+]
+
+
+

test_datasets.ipynb

@@ -0,0 +1,54 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae0e83db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dspy \n",
+    "import duckdb \n",
+    "\n",
+    "excel = pd.read_excel(io.BytesIO(contents), sheet_name=sheet_name)\n",
+    "\n",
+    "# Preprocessing steps\n",
+    "# 1. Drop empty rows and columns\n",
+    "excel_df.dropna(how='all', inplace=True)  # Remove empty rows\n",
+    "excel_df.dropna(how='all', axis=1, inplace=True)  # Remove empty columns\n",
+    "\n",
+    "# 2. Clean column names\n",
+    "excel_df.columns = excel_df.columns.str.strip()  # Remove extra spaces\n",
+    "\n",
+    "# 3. Convert Excel data to CSV with UTF-8-sig encoding\n",
+    "csv_buffer = io.StringIO()\n",
+    "excel_df.to_csv(csv_buffer, index=False, encoding='utf-8-sig')\n",
+    "csv_buffer.seek(0)\n",
+    "\n",
+    "# Read the processed CSV back into a dataframe\n",
+    "new_df = pd.read_csv(csv_buffer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcef79e3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}