Big O

.gitattributes

@@ -33,9 +33,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-data/*.png filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -19,14 +19,6 @@ tags:
 
 ---
 
-## 🎥 Project Demo Video
-
-[![ReMind Demo Video](https://img.youtube.com/vi/CD0j2dGVycs/maxresdefault.jpg)](https://youtu.be/CD0j2dGVycs)
-
-**Watch the full project demonstration:** [https://youtu.be/CD0j2dGVycs](https://youtu.be/CD0j2dGVycs)
-
----
-
 # 🧠 ReMind — Bring your past to mind
 
 **ReMind is a unified digital-memory assistant that learns from your AI newsletters, curates your Chrome bookmarks, and lets you query both in natural language—all orchestrated by a multi-agent system.**
@@ -44,19 +36,19 @@ Built for the **Agents & MCP Hackathon 2025 — Track 3: Agentic Demo Showcase**
 | Newsletter overload every morning                | One-click OAuth fetch, auto-tagging & summarisation     |
 | Thousand-tab bookmark graveyard                  | AI categorisation & semantic search                     |
 | Context-switching between inbox, browser, search | Single chat interface powered by agents                 |
-| Opaque AI answers                                | Real-time "thought process" trace & clickable citations |
+| Opaque AI answers                                | Real-time “thought process” trace & clickable citations |
 
 ---
 
 ## 🚀 Live Demo — 30-Second Flow
 
-1. **Open the Space** and start with the **"ℹ️ About"** tab to understand the system architecture.
-2. Switch to **"💬 Chat Assistant"** tab to interact with the multi-agent system.
-3. Ask questions like "Search my AI bookmarks" or "Show me my latest emails from news@alphasignal.ai".
-4. Check the **"🏷️ Categories Dashboard"** to see your bookmarks organized by AI categories.  
-5. Watch the agents think in real-time and explore your organized digital knowledge.
+1. **Open the Space** and switch to **Tab 1 — Connect**.
+2. Click **“Connect Gmail”**, grant read-only access, then hit **“Ingest”**.
+3. Jump to **Tab 2 — Analytics** to see tag clouds and sender stats.
+4. Ask questions in **Tab 3 — Ask ReMind** e.g. “Summarise recent Anthropic updates.”
+5. Watch the agents think, then follow the citations back to the original email or bookmark.
 
-[▶︎ **Live Demo Space**](https://huggingface.co/spaces/Agents-MCP-Hackathon/ReMind)
+[▶︎ **4-min video demo**](https://youtu.be/xxxxxxxx)
 
 ---
 
@@ -65,21 +57,21 @@ Built for the **Agents & MCP Hackathon 2025 — Track 3: Agentic Demo Showcase**
 ```
 ReMind/
 ├─ config/                # default prompts, yaml configs
-├─ data/                  # AI bookmarks cache & diagram
+├─ data/                  # sample inbox & bookmark JSON (redacted)
 ├─ src/                   # package root (importable as `remind`)
-│   ├─ agents/            # Manager, Gmail, Bookmark, Categorizer, Web Search
-│   ├─ interfaces/        # Gradio interface with 3 tabs
+│   ├─ agents/            # Manager, Gmail, Bookmark, RAG, etc.
+│   ├─ interfaces/        # Gradio Blocks & helper UI widgets
 │   ├─ tools/             # Gmail API wrapper, bookmark parser, utils
 │   ├─ __init__.py
 │   └─ main.py            # entrypoint when imported as a module
-├─ app.py                 # HF Spaces entrypoint
+├─ app.py                 # HF Spaces entrypoint (imports src.main)
 ├─ .env.example           # copy → .env and fill in secrets
 ├─ requirements.txt       # runtime deps
 ├─ pyproject.toml         # tooling & formatting
 └─ README.md              # you are here
 ```
 
-The **root‐level `app.py`** is the single entrypoint required by Hugging Face Spaces. It loads the Gradio interface from `src/interfaces/gradio_interface.py`.
+The **root‐level `app.py`** is the single entrypoint required by Hugging Face Spaces. It bootstraps the Gradio UI by calling `remind.interfaces.build()` inside `src/`.
 
 ---
 
@@ -101,54 +93,45 @@ $ python app.py         # opens http://localhost:7860
 
 | Key                                            | Description                                              |
 | ---------------------------------------------- | -------------------------------------------------------- |
-| `HF_TOKEN`                                     | Hugging Face token for Qwen3-32B model access          |
-| `GOOGLE_CLIENT_ID` / `GOOGLE_CLIENT_SECRET`    | OAuth credentials from Google Cloud Console             |
-| `GOOGLE_REFRESH_TOKEN` / `GOOGLE_ACCESS_TOKEN` | Generated by Gmail OAuth setup for trusted email access |
+| `HF_TOKEN`                                     | Hugging Face token for optional Inference API calls      |
+| `GOOGLE_CLIENT_ID` / `GOOGLE_CLIENT_SECRET`    | OAuth credentials from Google Cloud Console              |
+| `GOOGLE_REFRESH_TOKEN` / `GOOGLE_ACCESS_TOKEN` | Generated by `python scripts/setup_gmail_credentials.py` |
 
-> **Tip — Gmail setup**: Configure Google Cloud Console OAuth for Gmail API access. Email reading is restricted to `news@alphasignal.ai` for security.
+> **Tip — Gmail setup**: Run `python scripts/setup_gmail_credentials.py` (included in `src/tools`) once. It opens a browser window, you sign in, and it prints the refresh & access tokens for `.env`.
 
 ---
 
 ## 🛠️ Key Components
 
-![ReMind AI System Architecture](data/ai_diagram.png)
-*System architecture showing the multi-agent orchestration and data flow in ReMind*
-
 * **Multi-Agent Orchestrator** — built with *SmolagentS*; assigns Gmail parsing, bookmarking, and RAG tasks to specialists.
-* **LLM Stack** — Qwen3-32B for reasoning and natural language understanding
-* **Vector Store** — in-memory JSON caching for fast bookmark and email access
-* **Gradio 5 UI** — three-tab layout: About, Chat Assistant, and Categories Dashboard with real-time agent "thoughts".
+* **LLM Stack** — OpenAI GPT-4o for reasoning, ADA-002 for embeddings (pluggable).
+* **Vector Store** — in-memory FAISS during hackathon (MongoDB Atlas planned).
+* **Gradio 5 UI** — three-tab layout + real-time agent “thoughts” accordion.
 
 ---
 
 ## 🎯 Core Features (MVP)
 
-1. **Chrome Bookmark Management**
-   • Import and cache AI resources from Chrome bookmarks
-   • Intelligent categorization into 10 AI-focused categories
-   • Search and filter by domain, keywords, and categories
-2. **Gmail Integration (read-only)**  
-   • Secure access to trusted AI newsletter sources
-   • Search and browse recent emails with intelligent filtering
-   • Email content reading with natural language queries
-3. **Natural-Language Q&A**
-   • Multi-agent system for complex query handling
-   • Real-time web search integration for current information
-   • Transparent agent reasoning with step-by-step thinking display
-4. **Categories Dashboard**
-   • Visual organization of AI bookmarks by category
-   • Statistics and insights about your AI knowledge base
-   • Easy browsing and discovery of forgotten resources
+1. **Gmail Ingestion (read-only)**
+   • Detect newsletters via `List-Unsubscribe` header
+   • Parse HTML → Markdown → clean text
+2. **Bookmark Brain**
+   • Import `Bookmarks.json`
+   • Zero-shot topic tagging
+3. **Natural-Language Q\&A**
+   • RAG over combined email+bookmark corpus
+   • Inline citations
+4. **Analytics Dashboard**
+   • Tag cloud, sender histogram, engagement insights
 
 ---
 
 ## 🛣️ Roadmap (Post-Hackathon)
 
-* Enhanced bookmark import from multiple browsers
-* Advanced categorization with machine learning
-* Email notification integration for real-time updates
-* Shared knowledge bases for teams and communities  
-* Mobile-optimized interface for on-the-go access
+* Gmail push notifications for near real-time ingest
+* Atlas Vector Search backend
+* Shared team workspaces
+* Mobile PWA wrapper
 
 Contribute ideas → [Discussions](https://huggingface.co/spaces/Agents-MCP-Hackathon/ReMind/discussions).
 
@@ -161,7 +144,7 @@ PRs welcome! During hack-week we operate on a **fast-merge** basis.
 * README with live demo & video ✔️
 * HF Space public & reproducible ✔️
 
-If ReMind sparks joy, **⭐️ the repo & "Like" the Space** — community engagement counts!
+If ReMind sparks joy, **⭐️ the repo & “Like” the Space** — community engagement counts!
 
 ---
 
@@ -173,7 +156,7 @@ If ReMind sparks joy, **⭐️ the repo & "Like" the Space** — community engag
 | Omar Issa    | [@omarissa24](https://github.com/omarissa24)       | Contributor                          |
 | Ziad Mazzawi | [@zmazz](https://github.com/zmazz)                 | Contributor                          |
 
-Thanks to **Modal Labs**, **Hugging Face**, **Nebius**, **Anthropic**, **OpenAI**, **Hyperbolic Labs**, **MistralAI**, and **Sambanova.AI** for providing credits that power this demo.
+Thanks to **Modal Labs**, **Hugging Face**, **OpenAI**, **MistralAI**, and **Anthropic** for credits that power this demo.
 
 ---
 

app.py

@@ -8,8 +8,10 @@ import os
 import sys
 from dotenv import load_dotenv
 
+# Load environment variables
 load_dotenv()
 
+# Add debug information for deployment
 print(f"Python version: {sys.version}")
 
 
@@ -17,10 +19,12 @@ def main():
     """Main application entry point for Hugging Face Spaces"""
     print("🚀 Starting ReMind Digital Assistant...")
 
+    # Check for required environment variables
     hf_token = os.getenv("HF_TOKEN")
     if not hf_token:
         print("⚠️  Warning: HF_TOKEN not found. Some features may be limited.")
 
+    # Check for Gmail OAuth credentials
     google_client_id = os.getenv("GOOGLE_CLIENT_ID")
     google_client_secret = os.getenv("GOOGLE_CLIENT_SECRET")
     google_refresh_token = os.getenv("GOOGLE_REFRESH_TOKEN")
@@ -33,17 +37,20 @@ def main():
         print("✅ Gmail OAuth credentials configured.")
 
     try:
+        # Import the Gradio interface
         from src.interfaces.gradio_interface import demo
 
         print("✅ ReMind Digital Assistant ready!")
         print("🤔 Real-time AI thinking display enabled")
         print("📧 Email • 🌐 Web Search • 🔖 Bookmarks")
 
+        # Return the demo for Hugging Face Spaces
         return demo
 
     except ImportError as e:
         print(f"❌ Import error: {e}")
         print("Please ensure all dependencies are installed.")
+        # Print more detailed error info for debugging
         import traceback
 
         traceback.print_exc()
@@ -51,16 +58,19 @@ def main():
 
     except Exception as e:
         print(f"❌ Error starting ReMind: {e}")
+        # Print more detailed error info for debugging
         import traceback
 
         traceback.print_exc()
         raise
 
 
+# For Hugging Face Spaces
 if __name__ == "__main__":
     try:
         demo = main()
-        demo.queue(default_concurrency_limit=20).launch(ssr_mode=False)
+        # Simple launch configuration for maximum compatibility
+        demo.launch()
     except Exception as e:
         print(f"❌ Critical error during launch: {e}")
         import traceback
@@ -68,4 +78,5 @@ if __name__ == "__main__":
         traceback.print_exc()
         sys.exit(1)
 else:
+    # When imported, return the demo
     demo = main()

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@@ -1,189 +1,417 @@
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-      "url": "https://tools.forwardfuture.ai/tools/flux-kontext/r/reclzsET2G65tIRwr?_bhlid=fdd79136f4e621cd173c244a3a29508b1d9d5645&utm_campaign=apple-wwdc25-recap-amd-vs-nvidia-enterprise-ai-showdown&utm_medium=newsletter&utm_source=forwardfuture.ai",
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+      "url": "https://my.roboforex.com/en/copyfx/providers/show/326561/?pg=4&grid_token=pamm_providers_trades_closed#pamm_providers_trades_closed",
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+      "url": "https://www.sil.org/linguistics/language-data",
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+      "url": "https://www.iguazio.com/blog/best-free-human-annotated-datasets-for-ml/",
+      "date_added": "13362570265375490",
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+      "category_name": "Benchmarks & Leaderboards"
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+      "url": "https://huggingface.co/RLHFlow/ArmoRM-Llama3-8B-v0.1",
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+      "url": "https://www.youtube.com/watch?v=8CNgaLOdfzU&list=PLWT7hkKacBMKEQlMyOMt6qSzKHTGguP9I&index=14&ab_channel=MatthieuStefani",
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+      "url": "https://huggingface.co/datasets/proj-persona/PersonaHub",
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+      "category_name": "Uncategorized"
+    },
+    {
+      "title": "The 7 best AI image generators in 2025 | Zapier",
+      "url": "https://zapier.com/blog/best-ai-image-generator/?utm_campaign=yt-gbl-nua-evr-Kevin_Stratvert_121624_Third_Party_Channel-vid&utm_medium=social&utm_source=youtube",
+      "date_added": "13385992373817306",
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+      "id": "366",
+      "category": "tools_frameworks",
+      "category_name": "Tools, Frameworks & Platforms"
+    },
+    {
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+      "url": "https://www.satvikshrivastava.tech/#top",
+      "date_added": "13393074779680121",
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+      "id": "370",
+      "category": "uncategorized",
+      "category_name": "Uncategorized"
     }
   ],
-  "last_updated": "2025-06-11T00:55:40.495745",
+  "last_updated": "2025-06-09T00:16:13.739024",
   "folder_name": "AI ressources",
-  "total_count": 26
+  "total_count": 44,
+  "last_categorized": "2025-06-09T00:19:15.060481",
+  "categorization_stats": {
+    "research_breakthroughs": 0,
+    "model_releases": 15,
+    "tools_frameworks": 5,
+    "applications_industry": 0,
+    "regulation_ethics": 0,
+    "investment_funding": 0,
+    "benchmarks_leaderboards": 3,
+    "community_events": 4,
+    "security_privacy": 0,
+    "market_trends": 2,
+    "uncategorized": 15
+  }
 }

requirements.txt

@@ -1,9 +1,9 @@
-gradio
-google-api-python-client
-google-auth-httplib2
-google-auth-oauthlib
-python-dotenv
-smolagents
+gradio>=4.0.0
+google-api-python-client>=2.0.0
+google-auth-httplib2>=0.1.0
+google-auth-oauthlib>=1.0.0
+python-dotenv>=1.0.0
+smolagents>=0.3.0
 smolagents[openai]
-openai
-requests 
+openai>=1.0.0
+requests>=2.25.0 

src/agents/bookmarks_agent.py

@@ -1,5 +1,5 @@
 from typing import List, Dict, Any
-from smolagents import CodeAgent, InferenceClientModel, tool
+from smolagents import CodeAgent, OpenAIServerModel, tool
 import os
 import json
 import sys
@@ -328,9 +328,9 @@ def get_cache_info() -> Dict[str, Any]:
 
 # Instantiate the Bookmarks CodeAgent with enhanced tools
 bookmarks_agent = CodeAgent(
-    model=InferenceClientModel(
-        provider="nebius",
-        token=os.environ["HF_TOKEN"],
+    model=OpenAIServerModel(
+        model_id="o4-mini-2025-04-16",
+        api_key=os.environ["OPENAI_API_KEY"],
     ),
     tools=[
         update_ai_bookmarks_cache,
@@ -343,9 +343,6 @@ bookmarks_agent = CodeAgent(
     ],
     name="bookmarks_agent",
     description="Specialized agent for Chrome bookmarks operations, focusing on AI ressources folder. Extracts bookmarks from Chrome and caches them in data/ai_bookmarks_cache.json to avoid direct interaction with Chrome's raw JSON. Provides search, filtering, statistics, and cache management for AI-related bookmarks.",
-    max_steps=10,
+    max_steps=5,
     additional_authorized_imports=["json", "datetime", "urllib.parse", "pathlib"],
-    # Reduce verbosity
-    stream_outputs=False,
-    max_print_outputs_length=300,
 )

src/agents/categoriser_agent.py

@@ -1,5 +1,5 @@
 from typing import List, Dict, Any
-from smolagents import CodeAgent, InferenceClientModel, tool
+from smolagents import CodeAgent, OpenAIServerModel, tool
 import os
 import json
 from pathlib import Path
@@ -524,9 +524,9 @@ def search_bookmarks_by_category_and_query(category: str, query: str) -> List[Di
 
 # Instantiate the Categoriser CodeAgent
 categoriser_agent = CodeAgent(
-    model=InferenceClientModel(
-        provider="nebius",
-        token=os.environ["HF_TOKEN"],
+    model=OpenAIServerModel(
+        model_id="o4-mini-2025-04-16",
+        api_key=os.environ["OPENAI_API_KEY"],
     ),
     tools=[
         categorize_all_bookmarks,
@@ -538,9 +538,6 @@ categoriser_agent = CodeAgent(
     ],
     name="categoriser_agent",
     description="Specializes in categorizing AI news and bookmarks into 10 predefined categories: Research & Breakthroughs, Model Releases & Updates, Tools/Frameworks/Platforms, Applications & Industry Use Cases, Regulation/Ethics/Policy, Investment/Funding/M&A, Benchmarks & Leaderboards, Community/Events/Education, Security/Privacy/Safety, and Market Trends & Analysis. Uses keyword-based categorization and provides tools for managing and searching categorized content.",
-    max_steps=10,
+    max_steps=5,
     additional_authorized_imports=["json", "datetime", "re", "pathlib"],
-    # Reduce verbosity
-    stream_outputs=False,
-    max_print_outputs_length=300,
 )

src/agents/gmail_agent.py

@@ -1,6 +1,8 @@
+# src/agents/gmail_agent.py
+
 import os
 from typing import List, Dict, Any
-from smolagents import CodeAgent, InferenceClientModel, tool
+from smolagents import CodeAgent, OpenAIServerModel, tool
 
 from src.tools.gmail_mcp_client import get_recent_emails as _get_recent_emails
 from src.tools.gmail_mcp_client import search_emails_simple as _search_emails_simple
@@ -20,6 +22,7 @@ def get_recent_emails(max_results: int = 10) -> List[Dict[str, str]]:
         List of email dictionaries with 'id', 'subject', 'sender', 'date', and 'snippet' fields.
         Returns empty list if no emails found. Each email can be read in detail using read_email_content.
     """
+    # Validate input
     if max_results < 1:
         max_results = 1
     elif max_results > 50:
@@ -42,6 +45,7 @@ def search_emails(query: str, max_results: int = 10) -> List[Dict[str, str]]:
         List of email dictionaries with 'id', 'subject', 'sender', 'date', and 'snippet' fields.
         Returns empty list if no matching emails found. Use read_email_content to get full email text.
     """
+    # Validate input
     if not query or not query.strip():
         print("Error: Empty search query provided")
         return [{"error": "Search query cannot be empty"}]
@@ -74,16 +78,15 @@ def read_email_content(message_id: str) -> Dict[str, Any]:
     return _read_email_content(message_id.strip())
 
 
+# Create a simplified Gmail agent focused on reading emails only
 gmail_agent = CodeAgent(
-    model=InferenceClientModel(
-        provider="nebius",
-        token=os.environ["HF_TOKEN"],
+    model=OpenAIServerModel(
+        model_id="o4-mini-2025-04-16",
+        api_key=os.environ["OPENAI_API_KEY"],
     ),
     tools=[get_recent_emails, search_emails, read_email_content],
     name="gmail_agent",
     description="Gmail agent specialized in reading and searching emails from habib.adoum01@gmail.com and news@alphasignal.ai only",
-    max_steps=10,
+    max_steps=6,  # Reduced from 10 to prevent token overflow
     additional_authorized_imports=["json"],
-    stream_outputs=False,
-    max_print_outputs_length=300,
 )

src/agents/manager_agent.py

@@ -1,6 +1,6 @@
 # src/agents/manager_agent.py
 
-from smolagents import CodeAgent, InferenceClientModel
+from smolagents import CodeAgent, OpenAIServerModel
 from dotenv import load_dotenv
 import os
 
@@ -15,9 +15,9 @@ load_dotenv()
 # Create a single focused agent instead of complex multi-agent system
 # This follows the smolagents principle: "The best agentic systems are the simplest"
 manager_agent = CodeAgent(
-    model=InferenceClientModel(
-        provider="nebius",
-        token=os.environ["HF_TOKEN"],
+    model=OpenAIServerModel(
+        model_id="o4-mini-2025-04-16",
+        api_key=os.environ["OPENAI_API_KEY"],
     ),
     managed_agents=[web_agent, gmail_agent, bookmarks_agent, categoriser_agent],
     name="digital_assistant",
@@ -43,11 +43,8 @@ manager_agent = CodeAgent(
         "• Manually recategorize bookmarks when needed\n\n"
         "I combine these capabilities to help you with research, information gathering, and digital organization tasks."
     ),
-    max_steps=10,  # Reduced to prevent token overflow
+    max_steps=6,  # Reduced to prevent token overflow
     additional_authorized_imports=["json"],
     # Add planning to help with complex queries
     planning_interval=3,  # Plan every 3 steps to maintain focus
-    # Reduce verbosity - disable streaming outputs and minimize console display
-    stream_outputs=False,  # Disable live streaming of thoughts to terminal
-    max_print_outputs_length=500,  # Limit output length to reduce terminal noise
 )

src/agents/web_agents.py

@@ -1,14 +1,14 @@
-import os
 from smolagents import (
     ToolCallingAgent,
-    InferenceClientModel,
+    OpenAIServerModel,
     WebSearchTool,
 )
 
+import os
 
-model = InferenceClientModel(
-    provider="nebius",
-    token=os.environ["HF_TOKEN"],
+model = OpenAIServerModel(
+    model_id="o4-mini-2025-04-16",
+    api_key=os.environ["OPENAI_API_KEY"],
 )
 
 web_agent = ToolCallingAgent(

src/interfaces/gradio_interface.py

@@ -173,152 +173,101 @@ def create_categories_interface():
 def create_about_interface():
     """Create the about page interface."""
 
-    intro_content = """
-    # 🧠 About ReMind - AI and Tech News Assistant
-
-    ## 🎥 Project Demo Video
-
-    [![ReMind Demo Video](https://img.youtube.com/vi/CD0j2dGVycs/maxresdefault.jpg)](https://youtu.be/CD0j2dGVycs)
+    about_content = """
+# 🧠 About ReMind
 
-    **Watch the full project demonstration:** [https://youtu.be/CD0j2dGVycs](https://youtu.be/CD0j2dGVycs)
+## Bring your past to mind.
 
-    ---
+**ReMind** is your intelligent digital memory assistant that helps you rediscover, organize, and make sense of your accumulated digital knowledge. In our information-rich world, we often bookmark valuable resources only to forget about them later. ReMind solves this problem by intelligently categorizing and surfacing your digital discoveries when you need them most.
 
-    ## Bring your AI knowledge to mind.
-
-    **ReMind** is your intelligent digital memory assistant that helps you rediscover, organize, and make sense of your accumulated AI and technology knowledge. In our information-rich world, we often bookmark valuable resources and receive important newsletters only to forget about them later. This system solves this problem by intelligently categorizing and surfacing your digital discoveries when you need them most.
-    """
+---
 
-    about_content = """
 ## 🎯 What ReMind Does
 
 ### 🔖 **Smart Bookmark Management**
-- Automatically extracts and caches AI bookmarks from your Chrome "AI ressources" folder
-- Provides intelligent search and filtering capabilities by domain, keywords, and categories
+- Automatically imports and manages your Chrome bookmarks
+- Provides intelligent search and filtering capabilities
 - Tracks bookmark statistics and usage patterns
-- Focuses specifically on AI and technology resources with comprehensive caching system
-
-### 📧 **Newsletter Email Integration**
-- Securely accesses emails from trusted AI news sources (news@alphasignal.ai)
-- Searches through AI newsletters and updates with intelligent filtering
-- Extracts insights from email-based learning resources
-- Provides recent email browsing and content reading capabilities
-
-### 🌐 **Real-time Web Search**
-- Performs live web searches for the latest AI and tech developments
-- Combines cached knowledge with current information
-- Supports up to 6-step search processes for comprehensive research
-- Delivers real-time results and analysis
+- Focuses specifically on AI and technology resources
 
 ### 🏷️ **Intelligent Categorization**
-The system automatically organizes your content into **10 key AI categories**:
-
-1. **🔬 Research & Breakthroughs** - Latest papers, theoretical advances, and novel architectures
-2. **🚀 Model Releases & Updates** - New AI models, version upgrades, and open-source checkpoints  
-3. **🛠️ Tools, Frameworks & Platforms** - Developer SDKs, libraries, cloud services, and deployment solutions
-4. **🏭 Applications & Industry Use Cases** - Real-world AI implementations across healthcare, finance, manufacturing
-5. **⚖️ Regulation, Ethics & Policy** - AI governance, ethical considerations, and compliance guidelines
-6. **💰 Investment, Funding & M&A** - Market movements, startup funding, and acquisition news
-7. **🏆 Benchmarks & Leaderboards** - Performance comparisons, academic challenges, and ranking systems
-8. **🎓 Community, Events & Education** - Learning resources, conferences, workshops, and tutorials
-9. **🔒 Security, Privacy & Safety** - AI safety research, adversarial attacks, and privacy protection
-10. **📈 Market Trends & Analysis** - Industry insights, adoption rates, and market forecasts
-
-### 💬 **Multi-Agent Conversational Interface**
-- **Manager Agent**: Routes queries to specialized agents based on context
-- **Bookmarks Agent**: Handles Chrome bookmark extraction, caching, and management
-- **Gmail Agent**: Manages email access, search, and content reading
-- **Categorizer Agent**: Provides intelligent classification with manual override capabilities
-- **Web Search Agent**: Performs real-time web research and analysis
+ReMind automatically organizes your bookmarks into **10 key AI categories**:
+
+1. **🔬 Research & Breakthroughs** - Latest papers and theoretical advances
+2. **🚀 Model Releases & Updates** - New AI models and version updates  
+3. **🛠️ Tools, Frameworks & Platforms** - Developer tools and SDKs
+4. **🏭 Applications & Industry Use Cases** - Real-world AI implementations
+5. **⚖️ Regulation, Ethics & Policy** - AI governance and ethical considerations
+6. **💰 Investment, Funding & M&A** - Market movements and startup funding
+7. **🏆 Benchmarks & Leaderboards** - Performance comparisons and competitions
+8. **🎓 Community, Events & Education** - Learning resources and conferences
+9. **🔒 Security, Privacy & Safety** - AI safety and security research
+10. **📈 Market Trends & Analysis** - Industry insights and forecasts
+
+### 💬 **Conversational Interface**
+- Chat naturally with your AI assistant about your bookmarks
+- Ask questions like "Show me my latest AI tools" or "Find research about transformers"
+- Get contextual recommendations based on your interests
+- Real-time thinking process visualization
+
+### 📧 **Email Integration**
+- Browse and search through your important emails
+- Focus on AI newsletters and updates from trusted sources
+- Extract insights from your email-based learning resources
 
 ---
 
 ## 🔧 How It Works
 
-**ReMind** is powered by HuggingFace's **Smolagents**. Here is how it works:
+**ReMind** is powered by **Smolagents**, a modern AI agent framework that enables:
 
-- **🤖 Multi-agent orchestration** - Specialized agents work together seamlessly
-- **🧠 Real-time reasoning** - Watch AI agents think through problems step-by-step
-- **🔄 Dynamic categorization** - Keyword-based classification with manual recategorization
-- **🔍 Semantic search** - Find resources across bookmarks, emails, and web results
-- **💾 Local caching** - Efficient JSON-based storage for offline access
+- **🤖 Multi-tool orchestration** - Seamlessly combines bookmark management, email access, and web search
+- **🧠 Real-time reasoning** - Watch the AI think through problems step-by-step
+- **🔄 Dynamic categorization** - Continuously learns and improves bookmark organization
+- **🔍 Semantic search** - Find resources based on meaning, not just keywords
 
 ---
 
 ## 🚀 Getting Started
 
-1. **Update Bookmark Cache**: Import your Chrome "AI ressources" bookmarks into the local cache
-2. **Categorize Content**: Automatically classify your AI resources into organized categories
-3. **Access Email Updates**: Browse recent newsletters and AI news from trusted sources
-4. **Search Across Sources**: Use natural language to find specific resources across all data sources
-5. **Stay Current**: Combine cached knowledge with real-time web search for latest developments
+1. **Load Your Bookmarks**: Use the chat interface to import your Chrome bookmarks
+2. **Categorize Content**: Ask ReMind to automatically categorize your AI resources
+3. **Explore Categories**: Browse organized categories in the Categories Dashboard
+4. **Search & Discover**: Use natural language to find specific resources
+5. **Stay Updated**: Let ReMind help you track new developments in AI
 
 ---
 
 ## 🔒 Privacy & Security
 
-- **Local JSON Caching**: Bookmarks processed and stored in local cache files
-- **Selective Email Access**: Only accesses specified trusted email sources with OAuth security
-- **Controlled Web Access**: Real-time search with responsible usage limits
-- **Transparent Operations**: All agent operations are visible and explainable
-- **No Data Sharing**: Personal information processed locally with secure authentication
+- **Local Processing**: Your bookmarks are processed and stored locally
+- **Selective Email Access**: Only accesses specified trusted email sources
+- **No Data Sharing**: Your personal information stays on your device
+- **Transparent Operations**: All AI operations are visible and explainable
 
 ---
 
 ## 💡 Why ReMind?
 
-In the fast-moving world of AI and technology, staying informed while managing information overload is challenging. This system transforms your passive bookmark collection and newsletter subscriptions into an active, intelligent knowledge base that:
-
-- **Surfaces forgotten resources** from your Chrome bookmarks
-- **Organizes email newsletters** into actionable intelligence  
-- **Combines multiple sources** for comprehensive AI knowledge management
-- **Provides real-time updates** through web search integration
-- **Learns and adapts** through intelligent categorization and recategorization
-
----
-
-## 🙏 Acknowledgments
+In the fast-moving world of AI and technology, staying informed while managing information overload is challenging. ReMind transforms your passive bookmark collection into an active, intelligent knowledge base that:
 
-Thanks to **Modal Labs**, **Hugging Face**, **Nebius**, **Anthropic**, **OpenAI**, **Hyperbolic Labs**, **MistralAI**, and **Sambanova.AI** for providing credits that power this demo.
+- **Surfaces forgotten gems** from your browsing history
+- **Identifies patterns** in your learning journey  
+- **Suggests connections** between different resources
+- **Keeps you organized** without manual effort
+- **Learns your interests** and adapts over time
 
 ---
 
-*"The smartest AI system is one that helps you manage AI knowledge itself."*
+*"The palest ink is better than the best memory, but the smartest AI makes both ink and memory work together."*
 
-**Welcome to ReMind - where your digital AI knowledge becomes your strategic advantage.**
+**Welcome to ReMind - where your digital past becomes your future advantage.**
     """
 
     with gr.Blocks() as about_tab:
-        gr.Markdown(intro_content)
-
-        # Add the AI architecture diagram
-        gr.Markdown("## 🏗️ System Architecture")
-        gr.Image(
-            value="data/ai_diagram.png",
-            label="ReMind AI System Architecture",
-            show_label=True,
-            show_download_button=True,
-            height=400,
-            width=None,
-            interactive=False,
-        )
-        gr.Markdown("*System architecture showing the multi-agent orchestration and data flow in ReMind*")
         gr.Markdown(about_content)
-    return about_tab
 
-
-def sanitize_content(content):
-    """Sanitize content to ensure it's a clean string without complex objects"""
-    if isinstance(content, str):
-        return content
-    elif isinstance(content, dict):
-        # If content is a dict, convert to string representation
-        return str(content)
-    elif hasattr(content, "__dict__"):
-        # If it's an object with attributes, convert to string
-        return str(content)
-    else:
-        return str(content)
+    return about_tab
 
 
 def validate_message_history(history):
@@ -326,12 +275,10 @@ def validate_message_history(history):
     validated = []
     for msg in history:
         if isinstance(msg, dict) and "role" in msg and "content" in msg:
-            # Ensure content is a string and properly formatted
-            content = sanitize_content(msg["content"])
-
-            # Create a clean message dict to avoid any nesting issues
-            clean_msg = {"role": str(msg["role"]), "content": content}
-            validated.append(clean_msg)
+            # Ensure content is a string
+            if not isinstance(msg["content"], str):
+                msg["content"] = str(msg["content"])
+            validated.append(msg)
         else:
             print(f"Warning: Invalid message format detected: {msg}")
     return validated
@@ -352,23 +299,20 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
             if isinstance(item, dict):
                 # Already a dict, check if it has required keys
                 if "role" in item and "content" in item:
-                    # Ensure content is a simple string
-                    content = sanitize_content(item["content"])
-                    formatted_history.append({"role": str(item["role"]), "content": content})
+                    formatted_history.append(item)
                 else:
                     # Skip malformed dict items
                     print(f"Warning: Skipping malformed history item: {item}")
                     continue
             elif hasattr(item, "role") and hasattr(item, "content"):
-                # ChatMessage object - convert to dict with string content
-                content = sanitize_content(item.content)
-                formatted_history.append({"role": str(item.role), "content": content})
+                # ChatMessage object - convert to dict
+                formatted_history.append({"role": item.role, "content": item.content})
             elif isinstance(item, (list, tuple)) and len(item) == 2:
                 # Legacy format: [user_message, assistant_message] or (user, assistant)
                 # Convert to proper message format
                 if isinstance(item[0], str) and isinstance(item[1], str):
-                    formatted_history.append({"role": "user", "content": str(item[0])})
-                    formatted_history.append({"role": "assistant", "content": str(item[1])})
+                    formatted_history.append({"role": "user", "content": item[0]})
+                    formatted_history.append({"role": "assistant", "content": item[1]})
                 else:
                     print(f"Warning: Skipping malformed history item: {item}")
                     continue
@@ -383,10 +327,10 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
         # Start with user message in history
         new_history = formatted_history.copy()
 
-        # Show initial thinking message with spinner
+        # Show initial thinking message
         thinking_message = {
             "role": "assistant",
-            "content": "🔄 **Agent Planning** ⏳\n\n💫 Analyzing your request and creating execution plan...\n\n*Please wait while I process your request...*",
+            "content": "🧠 **Agent Planning**\n\nAnalyzing your request and creating execution plan...",
         }
         new_history.append(thinking_message)
         yield validate_message_history(new_history)
@@ -404,16 +348,16 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
             for step in agent_stream:
                 step_count += 1
 
-                # Update thinking message with current step info and spinner
+                # Update thinking message with current step info
                 if hasattr(step, "step_number") and hasattr(step, "action"):
-                    step_content = "⚡ **Agent Working** 🔄\n\n"
-                    step_content += f"🔍 **Step {step.step_number}:** *In Progress...*\n\n"
+                    step_content = "🧠 **Agent Planning & Execution**\n\n"
+                    step_content += f"**Step {step.step_number}:**\n"
 
                     if hasattr(step, "thought") and step.thought:
-                        step_content += f"💭 **Thought:** {str(step.thought)}\n\n"
+                        step_content += f"💭 **Thought:** {step.thought}\n\n"
 
                     if hasattr(step, "action") and step.action:
-                        step_content += f"🛠️ **Action:** {str(step.action)}\n\n"
+                        step_content += f"🛠️ **Action:** {step.action}\n\n"
 
                     if hasattr(step, "observations") and step.observations:
                         obs_text = str(step.observations)[:300]
@@ -421,10 +365,7 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
                             obs_text += "..."
                         step_content += f"👁️ **Observation:** {obs_text}\n\n"
 
-                    step_content += "⏳ *Processing next step...*"
-
-                    # Ensure the content is a clean string
-                    thinking_message = {"role": "assistant", "content": str(step_content)}
+                    thinking_message["content"] = step_content
                     new_history[-1] = thinking_message
                     yield validate_message_history(new_history)
 
@@ -432,10 +373,7 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
             # If streaming fails, fall back to regular execution
             print(f"Streaming failed: {stream_error}, falling back to regular execution")
 
-            thinking_message = {
-                "role": "assistant",
-                "content": "⚡ **Agent Working** 🔄\n\n💫 Processing your request using available tools...\n\n⏳ *Please wait...*",
-            }
+            thinking_message["content"] = "🧠 **Agent Working**\n\nProcessing your request using available tools..."
             new_history[-1] = thinking_message
             yield validate_message_history(new_history)
 
@@ -489,35 +427,28 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
                     tool_usage_content = "Agent executed actions successfully"
 
             # Update thinking to show completion
-            thinking_message = {
-                "role": "assistant",
-                "content": "✅ **Agent Complete** 🎉\n\n✅ Request processed successfully\n✅ Response prepared",
-            }
+            thinking_message["content"] = (
+                "🧠 **Agent Complete**\n\n✅ Request processed successfully\n✅ Response prepared"
+            )
             new_history[-1] = thinking_message
             yield validate_message_history(new_history)
 
             # Add tool usage message if there were tools used
             if tool_usage_content:
-                tool_message = {
-                    "role": "assistant",
-                    "content": f"🛠️ **Tools & Actions Used**\n\n{str(tool_usage_content)}",
-                }
+                tool_message = {"role": "assistant", "content": f"🛠️ **Tools & Actions Used**\n\n{tool_usage_content}"}
                 new_history.append(tool_message)
                 yield validate_message_history(new_history)
 
             # Add final response
             final_response = str(result) if result else "I couldn't process your request."
-            final_message = {"role": "assistant", "content": str(final_response)}
+            final_message = {"role": "assistant", "content": final_response}
             new_history.append(final_message)
             yield validate_message_history(new_history)
             return
 
         # If we get here, streaming worked, so get the final result
         # The streaming should have shown all the steps, now get final answer
-        thinking_message = {
-            "role": "assistant",
-            "content": "✅ **Agent Complete** 🎉\n\n✅ All steps executed\n✅ Preparing final response",
-        }
+        thinking_message["content"] = "🧠 **Agent Complete**\n\n✅ All steps executed\n✅ Preparing final response"
         new_history[-1] = thinking_message
         yield validate_message_history(new_history)
 
@@ -529,7 +460,7 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
             if hasattr(last_step, "observations") and last_step.observations:
                 final_response = str(last_step.observations)
 
-        final_message = {"role": "assistant", "content": str(final_response)}
+        final_message = {"role": "assistant", "content": final_response}
         new_history.append(final_message)
         yield validate_message_history(new_history)
 
@@ -551,7 +482,7 @@ def chat_with_agent(message: str, history: List) -> Generator[List, None, None]:
 chat_interface = gr.ChatInterface(
     fn=chat_with_agent,
     type="messages",
-    title="🔖 AI and Tech News Assistant - Powered by Smolagents",
+    title="🔖 Digital Assistant - Powered by Smolagents",
     description="""
     ## Your Comprehensive AI Assistant! 🤖
     
@@ -579,11 +510,13 @@ chat_interface = gr.ChatInterface(
     - Research topics and gather up-to-date data
     
     ---
-    **⏱️ Processing Time Note:** Depending on the type of query, processing can take several seconds or minutes to complete.
+    **🔒 Security Note:** Email read access is limited to `habib.adoum01@gmail.com` and `news@alphasignal.ai`
+    
+    **💡 Watch the agent think in real-time** - You'll see my reasoning process, tool selection, and execution steps in collapsible sections!
     """,
     examples=[
-        "📧 Show me my latest 5 emails",
         "🔖 Search my AI bookmarks",
+        "📧 Show me my latest 5 emails",
         "🤖 Find emails about AI",
         "🌐 Search for latest AI news",
         "💎 What AI resources do I have?",
@@ -596,7 +529,6 @@ chat_interface = gr.ChatInterface(
         "🛠️ Find tools and frameworks bookmarks",
     ],
     show_progress="hidden",
-    cache_examples=False,
 )
 
 # Create categories and about interfaces