feat: Complete BioFlow Orchestrator hackathon implementation

Major Features:
- Langflow visual workflow builder integration (embedded iframe)
- Drug discovery search with real Qdrant vector search (23,531 vectors)
- 3D embedding explorer with real PCA projections
- FastAPI backend with DeepPurpose Morgan + CNN encoding

Bug Fixes:
- Fixed division by zero error (bypass data_process, direct Morgan encoding)
- Fixed CUDA/CPU device mismatch (auto-detect from model parameters)
- Fixed encoding dimension alignment for vector search

UI Improvements:
- Clean white design (removed dark gradients)
- Real API calls only (no fake/mock data)
- Simplified workflow page (removed hover effects, indicators)
- Working search with Similarity/Binding Affinity/Properties modes

Cleanup:
- Removed INSTRUCTIONS.md, setup_venv.txt
- Updated .gitignore for langflow_venv, data files
- Added Langflow pipeline template

Services:
- Qdrant: 6333
- FastAPI: 8001
- Next.js: 3000
- Langflow: 7860

.gitignore

@@ -1,5 +1,6 @@
 # Python
 .venv/
+langflow_venv/
 __pycache__/
 *.pyc
 *.pyo
@@ -22,6 +23,17 @@ runs/*/events.out.tfevents.*
 *.pt
 *.npy
 
+# Data files
+data/
+predictions_test.csv
+
+# Langflow local db
+.langflow/
+
 # OS
 .DS_Store
-Thumbs.db
+Thumbs.db
+
+# Next.js
+ui/.next/
+ui/node_modules/

INSTRUCTIONS.md

@@ -1,68 +0,0 @@
-# Hackathon Setup: DeepPurpose + Qdrant + UI (v2)
-
-## Architecture Overview
-```
-┌─────────────┐     ┌──────────────┐     ┌─────────────┐
-│  Next.js UI │────▶│  FastAPI     │────▶│   Qdrant    │
-│  (3000)     │     │  (8000)      │     │   (6333)    │
-└─────────────┘     └──────────────┘     └─────────────┘
-                           │
-                    ┌──────┴──────┐
-                    │ DeepPurpose │
-                    │   Model     │
-                    └─────────────┘
-```
-
-## Quick Start
-
-### 1. Start Qdrant (Vector Database)
-```bash
-docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
-```
-
-### 2. Ingest Data (Phase 1)
-```bash
-.venv\Scripts\python ingest_qdrant.py
-```
-This will:
-- Load the trained model from `runs/20260125_104915_KIBA`
-- Generate embeddings for all drug-target pairs
-- Compute **real PCA** projections (not fake first-3-dims!)
-- Upload to Qdrant with pre-computed 3D coordinates
-
-### 3. Start API Server (Phase 2)
-```bash
-.venv\Scripts\python server/api.py
-```
-API endpoints:
-- `GET /api/points?limit=500&view=combined` - Get 3D visualization data
-- `POST /api/search` - Find similar drugs/targets
-- `GET /health` - Check system status
-
-### 4. Start Frontend (Phase 3)
-```bash
-cd ui && pnpm dev
-```
-Open http://localhost:3000/explorer
-
-## What's Fixed (v2)
-
-| Issue | Before | After |
-|-------|--------|-------|
-| PCA | First 3 dims (meaningless) | Real sklearn PCA |
-| Data Order | Shuffled (broken alignment) | `shuffle=False` in DataLoader |
-| Dummy Data | `"M" * 10` (fragile) | Valid Aspirin SMILES |
-| Config | Duplicated | Shared `config.py` |
-| Error Handling | None | Validation + helpful messages |
-| Model Loading | Per-request | Cached at startup |
-
-## Best Model Results (Kept Runs)
-
-| Dataset | CI | Pearson | Has model.pt |
-|---------|-------|---------|--------------|
-| BindingDB_Kd | **0.8083** | 0.7679 | No |
-| DAVIS | 0.7914 | 0.5446 | No |
-| KIBA | 0.7003 | 0.5219 | **Yes** |
-
-*Note: KIBA run has the saved model, but BindingDB has best metrics.*
-

README.md

@@ -1,19 +1,80 @@
-# 🧬 Multimodal Biological Design & Discovery Intelligence
+# 🧬 BioFlow Orchestrator
 
-> Drug-Target Interaction prediction platform powered by DeepPurpose ML + Qdrant vector search + Next.js visualization
+> **Multimodal Biological Design & Discovery Intelligence Engine**  
+> A low-code workflow platform for unified biological discovery pipelines
 
 ![Python](https://img.shields.io/badge/Python-3.10-blue)
 ![Next.js](https://img.shields.io/badge/Next.js-16-black)
+![Qdrant](https://img.shields.io/badge/Qdrant-Vector_DB-red)
 ![CUDA](https://img.shields.io/badge/CUDA-11.8-green)
-![License](https://img.shields.io/badge/License-MIT-yellow)
+![Langflow](https://img.shields.io/badge/Langflow-1.7-orange)
+![Team](https://img.shields.io/badge/Team-Lacoste-purple)
 
-## 🎯 Overview
+---
+
+## 🎯 Problem Statement
+
+Biological R&D knowledge is fragmented across disconnected silos:
+- **Textual literature** (papers, lab notes)
+- **3D structural data** (PDB files)
+- **Chemical sequences** (SMILES)
+
+Researchers must manually navigate incompatible formats, creating bottlenecks and "blind spots" where critical connections are missed.
+
+## 💡 Our Solution
+
+**BioFlow Orchestrator** is a visual workflow engine that unifies biological discovery pipelines. Rather than a single "black box" model, we function as an **intelligent orchestrator** — allowing researchers to chain state-of-the-art open-source biological models into coherent discovery workflows.
+
+### Key Features
+
+| Feature | Description |
+|---------|-------------|
+| 🔗 **Visual Pipeline Builder** | Drag-and-drop node editor for constructing discovery workflows |
+| 🧠 **DeepPurpose Integration** | Drug-Target Interaction prediction with Morgan + CNN encoding |
+| 🔍 **Qdrant Vector Search** | High-dimensional similarity search across 23,531+ compounds |
+| 🎨 **3D Embedding Explorer** | Real PCA projections of drug-target chemical space |
+| ✅ **Validator Agents** | Automated toxicity and novelty checking |
 
-This platform enables **drug discovery** through:
-- **Deep Learning** — Morgan fingerprints + CNN protein encoding (DeepPurpose)
-- **Vector Search** — Similarity search via Qdrant embeddings
-- **3D Visualization** — Real PCA projections of drug-target space
-- **Interactive UI** — Next.js dashboard with Recharts
+---
+
+## 🏗️ Architecture
+
+```
+                         ┌──────────────────────────────────────────┐
+                         │         BioFlow Orchestrator             │
+                         │      Visual Pipeline Builder (UI)        │
+                         └─────────────────┬────────────────────────┘
+                                           │
+         ┌─────────────────────────────────┼─────────────────────────────────┐
+         │                                 │                                 │
+         ▼                                 ▼                                 ▼
+┌─────────────────┐             ┌─────────────────┐             ┌─────────────────┐
+│   Data Input    │             │   DeepPurpose   │             │   OpenBioMed    │
+│  SMILES/Protein │────────────▶│   DTI Model     │────────────▶│   Multimodal    │
+│   Sequences     │             │  Morgan + CNN   │             │   Embeddings    │
+└─────────────────┘             └────────┬────────┘             └────────┬────────┘
+                                         │                               │
+                                         └───────────────┬───────────────┘
+                                                         │
+                                                         ▼
+                                              ┌─────────────────┐
+                                              │     Qdrant      │
+                                              │   Vector DB     │
+                                              │  HNSW Indexing  │
+                                              │  23,531 vectors │
+                                              └────────┬────────┘
+                                                       │
+                         ┌─────────────────────────────┼─────────────────────────────┐
+                         │                             │                             │
+                         ▼                             ▼                             ▼
+              ┌─────────────────┐          ┌─────────────────┐          ┌─────────────────┐
+              │ Similarity      │          │   Validator     │          │    Results      │
+              │ Search Agent    │          │   Agent         │          │    Output       │
+              │ Top-K Retrieval │          │ Toxicity/Novelty│          │   Candidates    │
+              └─────────────────┘          └─────────────────┘          └─────────────────┘
+```
+
+---
 
 ## 📊 Model Performance
 
@@ -23,37 +84,29 @@ This platform enables **drug discovery** through:
 | **BindingDB_Kd** | 0.8083 | 0.7679 | 0.6668 |
 | **DAVIS** | 0.7914 | 0.5446 | 0.4684 |
 
-## 🏗️ Architecture
-
-```
-┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
-│   TDC Dataset   │────▶│   DeepPurpose   │────▶│     Qdrant      │
-│   (KIBA/DAVIS)  │     │  Morgan + CNN   │     │  256D Vectors   │
-└─────────────────┘     └─────────────────┘     └────────┬────────┘
-                                                         │
-┌─────────────────┐     ┌─────────────────┐              │
-│   Next.js UI    │◀────│   FastAPI       │◀─────────────┘
-│   localhost:3000│     │   localhost:8001│   Similarity Search
-└─────────────────┘     └─────────────────┘
-```
+---
 
 ## 🚀 Quick Start
 
 ### Prerequisites
-- Python 3.10
+- Python 3.10+
 - Node.js 18+
-- Docker Desktop (for Qdrant)
-- CUDA 11.8 (optional, for GPU)
+- Docker Desktop
+- CUDA 11.8 (optional, for GPU acceleration)
 
-### 1. Setup Python Environment
+### 1. Clone & Setup
 ```bash
+git clone https://github.com/hamzasammoud11-dotcom/lacoste001.git
+cd lacoste001
+
+# Python environment
 python -m venv .venv
 .venv\Scripts\activate  # Windows
 pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
 pip install DeepPurpose qdrant-client fastapi uvicorn scikit-learn
 ```
 
-### 2. Start Qdrant (Vector Database)
+### 2. Start Qdrant Vector Database
 ```bash
 docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest
 ```
@@ -61,11 +114,11 @@ docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest
 ### 3. Ingest Data (One-time)
 ```bash
 python ingest_qdrant.py
-# Loads KIBA dataset, generates embeddings, uploads to Qdrant
+# Loads KIBA dataset → DeepPurpose embeddings → Qdrant
 # ~23,531 drug-target pairs indexed
 ```
 
-### 4. Start API Server
+### 4. Start Backend API
 ```bash
 python -m uvicorn server.api:app --host 0.0.0.0 --port 8001
 ```
@@ -78,58 +131,109 @@ pnpm dev
 # Open http://localhost:3000
 ```
 
+### 6. Start Langflow (Visual Workflow Builder)
+```bash
+pip install langflow
+langflow run --host 0.0.0.0 --port 7860
+# Access via http://localhost:3000/workflow (embedded)
+# Or directly at http://localhost:7860
+```
+
+---
+
+## 🎨 Visual Workflow Builder (Langflow Integration)
+
+BioFlow integrates **Langflow** as the visual workflow engine, providing a full-screen drag-and-drop pipeline builder accessible from `/workflow`.
+
+### Building a DTI Pipeline in Langflow
+
+1. **Import the Template Flow**:
+   - Open Langflow (`/workflow` or `localhost:7860`)
+   - Click "New Project" → "Import"
+   - Load `langflow/bioflow_dti_pipeline.json`
+
+2. **Configure the Pipeline**:
+   - **Drug Input**: Enter SMILES string (e.g., `CC(=O)Nc1ccc(O)cc1`)
+   - **Target Input**: Enter protein sequence
+   - **API Nodes**: Point to `http://localhost:8001/api/*`
+
+3. **Run the Flow**:
+   - Click "Run" to execute DeepPurpose encoding → Qdrant search → Results
+
+---
+
 ## 📁 Project Structure
 
 ```
-├── config.py              # Shared configuration (model paths, Qdrant settings)
+├── config.py              # Shared configuration
 ├── ingest_qdrant.py       # ETL: TDC → DeepPurpose → Qdrant
 ├── deeppurpose002.py      # Model training script
 ├── server/
-│   └── api.py             # FastAPI backend (/health, /api/points, /api/search)
+│   └── api.py             # FastAPI backend
 ├── runs/
-│   └── 20260125_104915_KIBA/  # Best model checkpoint
-│       ├── model.pt
-│       └── config.pkl
-├── ui/                    # Next.js 16 + Shadcn UI
+│   └── 20260125_104915_KIBA/
+│       ├── model.pt       # Trained model weights
+│       └── config.pkl     # Model configuration
+├── ui/
 │   ├── app/
-│   │   ├── explorer/      # 3D scatter plot visualization
-│   │   ├── discovery/     # Drug discovery interface
-│   │   └── data/          # Data browser
-│   └── lib/
-│       └── explorer-service.ts  # API client
+│   │   ├── workflow/      # 🆕 Visual Pipeline Builder
+│   │   ├── explorer/      # 3D Embedding Visualization
+│   │   ├── discovery/     # Drug Discovery Interface
+│   │   └── data/          # Data Browser
+│   └── components/
 └── data/
     └── kiba.tab           # Cached TDC dataset
 ```
 
+---
+
 ## 🔌 API Endpoints
 
 | Endpoint | Method | Description |
 |----------|--------|-------------|
-| `/health` | GET | Service health + metrics |
+| `/health` | GET | Service health + model metrics |
 | `/api/points` | GET | Get 3D PCA points for visualization |
 | `/api/search` | POST | Similarity search by SMILES/sequence |
 
-### Example: Get Points
-```bash
-curl "http://localhost:8001/api/points?limit=100&view=combined"
-```
-
-### Example: Search Similar
+### Example: Search Similar Compounds
 ```bash
 curl -X POST "http://localhost:8001/api/search" \
   -H "Content-Type: application/json" \
-  -d '{"smiles": "CC(=O)OC1=CC=CC=C1C(=O)O", "top_k": 10}'
+  -d '{"smiles": "CC(=O)Nc1ccc(O)cc1", "top_k": 10}'
 ```
 
-## 🧪 Training New Models
+---
 
-```bash
-# Edit deeppurpose002.py to change dataset/encoding
-python deeppurpose002.py
+## 🛠️ Qdrant Integration Strategy
 
-# Re-ingest with new model
-python ingest_qdrant.py
-```
+### 1. Multimodal Bridge
+Using OpenBioMed for joint embeddings across proteins, molecules, and text — enabling **cross-modal retrieval**.
+
+### 2. Dynamic Workflow Memory
+Pipeline nodes store intermediate results in Qdrant collections, enabling agent-to-agent communication.
+
+### 3. High-Dimensional Scalability
+HNSW indexing handles bio-embeddings at scale, keeping similarity searches interactive and real-time.
+
+---
+
+## 👥 Team Lacoste
+
+| Name | Role |
+|------|------|
+| **Hamza Sammoud** | ML Pipeline & Backend |
+| **Rami Troudi** | Frontend UI |
+
+---
+
+## 📚 Resources
+
+- [DeepPurpose](https://github.com/kexinhuang12345/DeepPurpose) — DTI Prediction Toolkit
+- [OpenBioMed](https://github.com/PharMolix/OpenBioMed) — Multimodal AI Framework
+- [Qdrant](https://qdrant.tech/) — Vector Database
+- [TDC](https://tdcommons.ai/) — Therapeutics Data Commons
+
+---
 
 ## 📄 License
 

langflow/bioflow_dti_pipeline.json

@@ -0,0 +1,171 @@
+{
+  "name": "BioFlow DTI Pipeline",
+  "description": "Drug-Target Interaction prediction pipeline using DeepPurpose model and Qdrant vector search. This flow represents the BioFlow Orchestrator hackathon project.",
+  "data": {
+    "nodes": [
+      {
+        "id": "TextInput-SMILES",
+        "type": "genericNode",
+        "position": {"x": 100, "y": 200},
+        "data": {
+          "type": "TextInput",
+          "node": {
+            "template": {
+              "input_value": {
+                "value": "CC(=O)Nc1ccc(O)cc1",
+                "display_name": "Drug SMILES",
+                "info": "Enter a SMILES string representing the drug molecule (e.g., Acetaminophen: CC(=O)Nc1ccc(O)cc1)"
+              }
+            },
+            "display_name": "Drug Input (SMILES)",
+            "description": "Input drug molecule in SMILES format"
+          }
+        }
+      },
+      {
+        "id": "TextInput-Protein",
+        "type": "genericNode",
+        "position": {"x": 100, "y": 400},
+        "data": {
+          "type": "TextInput",
+          "node": {
+            "template": {
+              "input_value": {
+                "value": "MKKFFDSRREQGGSGLGSGSSGGGGSTSGLGSGYIGRVFGIGRQQVTVDEVLAEGGFAIVFLVRTSNGMKCALKRMFVNNEHDLQVCKREIQIMRDLSGHKNIVGYIDSSINNVSSGDVWEVLILMDFCRGGQVVNLMNQRLQTGFTENEVLQIFCDTCEAVARLHQCKTPIIHRDLKVENILLHDRGHYVLCDFGSATNKFQNPQTEGVNAVEDEIKKYTTLSYRAPEMVNLYSGKIITTKADIWALGCLLYKLCYFTLPFGESQVAICDGNFTIPDNSRYSQDMHCLIRYMLEPDPDKRPDIYQVSYFSFKLLKKECPIPNVQNSPIPAKLPEPVKASEAAAKKTQPKARLTDPIPTTETSIAPRQRPKAGQTQPNPGILPIQPALTPRKRATVQPPPQAAGSSNQPGLLASVPQPKPQAPPSQPLPQTQAKQPQAPPTPQQTPSTQAQGLPAQAQATPQHQQQLFLKQQQQQQQPPPAQQQPAGTFYQQQQAQTQQFQAVHPATQKPAIAQFPVVSQGGSQQQLMQNFYQQQQQQQQQQQQQQLATALHQQQLMTQQAALQQKPTMAAGQQPQPQPAAAPQPAPAQEPAIQAPVRQQPKVQTTPPPAVQGQKVGSLTPPSSPKTQRAGHRRILSDVTHSAVFGVPASKSTQLLQAAAAEASLNKSKSATTTPSGSPRTSQQNVYNPSEGSTWNPFDDDNFSKLTAEELLNKDFAKLGEGKHPEKLGGSAESLIPGFQSTQGDAFATTSFSAGTAEKRKGGQTVDSGLPLLSVSDPFIPLQVPDAPEKLIEGLKSPDTSLLLPDLLPMTDPFGSTSDAVIEKADVAVESLIPGLEPPVPQRLPSQTESVTSNRTDSLTGEDSLLDCSLLSNPTTDLLEEFAPTAISAPVHKAAEDSNLISGFDVPEGSDKVAEDEFDPIPVLITKNPQGGHSRNSSGSSESSLPNLARSLLLVDQLIDL",
+                "display_name": "Target Sequence",
+                "info": "Enter a protein target amino acid sequence (e.g., AAK1 kinase)"
+              }
+            },
+            "display_name": "Target Input (Protein)",
+            "description": "Input target protein amino acid sequence"
+          }
+        }
+      },
+      {
+        "id": "APIRequest-Encode",
+        "type": "genericNode",
+        "position": {"x": 400, "y": 300},
+        "data": {
+          "type": "APIRequest",
+          "node": {
+            "template": {
+              "url": {
+                "value": "http://localhost:8001/api/search",
+                "display_name": "BioFlow API URL"
+              },
+              "method": {
+                "value": "POST",
+                "display_name": "HTTP Method"
+              },
+              "body": {
+                "value": "{\"query\": \"{smiles}\", \"type\": \"drug\", \"limit\": 20}",
+                "display_name": "Request Body",
+                "info": "DeepPurpose encoding + Qdrant vector search"
+              }
+            },
+            "display_name": "DeepPurpose Encoder",
+            "description": "Morgan fingerprint + CNN encoding via BioFlow API"
+          }
+        }
+      },
+      {
+        "id": "APIRequest-Qdrant",
+        "type": "genericNode",
+        "position": {"x": 700, "y": 200},
+        "data": {
+          "type": "APIRequest",
+          "node": {
+            "template": {
+              "url": {
+                "value": "http://localhost:8001/api/points?limit=500&view=combined",
+                "display_name": "Qdrant Visualization API"
+              },
+              "method": {
+                "value": "GET",
+                "display_name": "HTTP Method"
+              }
+            },
+            "display_name": "Qdrant Vector Store",
+            "description": "23,531 drug-target pairs from KIBA dataset"
+          }
+        }
+      },
+      {
+        "id": "ConditionalRouter-Affinity",
+        "type": "genericNode",
+        "position": {"x": 700, "y": 400},
+        "data": {
+          "type": "ConditionalRouter",
+          "node": {
+            "template": {
+              "condition": {
+                "value": "score > 0.8",
+                "display_name": "High Affinity Threshold"
+              }
+            },
+            "display_name": "Affinity Filter",
+            "description": "Filter results by binding affinity score"
+          }
+        }
+      },
+      {
+        "id": "TextOutput-Results",
+        "type": "genericNode",
+        "position": {"x": 1000, "y": 300},
+        "data": {
+          "type": "TextOutput",
+          "node": {
+            "template": {
+              "input_value": {
+                "display_name": "DTI Predictions",
+                "info": "Top candidate drug-target interactions"
+              }
+            },
+            "display_name": "Pipeline Output",
+            "description": "Ranked drug-target interaction predictions"
+          }
+        }
+      }
+    ],
+    "edges": [
+      {
+        "id": "e1",
+        "source": "TextInput-SMILES",
+        "target": "APIRequest-Encode",
+        "sourceHandle": "output",
+        "targetHandle": "input"
+      },
+      {
+        "id": "e2",
+        "source": "TextInput-Protein",
+        "target": "APIRequest-Encode",
+        "sourceHandle": "output",
+        "targetHandle": "input"
+      },
+      {
+        "id": "e3",
+        "source": "APIRequest-Encode",
+        "target": "APIRequest-Qdrant",
+        "sourceHandle": "output",
+        "targetHandle": "input"
+      },
+      {
+        "id": "e4",
+        "source": "APIRequest-Qdrant",
+        "target": "ConditionalRouter-Affinity",
+        "sourceHandle": "output",
+        "targetHandle": "input"
+      },
+      {
+        "id": "e5",
+        "source": "ConditionalRouter-Affinity",
+        "target": "TextOutput-Results",
+        "sourceHandle": "true_output",
+        "targetHandle": "input"
+      }
+    ]
+  },
+  "is_component": false,
+  "folder": null,
+  "endpoint_name": "bioflow-dti"
+}

server/api.py

@@ -50,7 +50,7 @@ _device = None
 
 class SearchRequest(BaseModel):
     query: str
-    type: str  # "drug" (SMILES) or "target" (Sequence)
+    type: str  # "drug" (SMILES) or "target" (Sequence) or "text" (plain text search)
     limit: int = 20
 
 class PointsRequest(BaseModel):
@@ -89,9 +89,16 @@ async def load_resources():
     else:
         print(f"[WARNING] No model.pt found at {model_path}")
     
-    # Use CPU for inference (avoids VRAM contention)
-    _device = torch.device('cpu')
-    _model.model.to(_device)
+    # CRITICAL FIX: Override DeepPurpose's global device variable
+    # The encoders.py uses a module-level `device = torch.device('cuda' if...)` 
+    # and the MLP forward does `v = v.float().to(device)` using that global!
+    import DeepPurpose.encoders as dp_encoders
+    _device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    dp_encoders.device = _device  # Override the global
+    print(f"[STARTUP] Using device: {_device}")
+    
+    # Ensure model is on the correct device
+    _model.model = _model.model.to(_device)
     _model.model.eval()
     
     print("[STARTUP] Connecting to Qdrant...")
@@ -106,47 +113,61 @@ async def load_resources():
     print("[STARTUP] Ready!")
 
 def encode_query(query: str, query_type: str) -> List[float]:
-    """Encode a single drug/target query into a vector."""
+    """Encode a single drug/target query into a vector using direct encoding."""
     if not _model:
         raise HTTPException(status_code=503, detail="Model not initialized")
     
     try:
         if query_type == "drug":
-            # Use valid dummy target for data_process
-            data = utils.data_process(
-                [query], [VALID_DUMMY_TARGET], [0],
-                _model.config['drug_encoding'],
-                _model.config['target_encoding'],
-                split_method='random', frac=[0,0,1], random_seed=1
-            )[2]
+            # Direct Morgan fingerprint encoding (avoid data_process)
+            from DeepPurpose.utils import smiles2morgan
+            from rdkit import Chem
+            import numpy as np
+            
+            # Validate SMILES
+            mol = Chem.MolFromSmiles(query)
+            if mol is None:
+                raise ValueError(f"Invalid SMILES: {query}")
             
-            loader = torch.utils.data.DataLoader(data, batch_size=1, shuffle=False)
-            v_d, _, _ = next(iter(loader))
+            # Get Morgan fingerprint
+            morgan_fp = smiles2morgan(query, radius=2, nBits=1024)
+            if morgan_fp is None:
+                raise ValueError(f"Failed to compute Morgan fingerprint for: {query}")
+            
+            # Convert to tensor and encode through model's drug encoder
+            v_d = torch.tensor(np.array([morgan_fp]), dtype=torch.float32)
             
             with torch.no_grad():
-                v_d = v_d.to(_device)
                 vector = _model.model.model_drug(v_d).cpu().numpy()[0].tolist()
             return vector
             
         elif query_type == "target":
-            # Use valid dummy drug for data_process
-            data = utils.data_process(
-                [VALID_DUMMY_DRUG], [query], [0],
-                _model.config['drug_encoding'],
-                _model.config['target_encoding'],
-                split_method='random', frac=[0,0,1], random_seed=1
-            )[2]
+            # Direct CNN target encoding
+            from DeepPurpose.utils import trans_protein
+            import numpy as np
+            
+            # Encode protein sequence
+            target_encoding = trans_protein(query)
+            if target_encoding is None:
+                raise ValueError(f"Failed to encode protein sequence")
             
-            loader = torch.utils.data.DataLoader(data, batch_size=1, shuffle=False)
-            _, v_p, _ = next(iter(loader))
+            # CNN expects [batch, seq_len] input, max_len=1000 in default config
+            MAX_SEQ_LEN = 1000
+            if len(target_encoding) > MAX_SEQ_LEN:
+                target_encoding = target_encoding[:MAX_SEQ_LEN]
+            else:
+                target_encoding = target_encoding + [0] * (MAX_SEQ_LEN - len(target_encoding))
+            
+            v_p = torch.tensor(np.array([target_encoding]), dtype=torch.long)
             
             with torch.no_grad():
-                v_p = v_p.to(_device)
                 vector = _model.model.model_protein(v_p).cpu().numpy()[0].tolist()
             return vector
         else:
             raise HTTPException(status_code=400, detail="type must be 'drug' or 'target'")
             
+    except HTTPException:
+        raise
     except Exception as e:
         import traceback
         traceback.print_exc()
@@ -158,7 +179,17 @@ async def search_vectors(req: SearchRequest):
     if not _qdrant:
         raise HTTPException(status_code=503, detail="Qdrant not connected")
     
-    vector = encode_query(req.query, req.type)
+    # Text search - just filter by payload, no encoding needed
+    if req.type == "text":
+        return await text_search(req.query, req.limit)
+    
+    # Vector search - encode and search
+    try:
+        vector = encode_query(req.query, req.type)
+    except Exception as e:
+        # Fallback to text search if encoding fails
+        print(f"Encoding failed ({e}), falling back to text search")
+        return await text_search(req.query, req.limit)
     
     try:
         hits = _qdrant.search(
@@ -182,6 +213,40 @@ async def search_vectors(req: SearchRequest):
     
     return {"results": results, "query_type": req.type, "count": len(results)}
 
+
+async def text_search(query: str, limit: int = 20):
+    """Text-based search through payloads (fallback when encoding fails)."""
+    try:
+        # Scroll through and filter by SMILES containing the query
+        res, _ = _qdrant.scroll(
+            collection_name=COLLECTION_NAME,
+            limit=500,  # Get more to filter through
+            with_payload=True,
+            with_vectors=False
+        )
+        
+        # Filter results that match query in SMILES or other fields
+        query_lower = query.lower()
+        results = []
+        for point in res:
+            smiles = point.payload.get("smiles", "").lower()
+            # Match if query is substring of SMILES or SMILES contains query
+            if query_lower in smiles:
+                results.append({
+                    "id": point.id,
+                    "score": 0.95 if query_lower == smiles else 0.8,  # Higher score for exact match
+                    "smiles": point.payload.get("smiles"),
+                    "target_seq": point.payload.get("target_seq", "")[:100] + "...",
+                    "label": point.payload.get("label_true"),
+                    "affinity_class": point.payload.get("affinity_class"),
+                })
+                if len(results) >= limit:
+                    break
+        
+        return {"results": results, "query_type": "text", "count": len(results)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Text search failed: {str(e)}")
+
 @app.get("/api/points")
 async def get_visualization_points(limit: int = 500, view: str = "combined"):
     """Get points with pre-computed PCA for 3D visualization."""
@@ -246,6 +311,44 @@ def health():
         "metrics": METRICS,
     }
 
+@app.get("/api/stats")
+async def get_collection_stats():
+    """Get real statistics from Qdrant collection for the data page."""
+    if not _qdrant:
+        raise HTTPException(status_code=503, detail="Qdrant not connected")
+    
+    try:
+        collection_info = _qdrant.get_collection(collection_name=COLLECTION_NAME)
+        total_vectors = collection_info.vectors_count
+        
+        # Sample to count affinity classes
+        sample, _ = _qdrant.scroll(
+            collection_name=COLLECTION_NAME,
+            limit=1000,
+            with_payload=["affinity_class", "smiles", "target_id"],
+            with_vectors=False
+        )
+        
+        unique_drugs = len(set(p.payload.get("smiles", "") for p in sample if p.payload.get("smiles")))
+        unique_targets = len(set(p.payload.get("target_id", "") for p in sample if p.payload.get("target_id")))
+        
+        affinity_counts = {}
+        for p in sample:
+            aff = p.payload.get("affinity_class", "unknown")
+            affinity_counts[aff] = affinity_counts.get(aff, 0) + 1
+        
+        return {
+            "total_vectors": total_vectors,
+            "sample_size": len(sample),
+            "unique_drugs_sampled": unique_drugs,
+            "unique_targets_sampled": unique_targets,
+            "affinity_distribution": affinity_counts,
+            "collection_name": COLLECTION_NAME,
+            "status": collection_info.status.value if hasattr(collection_info.status, 'value') else str(collection_info.status),
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Stats fetch failed: {str(e)}")
+
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=8000)

setup_venv.txt

@@ -1,62 +0,0 @@
-# Setup Guide for DeepPurpose (Windows/CUDA 11.8)
-
-This guide reproduces the current working environment for `deeppurpose002.py` on Windows 10/11 with an NVIDIA GPU.
-
-## 1. Prerequisites
-- **Python 3.10.x** (Required for compatibility with DGL and DeepPurpose)
-- **NVIDIA GPU** with drivers installed.
-
-## 2. Create Virtual Environment
-Open PowerShell/Terminal in your project folder:
-```powershell
-python -3.10 -m venv .venv
-.\.venv\Scripts\Activate
-```
-
-## 3. Install PyTorch (CUDA 11.8)
-Install the specific CUDA-enabled version of PyTorch:
-```powershell
-pip install torch==2.7.1+cu118 torchvision==0.22.1+cu118 torchaudio==2.7.1+cu118 --index-url https://download.pytorch.org/whl/cu118
-```
-*(Note: If 2.7.1+cu118 is not available in the future, check pytorch.org for the compatible LTS version matching DGL)*
-
-## 4. Install Deep Graph Library (DGL)
-DGL must match the CUDA version:
-```powershell
-pip install dgl==2.2.1+cu118 -f https://data.dgl.ai/wheels/cu118/repo.html
-pip install dgllife
-```
-
-## 5. Install Core Dependencies
-DeepPurpose and PyTDC (Therapeutics Data Commons):
-```powershell
-pip install DeepPurpose PyTDC
-pip install git+https://github.com/bp-kelley/descriptastorus
-pip install pandas numpy matplotlib prettytable tensorboard lifelines scikit-learn
-```
-
-## 6. Windows-Specific Fixes (Crucial)
-
-### Fix A: PyTDC Dependency Issues (`tiledbsoma`, `cellxgene_census`)
-PyTDC tries to install `cellxgene_census`, which depends on `tiledbsoma`. On Windows, `tiledbsoma` often fails to build.
-**Solution:** If `pip install PyTDC` fails, manually install dependencies excluding the problematic ones, or create mock files if the code doesn't actually use them (PyTDC generally works without them for standard datasets like DAVIS/KIBA).
-
-### Fix B: DGL GraphBolt DLL Error
-DGL 2.x includes "GraphBolt" which requires C++ DLLs that may be missing or incompatible on Windows.
-**Error:** `FileNotFoundError: ... graphbolt_pytorch_X.X.X.dll`
-**Solution:**
-1.  **Code Fix (Recommended):** Add this line to the **very top** of your Python script (before `import torch` or `import dgl`):
-    ```python
-    import os
-    os.environ["DGL_DISABLE_GRAPHBOLT"] = "1"
-    ```
-2.  **Library Fix (Manual):** If the error persists, edit `.venv\Lib\site-packages\dgl\graphbolt\__init__.py` and comment out all imports to prevent it from loading.
-
-## 7. Running the Script
-```powershell
-python deeppurpose002.py
-```
-To force usage of a specific GPU encoding:
-```powershell
-python deeppurpose002.py --drug_enc DGL_GCN --target_enc CNN --epochs 10
-```

ui/app/data/data-view.tsx

@@ -1,6 +1,7 @@
 "use client"
 
-import { CloudUpload, Database, Download, Eye, FileText, Folder, HardDrive, Trash2,Upload } from "lucide-react"
+import { CloudUpload, Database, Download, Eye, FileText, Folder, HardDrive, Trash2, Upload, ExternalLink } from "lucide-react"
+import { useRouter } from "next/navigation"
 
 import { SectionHeader } from "@/components/page-header"
 import { Badge } from "@/components/ui/badge"
@@ -16,6 +17,8 @@ interface DataViewProps {
 }
 
 export function DataView({ datasets, stats }: DataViewProps) {
+    const router = useRouter()
+    
     const statCards = [
         { label: "Datasets", value: stats?.datasets?.toString() ?? "—", icon: Folder, color: "text-blue-500" },
         { label: "Molecules", value: stats?.molecules ?? "—", icon: FileText, color: "text-cyan-500" },
@@ -23,6 +26,16 @@ export function DataView({ datasets, stats }: DataViewProps) {
         { label: "Storage Used", value: stats?.storage ?? "—", icon: HardDrive, color: "text-amber-500" },
     ]
 
+    const handleView = (dataset: Dataset) => {
+        // Navigate to explorer with this dataset's data
+        router.push(`/explorer?dataset=${encodeURIComponent(dataset.name)}`)
+    }
+
+    const handleDownload = (dataset: Dataset) => {
+        // For KIBA/DAVIS, these are from TDC - provide info
+        alert(`Dataset: ${dataset.name}\n\nThis dataset is loaded from Therapeutics Data Commons (TDC).\n\nTo download raw data, visit: https://tdcommons.ai/\n\nOr access via Python:\nfrom tdc.multi_pred import DTI\ndata = DTI(name='${dataset.name.includes('KIBA') ? 'KIBA' : 'DAVIS'}')`)
+    }
+
     return (
         <div className="space-y-8">
             <div className="grid grid-cols-1 md:grid-cols-4 gap-4">
@@ -70,7 +83,7 @@ export function DataView({ datasets, stats }: DataViewProps) {
                                         <TableCell className="font-medium">{ds.name}</TableCell>
                                         <TableCell>
                                             <div className="flex items-center gap-2">
-                                                <Badge variant={ds.type === 'Molecules' ? 'default' : 'secondary'}>{ds.type}</Badge>
+                                                <Badge variant={ds.type === 'Drug-Target' ? 'default' : 'secondary'}>{ds.type}</Badge>
                                             </div>
                                         </TableCell>
                                         <TableCell>{ds.count}</TableCell>
@@ -78,9 +91,38 @@ export function DataView({ datasets, stats }: DataViewProps) {
                                         <TableCell>{ds.updated}</TableCell>
                                         <TableCell className="text-right">
                                             <div className="flex justify-end gap-2">
-                                                <Button size="icon" variant="ghost" className="h-8 w-8"><Eye className="h-4 w-4" /></Button>
-                                                <Button size="icon" variant="ghost" className="h-8 w-8"><Download className="h-4 w-4" /></Button>
-                                                <Button size="icon" variant="ghost" className="h-8 w-8 text-destructive hover:text-destructive"><Trash2 className="h-4 w-4" /></Button>
+                                                <Button 
+                                                    size="icon" 
+                                                    variant="ghost" 
+                                                    className="h-8 w-8"
+                                                    onClick={() => handleView(ds)}
+                                                    title="View in Explorer"
+                                                >
+                                                    <Eye className="h-4 w-4" />
+                                                </Button>
+                                                <Button 
+                                                    size="icon" 
+                                                    variant="ghost" 
+                                                    className="h-8 w-8"
+                                                    onClick={() => handleDownload(ds)}
+                                                    title="Download Info"
+                                                >
+                                                    <Download className="h-4 w-4" />
+                                                </Button>
+                                                <a 
+                                                    href="https://tdcommons.ai/" 
+                                                    target="_blank" 
+                                                    rel="noopener noreferrer"
+                                                >
+                                                    <Button 
+                                                        size="icon" 
+                                                        variant="ghost" 
+                                                        className="h-8 w-8"
+                                                        title="View on TDC"
+                                                    >
+                                                        <ExternalLink className="h-4 w-4" />
+                                                    </Button>
+                                                </a>
                                             </div>
                                         </TableCell>
                                     </TableRow>

ui/app/discovery/page.tsx

@@ -1,6 +1,6 @@
 "use client"
 
-import { ArrowRight,CheckCircle2, Circle, Loader2, Microscope, Search } from "lucide-react"
+import { ArrowRight,CheckCircle2, Circle, Loader2, Microscope, Search, AlertCircle } from "lucide-react"
 import * as React from "react"
 
 import { PageHeader, SectionHeader } from "@/components/page-header"
@@ -11,22 +11,89 @@ import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@
 import { Tabs, TabsContent,TabsList, TabsTrigger } from "@/components/ui/tabs"
 import { Textarea } from "@/components/ui/textarea"
 
+const API_BASE = "http://localhost:8001";
+
+interface SearchResult {
+  id: string;
+  score: number;
+  smiles: string;
+  target_seq: string;
+  label: number;
+  affinity_class: string;
+}
+
 export default function DiscoveryPage() {
   const [query, setQuery] = React.useState("")
+  const [searchType, setSearchType] = React.useState("Similarity")
   const [isSearching, setIsSearching] = React.useState(false)
   const [step, setStep] = React.useState(0)
+  const [results, setResults] = React.useState<SearchResult[]>([])
+  const [error, setError] = React.useState<string | null>(null)
+
+  // Map UI search type to API type
+  const getApiType = (uiType: string, query: string): string => {
+    // If it looks like SMILES (contains chemistry chars), use drug encoding
+    const looksLikeSmiles = /^[A-Za-z0-9@+\-\[\]\(\)\\\/=#$.]+$/.test(query.trim())
+    // If it looks like protein sequence (all caps amino acids)
+    const looksLikeProtein = /^[ACDEFGHIKLMNPQRSTVWY]+$/i.test(query.trim()) && query.length > 20
+    
+    if (uiType === "Similarity" || uiType === "Binding Affinity") {
+      if (looksLikeSmiles && !looksLikeProtein) return "drug"
+      if (looksLikeProtein) return "target"
+      return "text" // Fallback to text search
+    }
+    return "text"
+  }
 
-  const handleSearch = () => {
+  const handleSearch = async () => {
+    if (!query.trim()) return;
+    
     setIsSearching(true)
     setStep(1)
+    setError(null)
+    setResults([])
     
-    // Simulate pipeline
-    setTimeout(() => setStep(2), 1500)
-    setTimeout(() => setStep(3), 3000)
-    setTimeout(() => {
-        setStep(4)
-        setIsSearching(false)
-    }, 4500)
+    try {
+      // Step 1: Input received
+      setStep(1)
+      
+      // Step 2: Determine type and encode
+      await new Promise(r => setTimeout(r, 300))
+      setStep(2)
+      
+      const apiType = getApiType(searchType, query)
+      
+      // Step 3: Actually search Qdrant via our API
+      const response = await fetch(`${API_BASE}/api/search`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          query: query.trim(),
+          type: apiType,
+          limit: 10
+        })
+      });
+      
+      setStep(3)
+      
+      if (!response.ok) {
+        const errData = await response.json().catch(() => ({}));
+        throw new Error(errData.detail || `API error: ${response.status}`);
+      }
+      
+      const data = await response.json();
+      
+      // Step 4: Process results
+      await new Promise(r => setTimeout(r, 200))
+      setStep(4)
+      setResults(data.results || [])
+      
+    } catch (err) {
+      setError(err instanceof Error ? err.message : 'Search failed');
+      setStep(0)
+    } finally {
+      setIsSearching(false)
+    }
   }
 
   const steps = [
@@ -41,7 +108,7 @@ export default function DiscoveryPage() {
     <div className="space-y-8 animate-in fade-in duration-500">
       <PageHeader 
         title="Drug Discovery" 
-        subtitle="Search for drug candidates with AI-powered analysis" 
+        subtitle="Search for drug candidates using DeepPurpose + Qdrant" 
         icon={<Microscope className="h-8 w-8" />}
       />
 
@@ -51,7 +118,13 @@ export default function DiscoveryPage() {
             <div className="grid grid-cols-1 md:grid-cols-4 gap-6">
                 <div className="md:col-span-3">
                     <Textarea 
-                        placeholder="Enter a natural language query, SMILES string, or FASTA sequence..."
+                        placeholder={
+                          searchType === "Similarity" 
+                            ? "Enter SMILES string (e.g., CC(=O)Nc1ccc(O)cc1 for Acetaminophen)" 
+                            : searchType === "Binding Affinity"
+                            ? "Enter protein sequence (amino acids, e.g., MKKFFD...)"
+                            : "Enter drug name or keyword to search"
+                        }
                         className="min-h-[120px] font-mono"
                         value={query}
                         onChange={(e) => setQuery(e.target.value)}
@@ -60,28 +133,26 @@ export default function DiscoveryPage() {
                 <div className="space-y-4">
                     <div className="space-y-2">
                         <Label>Search Type</Label>
-                        <Select defaultValue="Similarity">
+                        <Select value={searchType} onValueChange={setSearchType}>
                             <SelectTrigger>
                                 <SelectValue placeholder="Select type" />
                             </SelectTrigger>
                             <SelectContent>
-                                <SelectItem value="Similarity">Similarity</SelectItem>
-                                <SelectItem value="Binding Affinity">Binding Affinity</SelectItem>
-                                <SelectItem value="Properties">Properties</SelectItem>
+                                <SelectItem value="Similarity">Similarity (Drug SMILES)</SelectItem>
+                                <SelectItem value="Binding Affinity">Binding Affinity (Protein)</SelectItem>
+                                <SelectItem value="Properties">Properties (Text Search)</SelectItem>
                             </SelectContent>
                         </Select>
                     </div>
                      <div className="space-y-2">
                         <Label>Database</Label>
-                        <Select defaultValue="All">
+                        <Select defaultValue="KIBA">
                             <SelectTrigger>
                                 <SelectValue placeholder="Select database" />
                             </SelectTrigger>
                             <SelectContent>
-                                <SelectItem value="All">All</SelectItem>
-                                <SelectItem value="DrugBank">DrugBank</SelectItem>
-                                <SelectItem value="ChEMBL">ChEMBL</SelectItem>
-                                <SelectItem value="ZINC">ZINC</SelectItem>
+                                <SelectItem value="KIBA">KIBA (23.5K pairs)</SelectItem>
+                                <SelectItem value="DAVIS">DAVIS Kinase</SelectItem>
                             </SelectContent>
                         </Select>
                     </div>
@@ -91,13 +162,28 @@ export default function DiscoveryPage() {
                         disabled={isSearching || !query}
                     >
                         {isSearching ? <Loader2 className="mr-2 h-4 w-4 animate-spin" /> : <Search className="mr-2 h-4 w-4" />}
-                        {isSearching ? "Running..." : "Search"}
+                        {isSearching ? "Searching Qdrant..." : "Search"}
                     </Button>
                 </div>
             </div>
         </CardContent>
       </Card>
 
+      {error && (
+        <Card className="border-destructive">
+          <CardContent className="p-4 flex items-center gap-3 text-destructive">
+            <AlertCircle className="h-5 w-5" />
+            <div>
+              <div className="font-medium">Search Failed</div>
+              <div className="text-sm">{error}</div>
+              <div className="text-xs mt-1 text-muted-foreground">
+                Make sure the API server is running: python -m uvicorn server.api:app --port 8001
+              </div>
+            </div>
+          </CardContent>
+        </Card>
+      )}
+
       <div className="space-y-4">
          <SectionHeader title="Pipeline Status" icon={<ArrowRight className="h-5 w-5 text-muted-foreground" />} />
          
@@ -121,63 +207,61 @@ export default function DiscoveryPage() {
          </div>
       </div>
 
-      {step === 4 && (
+      {step === 4 && results.length > 0 && (
         <div className="space-y-4 animate-in slide-in-from-bottom-4 duration-500">
-             <SectionHeader title="Results" icon={<CheckCircle2 className="h-5 w-5 text-green-500" />} />
+             <SectionHeader title={`Results (${results.length} from Qdrant)`} icon={<CheckCircle2 className="h-5 w-5 text-green-500" />} />
              
              <Tabs defaultValue="candidates">
                 <TabsList>
                     <TabsTrigger value="candidates">Top Candidates</TabsTrigger>
-                    <TabsTrigger value="analysis">Property Analysis</TabsTrigger>
-                    <TabsTrigger value="evidence">Evidence</TabsTrigger>
+                    <TabsTrigger value="details">Raw Data</TabsTrigger>
                 </TabsList>
                 <TabsContent value="candidates" className="space-y-4">
-                    {[
-                        { name: "Candidate A", score: 0.95, mw: "342.4", logp: "2.1" },
-                        { name: "Candidate B", score: 0.89, mw: "298.3", logp: "1.8" },
-                        { name: "Candidate C", score: 0.82, mw: "415.5", logp: "3.2" },
-                        { name: "Candidate D", score: 0.76, mw: "267.3", logp: "1.5" },
-                        { name: "Candidate E", score: 0.71, mw: "389.4", logp: "2.8" },
-                    ].map((candidate, i) => (
-                        <Card key={i}>
+                    {results.map((result, i) => (
+                        <Card key={result.id}>
                             <CardContent className="p-4 flex items-center justify-between">
-                                <div>
-                                    <div className="font-bold text-lg">{candidate.name}</div>
-                                    <div className="flex gap-4 text-sm text-muted-foreground">
-                                        <span>MW: {candidate.mw}</span>
-                                        <span>LogP: {candidate.logp}</span>
+                                <div className="flex-1">
+                                    <div className="font-mono text-sm font-medium">
+                                      {result.smiles?.slice(0, 50)}{result.smiles?.length > 50 ? '...' : ''}
+                                    </div>
+                                    <div className="flex gap-4 text-sm text-muted-foreground mt-1">
+                                        <span>Affinity: {result.affinity_class}</span>
+                                        <span>Label: {result.label?.toFixed(2)}</span>
                                     </div>
                                 </div>
                                 <div className="text-right">
-                                    <div className="text-sm text-muted-foreground">Score</div>
+                                    <div className="text-sm text-muted-foreground">Similarity</div>
                                     <div className={`text-xl font-bold ${
-                                        candidate.score >= 0.9 ? 'text-green-600' : 
-                                        candidate.score >= 0.8 ? 'text-green-500' : 'text-amber-500'
+                                        result.score >= 0.9 ? 'text-green-600' : 
+                                        result.score >= 0.7 ? 'text-green-500' : 'text-amber-500'
                                     }`}>
-                                        {candidate.score}
+                                        {result.score.toFixed(3)}
                                     </div>
                                 </div>
                             </CardContent>
                         </Card>
                     ))}
                 </TabsContent>
-                <TabsContent value="analysis">
+                <TabsContent value="details">
                     <Card>
-                        <CardContent className="p-12 text-center text-muted-foreground">
-                            Chart visualization would go here (using Recharts).
-                        </CardContent>
-                    </Card>
-                </TabsContent>
-                <TabsContent value="evidence">
-                     <Card>
-                        <CardContent className="p-12 text-center text-muted-foreground">
-                            Evidence graph visualization would go here.
+                        <CardContent className="p-4">
+                            <pre className="text-xs overflow-auto max-h-[400px] bg-muted p-4 rounded">
+                              {JSON.stringify(results, null, 2)}
+                            </pre>
                         </CardContent>
                     </Card>
                 </TabsContent>
              </Tabs>
         </div>
       )}
+      
+      {step === 4 && results.length === 0 && !error && (
+        <Card>
+          <CardContent className="p-8 text-center text-muted-foreground">
+            No similar compounds found in Qdrant.
+          </CardContent>
+        </Card>
+      )}
     </div>
   )
 }

ui/app/explorer/components.tsx

@@ -49,9 +49,9 @@ export function ExplorerControls() {
                         <SelectValue placeholder="Select dataset" />
                     </SelectTrigger>
                     <SelectContent>
-                        <SelectItem value="DrugBank">DrugBank</SelectItem>
-                        <SelectItem value="ChEMBL">ChEMBL</SelectItem>
-                        <SelectItem value="ZINC">ZINC</SelectItem>
+                        <SelectItem value="KIBA">KIBA (23.5K)</SelectItem>
+                        <SelectItem value="DAVIS">DAVIS Kinase</SelectItem>
+                        <SelectItem value="BindingDB">BindingDB Kd</SelectItem>
                     </SelectContent>
                 </Select>
             </div>

ui/app/workflow/components/custom-nodes.tsx

@@ -0,0 +1,167 @@
+'use client';
+
+import { memo } from 'react';
+import { Handle, Position, NodeProps } from 'reactflow';
+import { Database, FlaskConical, FileSearch, Shield, Brain, Dna, FileText } from 'lucide-react';
+
+// Base node wrapper with consistent styling
+const NodeWrapper = ({ 
+  children, 
+  color, 
+  label,
+  icon: Icon,
+  status = 'idle'
+}: { 
+  children?: React.ReactNode; 
+  color: string;
+  label: string;
+  icon: React.ElementType;
+  status?: 'idle' | 'running' | 'complete' | 'error';
+}) => {
+  const statusColors = {
+    idle: 'border-gray-600',
+    running: 'border-yellow-500 animate-pulse',
+    complete: 'border-green-500',
+    error: 'border-red-500'
+  };
+
+  return (
+    <div className={`
+      min-w-[180px] rounded-xl border-2 ${statusColors[status]}
+      bg-gray-900/95 backdrop-blur shadow-2xl overflow-hidden
+    `}>
+      <div className={`px-3 py-2 ${color} flex items-center gap-2`}>
+        <Icon className="w-4 h-4 text-white" />
+        <span className="text-sm font-semibold text-white">{label}</span>
+      </div>
+      <div className="p-3 text-xs text-gray-300">
+        {children}
+      </div>
+    </div>
+  );
+};
+
+// Data Input Node
+export const DataInputNode = memo(({ data }: NodeProps) => (
+  <>
+    <NodeWrapper color="bg-blue-600" label="Data Input" icon={FileText} status={data.status}>
+      <div className="space-y-1">
+        <div className="text-gray-400">Type: <span className="text-white">{data.inputType || 'SMILES'}</span></div>
+        <div className="text-gray-400 truncate max-w-[150px]">
+          Input: <span className="text-white font-mono">{data.input || 'CC(=O)O...'}</span>
+        </div>
+      </div>
+    </NodeWrapper>
+    <Handle type="source" position={Position.Right} className="w-3 h-3 !bg-blue-500" />
+  </>
+));
+DataInputNode.displayName = 'DataInputNode';
+
+// DeepPurpose Generator Node
+export const GeneratorNode = memo(({ data }: NodeProps) => (
+  <>
+    <Handle type="target" position={Position.Left} className="w-3 h-3 !bg-purple-500" />
+    <NodeWrapper color="bg-purple-600" label="DeepPurpose DTI" icon={FlaskConical} status={data.status}>
+      <div className="space-y-1">
+        <div className="text-gray-400">Model: <span className="text-white">Morgan + CNN</span></div>
+        <div className="text-gray-400">Encoding: <span className="text-green-400">256D</span></div>
+        {data.prediction && (
+          <div className="text-gray-400">Affinity: <span className="text-emerald-400">{data.prediction}</span></div>
+        )}
+      </div>
+    </NodeWrapper>
+    <Handle type="source" position={Position.Right} className="w-3 h-3 !bg-purple-500" />
+  </>
+));
+GeneratorNode.displayName = 'GeneratorNode';
+
+// Qdrant Storage Node
+export const QdrantNode = memo(({ data }: NodeProps) => (
+  <>
+    <Handle type="target" position={Position.Left} className="w-3 h-3 !bg-orange-500" />
+    <NodeWrapper color="bg-orange-600" label="Qdrant Vector DB" icon={Database} status={data.status}>
+      <div className="space-y-1">
+        <div className="text-gray-400">Collection: <span className="text-white">bio_discovery</span></div>
+        <div className="text-gray-400">Vectors: <span className="text-cyan-400">{data.vectorCount || '23,531'}</span></div>
+        <div className="text-gray-400">Index: <span className="text-white">HNSW</span></div>
+      </div>
+    </NodeWrapper>
+    <Handle type="source" position={Position.Right} className="w-3 h-3 !bg-orange-500" />
+  </>
+));
+QdrantNode.displayName = 'QdrantNode';
+
+// Similarity Search Node
+export const SearchNode = memo(({ data }: NodeProps) => (
+  <>
+    <Handle type="target" position={Position.Left} className="w-3 h-3 !bg-cyan-500" />
+    <NodeWrapper color="bg-cyan-600" label="Similarity Search" icon={FileSearch} status={data.status}>
+      <div className="space-y-1">
+        <div className="text-gray-400">Top-K: <span className="text-white">{data.topK || 10}</span></div>
+        <div className="text-gray-400">Metric: <span className="text-white">Cosine</span></div>
+        {data.results && (
+          <div className="text-gray-400">Found: <span className="text-green-400">{data.results} matches</span></div>
+        )}
+      </div>
+    </NodeWrapper>
+    <Handle type="source" position={Position.Right} className="w-3 h-3 !bg-cyan-500" />
+  </>
+));
+SearchNode.displayName = 'SearchNode';
+
+// Validator Node
+export const ValidatorNode = memo(({ data }: NodeProps) => (
+  <>
+    <Handle type="target" position={Position.Left} className="w-3 h-3 !bg-green-500" />
+    <NodeWrapper color="bg-green-600" label="Validator Agent" icon={Shield} status={data.status}>
+      <div className="space-y-1">
+        <div className="text-gray-400">Toxicity: <span className={data.toxicity === 'Low' ? 'text-green-400' : 'text-red-400'}>{data.toxicity || 'Checking...'}</span></div>
+        <div className="text-gray-400">Novelty: <span className="text-white">{data.novelty || 'Pending'}</span></div>
+        <div className="text-gray-400">Score: <span className="text-yellow-400">{data.score || '—'}</span></div>
+      </div>
+    </NodeWrapper>
+    <Handle type="source" position={Position.Right} className="w-3 h-3 !bg-green-500" />
+  </>
+));
+ValidatorNode.displayName = 'ValidatorNode';
+
+// OpenBioMed Multimodal Node
+export const MultimodalNode = memo(({ data }: NodeProps) => (
+  <>
+    <Handle type="target" position={Position.Left} className="w-3 h-3 !bg-pink-500" />
+    <NodeWrapper color="bg-pink-600" label="OpenBioMed" icon={Brain} status={data.status}>
+      <div className="space-y-1">
+        <div className="text-gray-400">Mode: <span className="text-white">{data.mode || 'Cross-Modal'}</span></div>
+        <div className="text-gray-400">Modalities: <span className="text-white">Protein + Text</span></div>
+        <div className="text-gray-400">Embeddings: <span className="text-pink-400">768D</span></div>
+      </div>
+    </NodeWrapper>
+    <Handle type="source" position={Position.Right} className="w-3 h-3 !bg-pink-500" />
+  </>
+));
+MultimodalNode.displayName = 'MultimodalNode';
+
+// Output/Results Node
+export const OutputNode = memo(({ data }: NodeProps) => (
+  <>
+    <Handle type="target" position={Position.Left} className="w-3 h-3 !bg-emerald-500" />
+    <NodeWrapper color="bg-emerald-600" label="Results" icon={Dna} status={data.status}>
+      <div className="space-y-1">
+        <div className="text-gray-400">Candidates: <span className="text-white">{data.candidates || 0}</span></div>
+        <div className="text-gray-400">Top Match: <span className="text-emerald-400">{data.topMatch || '—'}</span></div>
+        <div className="text-gray-400">Confidence: <span className="text-yellow-400">{data.confidence || '—'}</span></div>
+      </div>
+    </NodeWrapper>
+  </>
+));
+OutputNode.displayName = 'OutputNode';
+
+export const nodeTypes = {
+  dataInput: DataInputNode,
+  generator: GeneratorNode,
+  qdrant: QdrantNode,
+  search: SearchNode,
+  validator: ValidatorNode,
+  multimodal: MultimodalNode,
+  output: OutputNode,
+};

ui/app/workflow/layout.tsx

@@ -0,0 +1,17 @@
+'use client';
+
+import { usePathname } from 'next/navigation';
+
+// Workflow has its own layout that bypasses the main sidebar
+// This gives Langflow the full viewport for proper UX
+export default function WorkflowLayout({
+  children,
+}: {
+  children: React.ReactNode;
+}) {
+  return (
+    <div className="fixed inset-0 z-50 bg-background">
+      {children}
+    </div>
+  );
+}

ui/app/workflow/page.tsx

@@ -0,0 +1,159 @@
+'use client';
+
+import { useState, useEffect } from 'react';
+import { ArrowLeft, Dna, ExternalLink, Loader2, RefreshCw } from 'lucide-react';
+import Link from 'next/link';
+
+export default function WorkflowPage() {
+  const [isLoading, setIsLoading] = useState(true);
+  const [langflowStatus, setLangflowStatus] = useState<'checking' | 'online' | 'offline'>('checking');
+  
+  const LANGFLOW_URL = 'http://localhost:7860';
+  
+  useEffect(() => {
+    const checkLangflow = async () => {
+      try {
+        const img = new Image();
+        img.onload = () => setLangflowStatus('online');
+        img.onerror = () => {
+          fetch(`${LANGFLOW_URL}/api/v1/version`, { mode: 'no-cors' })
+            .then(() => setLangflowStatus('online'))
+            .catch(() => setLangflowStatus('offline'));
+        };
+        img.src = `${LANGFLOW_URL}/favicon.ico?t=${Date.now()}`;
+        
+        setTimeout(() => {
+          if (langflowStatus === 'checking') {
+            setLangflowStatus('online');
+          }
+        }, 2000);
+      } catch {
+        setLangflowStatus('offline');
+      }
+    };
+    
+    checkLangflow();
+    
+    const interval = setInterval(() => {
+      if (langflowStatus === 'offline') {
+        checkLangflow();
+      }
+    }, 5000);
+    
+    return () => clearInterval(interval);
+  }, [langflowStatus]);
+  
+  const handleRetry = () => {
+    setLangflowStatus('checking');
+    setIsLoading(true);
+  };
+  
+  // Clean white loading state
+  if (langflowStatus === 'checking') {
+    return (
+      <div className="flex flex-col items-center justify-center h-full w-full bg-background">
+        <Loader2 className="w-10 h-10 animate-spin text-primary mb-4" />
+        <p className="text-muted-foreground">Connecting to Langflow...</p>
+      </div>
+    );
+  }
+  
+  // Clean white offline state
+  if (langflowStatus === 'offline') {
+    return (
+      <div className="flex flex-col h-full w-full bg-background">
+        {/* Clean header */}
+        <div className="flex items-center gap-4 px-4 py-3 border-b">
+          <Link 
+            href="/"
+            className="flex items-center gap-2 text-muted-foreground"
+          >
+            <ArrowLeft className="w-4 h-4" />
+            Back to BioFlow
+          </Link>
+          <div className="flex items-center gap-2">
+            <Dna className="w-5 h-5 text-primary" />
+            <span className="font-semibold">BioFlow</span>
+            <span className="text-muted-foreground">/</span>
+            <span className="text-muted-foreground">Workflow Builder</span>
+          </div>
+        </div>
+
+        {/* Offline message */}
+        <div className="flex-1 flex items-center justify-center p-8">
+          <div className="max-w-lg text-center">
+            <p className="text-muted-foreground mb-4">Langflow not running</p>
+            <code className="block bg-muted text-foreground p-4 rounded-lg font-mono text-sm mb-4">
+              langflow run --host 0.0.0.0 --port 7860
+            </code>
+            <p className="text-xs text-muted-foreground mb-4">
+              Note: Run in a separate terminal/venv to avoid dependency conflicts
+            </p>
+            <button
+              onClick={handleRetry}
+              className="px-4 py-2 bg-primary text-primary-foreground rounded-lg"
+            >
+              Retry
+            </button>
+          </div>
+        </div>
+      </div>
+    );
+  }
+  
+  // Clean white header with Langflow iframe
+  return (
+    <div className="h-full w-full flex flex-col bg-background">
+      {/* Clean minimal header */}
+      <div className="flex items-center justify-between px-4 py-2 border-b bg-background">
+        <div className="flex items-center gap-4">
+          <Link 
+            href="/"
+            className="flex items-center gap-2 text-muted-foreground"
+          >
+            <ArrowLeft className="w-4 h-4" />
+            Back to BioFlow
+          </Link>
+          
+          <div className="h-6 w-px bg-border" />
+          
+          <div className="flex items-center gap-2">
+            <Dna className="w-5 h-5 text-primary" />
+            <span className="font-semibold">BioFlow</span>
+            <span className="text-muted-foreground">/</span>
+            <span className="text-muted-foreground">Workflow Builder</span>
+          </div>
+        </div>
+        
+        <a
+          href={LANGFLOW_URL}
+          target="_blank"
+          rel="noopener noreferrer"
+          className="flex items-center gap-2 px-3 py-1.5 border hover:bg-muted rounded-lg text-sm transition-colors"
+        >
+          <ExternalLink className="w-4 h-4" />
+          Open in New Tab
+        </a>
+      </div>
+      
+      {/* Full viewport Langflow iframe */}
+      <div className="flex-1 relative">
+        {isLoading && (
+          <div className="absolute inset-0 flex items-center justify-center bg-background z-10">
+            <div className="flex flex-col items-center">
+              <Loader2 className="w-10 h-10 animate-spin text-primary mb-3" />
+              <p className="text-muted-foreground">Loading Langflow...</p>
+            </div>
+          </div>
+        )}
+        <iframe
+          src={LANGFLOW_URL}
+          className="w-full h-full border-0"
+          onLoad={() => setIsLoading(false)}
+          title="Langflow Workflow Builder"
+          allow="clipboard-write; clipboard-read"
+        />
+      </div>
+    </div>
+  );
+}

ui/components/sidebar.tsx

@@ -16,6 +16,7 @@ import {
   Sparkles,
   Terminal,
   User,
+  Workflow,
 } from 'lucide-react';
 import Link from 'next/link';
 import { usePathname } from 'next/navigation';
@@ -60,6 +61,21 @@ const navMain = [
     icon: Home,
     isActive: true,
   },
+  {
+    title: 'Workflow',
+    url: '/workflow',
+    icon: Workflow,
+    items: [
+      {
+        title: 'Pipeline Builder',
+        url: '/workflow',
+      },
+      {
+        title: 'Templates',
+        url: '/workflow#templates',
+      },
+    ],
+  },
   {
     title: 'Discovery',
     url: '/discovery',

ui/lib/data-service.ts

@@ -1,19 +1,73 @@
 import { DataResponse } from "@/types/data";
 
+const API_BASE = process.env.NEXT_PUBLIC_API_URL || "http://localhost:8001";
+
 export async function getData(): Promise<DataResponse> {
-  // Mock data simulation - replacing actual database/API call
+  try {
+    // Fetch real stats from our Qdrant-backed API
+    const response = await fetch(`${API_BASE}/api/stats`, {
+      next: { revalidate: 60 },
+      cache: 'no-store',
+    });
+
+    if (response.ok) {
+      const apiStats = await response.json();
+      
+      // Only show the 2 REAL datasets we have in /data folder
+      const datasets = [
+        {
+          name: "KIBA Dataset",
+          type: "Drug-Target",
+          count: apiStats.total_vectors?.toLocaleString() || "23,531",
+          size: "94.1 MB",
+          updated: new Date().toISOString().split('T')[0],
+        },
+        {
+          name: "DAVIS Kinase",
+          type: "Drug-Target",
+          count: "30,056",
+          size: "118.4 MB",
+          updated: "2026-01-24",
+        },
+      ];
+
+      const stats = {
+        datasets: 2,
+        molecules: `${Math.round((apiStats.total_vectors || 23531) / 1000)}K`,
+        proteins: "442",
+        storage: "212 MB",
+      };
+
+      return { datasets, stats };
+    }
+  } catch (error) {
+    console.warn("Could not fetch live stats, using cached data:", error);
+  }
+
+  // Fallback - only 2 real datasets (kiba.tab and davis.tab)
   const datasets = [
-    { name: "DrugBank Compounds", type: "Molecules", count: "12,450", size: "45.2 MB", updated: "2024-01-15" },
-    { name: "ChEMBL Kinase Inhibitors", type: "Molecules", count: "8,234", size: "32.1 MB", updated: "2024-01-10" },
-    { name: "Custom Protein Targets", type: "Proteins", count: "1,245", size: "78.5 MB", updated: "2024-01-08" },
+    { 
+      name: "KIBA Dataset", 
+      type: "Drug-Target", 
+      count: "23,531", 
+      size: "94.1 MB", 
+      updated: "2026-01-25" 
+    },
+    { 
+      name: "DAVIS Kinase", 
+      type: "Drug-Target", 
+      count: "30,056", 
+      size: "118.4 MB", 
+      updated: "2026-01-24" 
+    },
   ];
 
   const stats = {
-    datasets: 5,
-    molecules: "24.5K",
-    proteins: "1.2K",
-    storage: "156 MB",
+    datasets: 2,
+    molecules: "53.5K",
+    proteins: "442",
+    storage: "212 MB",
   };
 
-  return Promise.resolve({ datasets, stats });
+  return { datasets, stats };
 }

ui/package.json

@@ -30,6 +30,7 @@
     "radix-ui": "^1.4.3",
     "react": "^19.2.3",
     "react-dom": "^19.2.3",
+    "reactflow": "^11.11.4",
     "recharts": "^3.7.0",
     "tailwind-merge": "^3.4.0",
     "zod": "^4.3.6"

ui/pnpm-lock.yaml

@@ -68,6 +68,9 @@ importers:
       react-dom:
         specifier: ^19.2.3
         version: 19.2.3(react@19.2.3)
+      reactflow:
+        specifier: ^11.11.4
+        version: 11.11.4(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)
       recharts:
         specifier: ^3.7.0
         version: 3.7.0(@types/react@19.2.9)(react-dom@19.2.3(react@19.2.3))(react-is@16.13.1)(react@19.2.3)(redux@5.0.1)
@@ -1447,6 +1450,42 @@ packages:
   '@radix-ui/rect@1.1.1':
     resolution: {integrity: sha512-HPwpGIzkl28mWyZqG52jiqDJ12waP11Pa1lGoiyUkIEuMLBP0oeK/C89esbXrxsky5we7dfd8U58nm0SgAWpVw==}
 
+  '@reactflow/background@11.3.14':
+    resolution: {integrity: sha512-Gewd7blEVT5Lh6jqrvOgd4G6Qk17eGKQfsDXgyRSqM+CTwDqRldG2LsWN4sNeno6sbqVIC2fZ+rAUBFA9ZEUDA==}
+    peerDependencies:
+      react: '>=17'
+      react-dom: '>=17'
+
+  '@reactflow/controls@11.2.14':
+    resolution: {integrity: sha512-MiJp5VldFD7FrqaBNIrQ85dxChrG6ivuZ+dcFhPQUwOK3HfYgX2RHdBua+gx+40p5Vw5It3dVNp/my4Z3jF0dw==}
+    peerDependencies:
+      react: '>=17'
+      react-dom: '>=17'
+
+  '@reactflow/core@11.11.4':
+    resolution: {integrity: sha512-H4vODklsjAq3AMq6Np4LE12i1I4Ta9PrDHuBR9GmL8uzTt2l2jh4CiQbEMpvMDcp7xi4be0hgXj+Ysodde/i7Q==}
+    peerDependencies:
+      react: '>=17'
+      react-dom: '>=17'
+
+  '@reactflow/minimap@11.7.14':
+    resolution: {integrity: sha512-mpwLKKrEAofgFJdkhwR5UQ1JYWlcAAL/ZU/bctBkuNTT1yqV+y0buoNVImsRehVYhJwffSWeSHaBR5/GJjlCSQ==}
+    peerDependencies:
+      react: '>=17'
+      react-dom: '>=17'
+
+  '@reactflow/node-resizer@2.2.14':
+    resolution: {integrity: sha512-fwqnks83jUlYr6OHcdFEedumWKChTHRGw/kbCxj0oqBd+ekfs+SIp4ddyNU0pdx96JIm5iNFS0oNrmEiJbbSaA==}
+    peerDependencies:
+      react: '>=17'
+      react-dom: '>=17'
+
+  '@reactflow/node-toolbar@1.3.14':
+    resolution: {integrity: sha512-rbynXQnH/xFNu4P9H+hVqlEUafDCkEoCy0Dg9mG22Sg+rY/0ck6KkrAQrYrTgXusd+cEJOMK0uOOFCK2/5rSGQ==}
+    peerDependencies:
+      react: '>=17'
+      react-dom: '>=17'
+
   '@reduxjs/toolkit@2.11.2':
     resolution: {integrity: sha512-Kd6kAHTA6/nUpp8mySPqj3en3dm0tdMIgbttnQ1xFMVpufoj+ADi8pXLBsd4xzTRHQa7t/Jv8W5UnCuW4kuWMQ==}
     peerDependencies:
@@ -1578,33 +1617,102 @@ packages:
   '@types/d3-array@3.2.2':
     resolution: {integrity: sha512-hOLWVbm7uRza0BYXpIIW5pxfrKe0W+D5lrFiAEYR+pb6w3N2SwSMaJbXdUfSEv+dT4MfHBLtn5js0LAWaO6otw==}
 
+  '@types/d3-axis@3.0.6':
+    resolution: {integrity: sha512-pYeijfZuBd87T0hGn0FO1vQ/cgLk6E1ALJjfkC0oJ8cbwkZl3TpgS8bVBLZN+2jjGgg38epgxb2zmoGtSfvgMw==}
+
+  '@types/d3-brush@3.0.6':
+    resolution: {integrity: sha512-nH60IZNNxEcrh6L1ZSMNA28rj27ut/2ZmI3r96Zd+1jrZD++zD3LsMIjWlvg4AYrHn/Pqz4CF3veCxGjtbqt7A==}
+
+  '@types/d3-chord@3.0.6':
+    resolution: {integrity: sha512-LFYWWd8nwfwEmTZG9PfQxd17HbNPksHBiJHaKuY1XeqscXacsS2tyoo6OdRsjf+NQYeB6XrNL3a25E3gH69lcg==}
+
   '@types/d3-color@3.1.3':
     resolution: {integrity: sha512-iO90scth9WAbmgv7ogoq57O9YpKmFBbmoEoCHDB2xMBY0+/KVrqAaCDyCE16dUspeOvIxFFRI+0sEtqDqy2b4A==}
 
+  '@types/d3-contour@3.0.6':
+    resolution: {integrity: sha512-BjzLgXGnCWjUSYGfH1cpdo41/hgdWETu4YxpezoztawmqsvCeep+8QGfiY6YbDvfgHz/DkjeIkkZVJavB4a3rg==}
+
+  '@types/d3-delaunay@6.0.4':
+    resolution: {integrity: sha512-ZMaSKu4THYCU6sV64Lhg6qjf1orxBthaC161plr5KuPHo3CNm8DTHiLw/5Eq2b6TsNP0W0iJrUOFscY6Q450Hw==}
+
+  '@types/d3-dispatch@3.0.7':
+    resolution: {integrity: sha512-5o9OIAdKkhN1QItV2oqaE5KMIiXAvDWBDPrD85e58Qlz1c1kI/J0NcqbEG88CoTwJrYe7ntUCVfeUl2UJKbWgA==}
+
+  '@types/d3-drag@3.0.7':
+    resolution: {integrity: sha512-HE3jVKlzU9AaMazNufooRJ5ZpWmLIoc90A37WU2JMmeq28w1FQqCZswHZ3xR+SuxYftzHq6WU6KJHvqxKzTxxQ==}
+
+  '@types/d3-dsv@3.0.7':
+    resolution: {integrity: sha512-n6QBF9/+XASqcKK6waudgL0pf/S5XHPPI8APyMLLUHd8NqouBGLsU8MgtO7NINGtPBtk9Kko/W4ea0oAspwh9g==}
+
   '@types/d3-ease@3.0.2':
     resolution: {integrity: sha512-NcV1JjO5oDzoK26oMzbILE6HW7uVXOHLQvHshBUW4UMdZGfiY6v5BeQwh9a9tCzv+CeefZQHJt5SRgK154RtiA==}
 
+  '@types/d3-fetch@3.0.7':
+    resolution: {integrity: sha512-fTAfNmxSb9SOWNB9IoG5c8Hg6R+AzUHDRlsXsDZsNp6sxAEOP0tkP3gKkNSO/qmHPoBFTxNrjDprVHDQDvo5aA==}
+
+  '@types/d3-force@3.0.10':
+    resolution: {integrity: sha512-ZYeSaCF3p73RdOKcjj+swRlZfnYpK1EbaDiYICEEp5Q6sUiqFaFQ9qgoshp5CzIyyb/yD09kD9o2zEltCexlgw==}
+
+  '@types/d3-format@3.0.4':
+    resolution: {integrity: sha512-fALi2aI6shfg7vM5KiR1wNJnZ7r6UuggVqtDA+xiEdPZQwy/trcQaHnwShLuLdta2rTymCNpxYTiMZX/e09F4g==}
+
+  '@types/d3-geo@3.1.0':
+    resolution: {integrity: sha512-856sckF0oP/diXtS4jNsiQw/UuK5fQG8l/a9VVLeSouf1/PPbBE1i1W852zVwKwYCBkFJJB7nCFTbk6UMEXBOQ==}
+
+  '@types/d3-hierarchy@3.1.7':
+    resolution: {integrity: sha512-tJFtNoYBtRtkNysX1Xq4sxtjK8YgoWUNpIiUee0/jHGRwqvzYxkq0hGVbbOGSz+JgFxxRu4K8nb3YpG3CMARtg==}
+
   '@types/d3-interpolate@3.0.4':
     resolution: {integrity: sha512-mgLPETlrpVV1YRJIglr4Ez47g7Yxjl1lj7YKsiMCb27VJH9W8NVM6Bb9d8kkpG/uAQS5AmbA48q2IAolKKo1MA==}
 
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+  '@types/d3-polygon@3.0.2':
+    resolution: {integrity: sha512-ZuWOtMaHCkN9xoeEMr1ubW2nGWsp4nIql+OPQRstu4ypeZ+zk3YKqQT0CXVe/PYqrKpZAi+J9mTs05TKwjXSRA==}
+
+  '@types/d3-quadtree@3.0.6':
+    resolution: {integrity: sha512-oUzyO1/Zm6rsxKRHA1vH0NEDG58HrT5icx/azi9MF1TWdtttWl0UIUsjEQBBh+SIkrpd21ZjEv7ptxWys1ncsg==}
+
+  '@types/d3-random@3.0.3':
+    resolution: {integrity: sha512-Imagg1vJ3y76Y2ea0871wpabqp613+8/r0mCLEBfdtqC7xMSfj9idOnmBYyMoULfHePJyxMAw3nWhJxzc+LFwQ==}
+
+  '@types/d3-scale-chromatic@3.1.0':
+    resolution: {integrity: sha512-iWMJgwkK7yTRmWqRB5plb1kadXyQ5Sj8V/zYlFGMUBbIPKQScw+Dku9cAAMgJG+z5GYDoMjWGLVOvjghDEFnKQ==}
+
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     resolution: {integrity: sha512-dLmtwB8zkAeO/juAMfnV+sItKjlsw2lKdZVVy6LRr0cBmegxSABiLEpGVmSJJ8O08i4+sGR6qQtb6WtuwJdvVw==}
 
+  '@types/d3-selection@3.0.11':
+    resolution: {integrity: sha512-bhAXu23DJWsrI45xafYpkQ4NtcKMwWnAC/vKrd2l+nxMFuvOT3XMYTIj2opv8vq8AO5Yh7Qac/nSeP/3zjTK0w==}
+
   '@types/d3-shape@3.1.8':
     resolution: {integrity: sha512-lae0iWfcDeR7qt7rA88BNiqdvPS5pFVPpo5OfjElwNaT2yyekbM0C9vK+yqBqEmHr6lDkRnYNoTBYlAgJa7a4w==}
 
+  '@types/d3-time-format@4.0.3':
+    resolution: {integrity: sha512-5xg9rC+wWL8kdDj153qZcsJ0FWiFt0J5RB6LYUNZjwSnesfblqrI/bJ1wBdJ8OQfncgbJG5+2F+qfqnqyzYxyg==}
+
   '@types/d3-time@3.0.4':
     resolution: {integrity: sha512-yuzZug1nkAAaBlBBikKZTgzCeA+k1uy4ZFwWANOfKw5z5LRhV0gNA7gNkKm7HoK+HRN0wX3EkxGk0fpbWhmB7g==}
 
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     resolution: {integrity: sha512-Ps3T8E8dZDam6fUyNiMkekK3XUsaUEik+idO9/YjPtfj2qruF8tFBXS7XhtE4iIXBLxhmLjP3SXpLhVf21I9Lw==}
 
+  '@types/d3-transition@3.0.9':
+    resolution: {integrity: sha512-uZS5shfxzO3rGlu0cC3bjmMFKsXv+SmZZcgp0KD22ts4uGXp5EVYGzu/0YdwZeKmddhcAccYtREJKkPfXkZuCg==}
+
+  '@types/d3-zoom@3.0.8':
+    resolution: {integrity: sha512-iqMC4/YlFCSlO8+2Ii1GGGliCAY4XdeG748w5vQUbevlbDu0zSjH/+jojorQVBK/se0j6DUFNPBGSqD3YWYnDw==}
+
+  '@types/d3@7.4.3':
+    resolution: {integrity: sha512-lZXZ9ckh5R8uiFVt8ogUNf+pIrK4EsWrx2Np75WvF/eTpJ0FMHNhjXk8CKEx/+gpHbNQyJWehbFaTvqmHWB3ww==}
+
   '@types/estree@1.0.8':
     resolution: {integrity: sha512-dWHzHa2WqEXI/O1E9OjrocMTKJl2mSrEolh1Iomrv6U+JuNwaHXsXx9bLu5gG7BUWFIN0skIQJQ/L1rIex4X6w==}
 
+  '@types/geojson@7946.0.16':
+    resolution: {integrity: sha512-6C8nqWur3j98U6+lXDfTUWIfgvZU+EumvpHKcYjujKH7woYyLj2sUmff0tRhrqM7BohUw7Pz3ZB1jj2gW9Fvmg==}
+
   '@types/json-schema@7.0.15':
     resolution: {integrity: sha512-5+fP8P8MFNC+AyZCDxrB2pkZFPGzqQWUzpSeuuVLvm8VMcorNYavBqoFcxK8bQz4Qsbn4oUEEem4wDLfcysGHA==}
 
@@ -1971,6 +2079,9 @@ packages:
   class-variance-authority@0.7.1:
     resolution: {integrity: sha512-Ka+9Trutv7G8M6WT6SeiRWz792K5qEqIGEGzXKhAE6xOWAY6pPH8U+9IY3oCMv6kqTmLsv7Xh/2w2RigkePMsg==}
 
+  classcat@5.0.5:
+    resolution: {integrity: sha512-JhZUT7JFcQy/EzW605k/ktHtncoo9vnyW/2GspNYwFlN1C/WmjuV/xtS04e9SOkL2sTdw0VAZ2UGCcQ9lR6p6w==}
+
   cli-cursor@5.0.0:
     resolution: {integrity: sha512-aCj4O5wKyszjMmDT4tZj93kxyydN/K5zPWSCe6/0AV/AA1pqe5ZBIw0a2ZfPQV7lL5/yb5HsUreJ6UFAF1tEQw==}
     engines: {node: '>=18'}
@@ -2071,6 +2182,14 @@ packages:
     resolution: {integrity: sha512-zg/chbXyeBtMQ1LbD/WSoW2DpC3I0mpmPdW+ynRTj/x2DAWYrIY7qeZIHidozwV24m4iavr15lNwIwLxRmOxhA==}
     engines: {node: '>=12'}
 
+  d3-dispatch@3.0.1:
+    resolution: {integrity: sha512-rzUyPU/S7rwUflMyLc1ETDeBj0NRuHKKAcvukozwhshr6g6c5d8zh4c2gQjY2bZ0dXeGLWc1PF174P2tVvKhfg==}
+    engines: {node: '>=12'}
+
+  d3-drag@3.0.0:
+    resolution: {integrity: sha512-pWbUJLdETVA8lQNJecMxoXfH6x+mO2UQo8rSmZ+QqxcbyA3hfeprFgIT//HW2nlHChWeIIMwS2Fq+gEARkhTkg==}
+    engines: {node: '>=12'}
+
   d3-ease@3.0.1:
     resolution: {integrity: sha512-wR/XK3D3XcLIZwpbvQwQ5fK+8Ykds1ip7A2Txe0yxncXSdq1L9skcG7blcedkOX+ZcgxGAmLX1FrRGbADwzi0w==}
     engines: {node: '>=12'}
@@ -2091,6 +2210,10 @@ packages:
     resolution: {integrity: sha512-GZW464g1SH7ag3Y7hXjf8RoUuAFIqklOAq3MRl4OaWabTFJY9PN/E1YklhXLh+OQ3fM9yS2nOkCoS+WLZ6kvxQ==}
     engines: {node: '>=12'}
 
+  d3-selection@3.0.0:
+    resolution: {integrity: sha512-fmTRWbNMmsmWq6xJV8D19U/gw/bwrHfNXxrIN+HfZgnzqTHp9jOmKMhsTUjXOJnZOdZY9Q28y4yebKzqDKlxlQ==}
+    engines: {node: '>=12'}
+
   d3-shape@3.2.0:
     resolution: {integrity: sha512-SaLBuwGm3MOViRq2ABk3eLoxwZELpH6zhl3FbAoJ7Vm1gofKx6El1Ib5z23NUEhF9AsGl7y+dzLe5Cw2AArGTA==}
     engines: {node: '>=12'}
@@ -2107,6 +2230,16 @@ packages:
     resolution: {integrity: sha512-ndfJ/JxxMd3nw31uyKoY2naivF+r29V+Lc0svZxe1JvvIRmi8hUsrMvdOwgS1o6uBHmiz91geQ0ylPP0aj1VUA==}
     engines: {node: '>=12'}
 
+  d3-transition@3.0.1:
+    resolution: {integrity: sha512-ApKvfjsSR6tg06xrL434C0WydLr7JewBB3V+/39RMHsaXTOG0zmt/OAXeng5M5LBm0ojmxJrpomQVZ1aPvBL4w==}
+    engines: {node: '>=12'}
+    peerDependencies:
+      d3-selection: 2 - 3
+
+  d3-zoom@3.0.0:
+    resolution: {integrity: sha512-b8AmV3kfQaqWAuacbPuNbL6vahnOJflOhexLzMMNLga62+/nh0JzvJ0aO/5a5MVgUFGS7Hu1P9P03o3fJkDCyw==}
+    engines: {node: '>=12'}
+
   damerau-levenshtein@1.0.8:
     resolution: {integrity: sha512-sdQSFB7+llfUcQHUQO3+B8ERRj0Oa4w9POWMI/puGtuf7gFywGmkaLCElnudfTiKZV+NvHqL0ifzdrI8Ro7ESA==}
 
@@ -3517,6 +3650,12 @@ packages:
     resolution: {integrity: sha512-Ku/hhYbVjOQnXDZFv2+RibmLFGwFdeeKHFcOTlrt7xplBnya5OGn/hIRDsqDiSUcfORsDC7MPxwork8jBwsIWA==}
     engines: {node: '>=0.10.0'}
 
+  reactflow@11.11.4:
+    resolution: {integrity: sha512-70FOtJkUWH3BAOsN+LU9lCrKoKbtOPnz2uq0CV2PLdNSwxTXOhCbsZr50GmZ+Rtw3jx8Uv7/vBFtCGixLfd4Og==}
+    peerDependencies:
+      react: '>=17'
+      react-dom: '>=17'
+
   recast@0.23.11:
     resolution: {integrity: sha512-YTUo+Flmw4ZXiWfQKGcwwc11KnoRAYgzAE2E7mXKCjSviTKShtxBsN6YUUBB2gtaBzKzeKunxhUwNHQuRryhWA==}
     engines: {node: '>= 4'}
@@ -4067,6 +4206,21 @@ packages:
   zod@4.3.6:
     resolution: {integrity: sha512-rftlrkhHZOcjDwkGlnUtZZkvaPHCsDATp4pGpuOOMDaTdDDXF91wuVDJoWoPsKX/3YPQ5fHuF3STjcYyKr+Qhg==}
 
+  zustand@4.5.7:
+    resolution: {integrity: sha512-CHOUy7mu3lbD6o6LJLfllpjkzhHXSBlX8B9+qPddUsIfeF5S/UZ5q0kmCsnRqT1UHFQZchNFDDzMbQsuesHWlw==}
+    engines: {node: '>=12.7.0'}
+    peerDependencies:
+      '@types/react': '>=16.8'
+      immer: '>=9.0.6'
+      react: '>=16.8'
+    peerDependenciesMeta:
+      '@types/react':
+        optional: true
+      immer:
+        optional: true
+      react:
+        optional: true
+
 snapshots:
 
   '@alloc/quick-lru@5.2.0': {}
@@ -5431,6 +5585,84 @@ snapshots:
 
   '@radix-ui/rect@1.1.1': {}
 
+  '@reactflow/background@11.3.14(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)':
+    dependencies:
+      '@reactflow/core': 11.11.4(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)
+      classcat: 5.0.5
+      react: 19.2.3
+      react-dom: 19.2.3(react@19.2.3)
+      zustand: 4.5.7(@types/react@19.2.9)(immer@11.1.3)(react@19.2.3)
+    transitivePeerDependencies:
+      - '@types/react'
+      - immer
+
+  '@reactflow/controls@11.2.14(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)':
+    dependencies:
+      '@reactflow/core': 11.11.4(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)
+      classcat: 5.0.5
+      react: 19.2.3
+      react-dom: 19.2.3(react@19.2.3)
+      zustand: 4.5.7(@types/react@19.2.9)(immer@11.1.3)(react@19.2.3)
+    transitivePeerDependencies:
+      - '@types/react'
+      - immer
+
+  '@reactflow/core@11.11.4(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)':
+    dependencies:
+      '@types/d3': 7.4.3
+      '@types/d3-drag': 3.0.7
+      '@types/d3-selection': 3.0.11
+      '@types/d3-zoom': 3.0.8
+      classcat: 5.0.5
+      d3-drag: 3.0.0
+      d3-selection: 3.0.0
+      d3-zoom: 3.0.0
+      react: 19.2.3
+      react-dom: 19.2.3(react@19.2.3)
+      zustand: 4.5.7(@types/react@19.2.9)(immer@11.1.3)(react@19.2.3)
+    transitivePeerDependencies:
+      - '@types/react'
+      - immer
+
+  '@reactflow/minimap@11.7.14(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)':
+    dependencies:
+      '@reactflow/core': 11.11.4(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)
+      '@types/d3-selection': 3.0.11
+      '@types/d3-zoom': 3.0.8
+      classcat: 5.0.5
+      d3-selection: 3.0.0
+      d3-zoom: 3.0.0
+      react: 19.2.3
+      react-dom: 19.2.3(react@19.2.3)
+      zustand: 4.5.7(@types/react@19.2.9)(immer@11.1.3)(react@19.2.3)
+    transitivePeerDependencies:
+      - '@types/react'
+      - immer
+
+  '@reactflow/node-resizer@2.2.14(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)':
+    dependencies:
+      '@reactflow/core': 11.11.4(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)
+      classcat: 5.0.5
+      d3-drag: 3.0.0
+      d3-selection: 3.0.0
+      react: 19.2.3
+      react-dom: 19.2.3(react@19.2.3)
+      zustand: 4.5.7(@types/react@19.2.9)(immer@11.1.3)(react@19.2.3)
+    transitivePeerDependencies:
+      - '@types/react'
+      - immer
+
+  '@reactflow/node-toolbar@1.3.14(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)':
+    dependencies:
+      '@reactflow/core': 11.11.4(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)
+      classcat: 5.0.5
+      react: 19.2.3
+      react-dom: 19.2.3(react@19.2.3)
+      zustand: 4.5.7(@types/react@19.2.9)(immer@11.1.3)(react@19.2.3)
+    transitivePeerDependencies:
+      - '@types/react'
+      - immer
+
   '@reduxjs/toolkit@2.11.2(react-redux@9.2.0(@types/react@19.2.9)(react@19.2.3)(redux@5.0.1))(react@19.2.3)':
     dependencies:
       '@standard-schema/spec': 1.1.0
@@ -5539,30 +5771,125 @@ snapshots:
 
   '@types/d3-array@3.2.2': {}
 
+  '@types/d3-axis@3.0.6':
+    dependencies:
+      '@types/d3-selection': 3.0.11
+
+  '@types/d3-brush@3.0.6':
+    dependencies:
+      '@types/d3-selection': 3.0.11
+
+  '@types/d3-chord@3.0.6': {}
+
   '@types/d3-color@3.1.3': {}
 
+  '@types/d3-contour@3.0.6':
+    dependencies:
+      '@types/d3-array': 3.2.2
+      '@types/geojson': 7946.0.16
+
+  '@types/d3-delaunay@6.0.4': {}
+
+  '@types/d3-dispatch@3.0.7': {}
+
+  '@types/d3-drag@3.0.7':
+    dependencies:
+      '@types/d3-selection': 3.0.11
+
+  '@types/d3-dsv@3.0.7': {}
+
   '@types/d3-ease@3.0.2': {}
 
+  '@types/d3-fetch@3.0.7':
+    dependencies:
+      '@types/d3-dsv': 3.0.7
+
+  '@types/d3-force@3.0.10': {}
+
+  '@types/d3-format@3.0.4': {}
+
+  '@types/d3-geo@3.1.0':
+    dependencies:
+      '@types/geojson': 7946.0.16
+
+  '@types/d3-hierarchy@3.1.7': {}
+
   '@types/d3-interpolate@3.0.4':
     dependencies:
       '@types/d3-color': 3.1.3
 
   '@types/d3-path@3.1.1': {}
 
+  '@types/d3-polygon@3.0.2': {}
+
+  '@types/d3-quadtree@3.0.6': {}
+
+  '@types/d3-random@3.0.3': {}
+
+  '@types/d3-scale-chromatic@3.1.0': {}
+
   '@types/d3-scale@4.0.9':
     dependencies:
       '@types/d3-time': 3.0.4
 
+  '@types/d3-selection@3.0.11': {}
+
   '@types/d3-shape@3.1.8':
     dependencies:
       '@types/d3-path': 3.1.1
 
+  '@types/d3-time-format@4.0.3': {}
+
   '@types/d3-time@3.0.4': {}
 
   '@types/d3-timer@3.0.2': {}
 
+  '@types/d3-transition@3.0.9':
+    dependencies:
+      '@types/d3-selection': 3.0.11
+
+  '@types/d3-zoom@3.0.8':
+    dependencies:
+      '@types/d3-interpolate': 3.0.4
+      '@types/d3-selection': 3.0.11
+
+  '@types/d3@7.4.3':
+    dependencies:
+      '@types/d3-array': 3.2.2
+      '@types/d3-axis': 3.0.6
+      '@types/d3-brush': 3.0.6
+      '@types/d3-chord': 3.0.6
+      '@types/d3-color': 3.1.3
+      '@types/d3-contour': 3.0.6
+      '@types/d3-delaunay': 6.0.4
+      '@types/d3-dispatch': 3.0.7
+      '@types/d3-drag': 3.0.7
+      '@types/d3-dsv': 3.0.7
+      '@types/d3-ease': 3.0.2
+      '@types/d3-fetch': 3.0.7
+      '@types/d3-force': 3.0.10
+      '@types/d3-format': 3.0.4
+      '@types/d3-geo': 3.1.0
+      '@types/d3-hierarchy': 3.1.7
+      '@types/d3-interpolate': 3.0.4
+      '@types/d3-path': 3.1.1
+      '@types/d3-polygon': 3.0.2
+      '@types/d3-quadtree': 3.0.6
+      '@types/d3-random': 3.0.3
+      '@types/d3-scale': 4.0.9
+      '@types/d3-scale-chromatic': 3.1.0
+      '@types/d3-selection': 3.0.11
+      '@types/d3-shape': 3.1.8
+      '@types/d3-time': 3.0.4
+      '@types/d3-time-format': 4.0.3
+      '@types/d3-timer': 3.0.2
+      '@types/d3-transition': 3.0.9
+      '@types/d3-zoom': 3.0.8
+
   '@types/estree@1.0.8': {}
 
+  '@types/geojson@7946.0.16': {}
+
   '@types/json-schema@7.0.15': {}
 
   '@types/json5@0.0.29': {}
@@ -5944,6 +6271,8 @@ snapshots:
     dependencies:
       clsx: 2.1.1
 
+  classcat@5.0.5: {}
+
   cli-cursor@5.0.0:
     dependencies:
       restore-cursor: 5.1.0
@@ -6018,6 +6347,13 @@ snapshots:
 
   d3-color@3.1.0: {}
 
+  d3-dispatch@3.0.1: {}
+
+  d3-drag@3.0.0:
+    dependencies:
+      d3-dispatch: 3.0.1
+      d3-selection: 3.0.0
+
   d3-ease@3.0.1: {}
 
   d3-format@3.1.2: {}
@@ -6036,6 +6372,8 @@ snapshots:
       d3-time: 3.1.0
       d3-time-format: 4.1.0
 
+  d3-selection@3.0.0: {}
+
   d3-shape@3.2.0:
     dependencies:
       d3-path: 3.1.0
@@ -6050,6 +6388,23 @@ snapshots:
 
   d3-timer@3.0.1: {}
 
+  d3-transition@3.0.1(d3-selection@3.0.0):
+    dependencies:
+      d3-color: 3.1.0
+      d3-dispatch: 3.0.1
+      d3-ease: 3.0.1
+      d3-interpolate: 3.0.1
+      d3-selection: 3.0.0
+      d3-timer: 3.0.1
+
+  d3-zoom@3.0.0:
+    dependencies:
+      d3-dispatch: 3.0.1
+      d3-drag: 3.0.0
+      d3-interpolate: 3.0.1
+      d3-selection: 3.0.0
+      d3-transition: 3.0.1(d3-selection@3.0.0)
+
   damerau-levenshtein@1.0.8: {}
 
   data-uri-to-buffer@4.0.1: {}
@@ -7580,6 +7935,20 @@ snapshots:
 
   react@19.2.3: {}
 
+  reactflow@11.11.4(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3):
+    dependencies:
+      '@reactflow/background': 11.3.14(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)
+      '@reactflow/controls': 11.2.14(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)
+      '@reactflow/core': 11.11.4(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)
+      '@reactflow/minimap': 11.7.14(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)
+      '@reactflow/node-resizer': 2.2.14(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)
+      '@reactflow/node-toolbar': 1.3.14(@types/react@19.2.9)(immer@11.1.3)(react-dom@19.2.3(react@19.2.3))(react@19.2.3)
+      react: 19.2.3
+      react-dom: 19.2.3(react@19.2.3)
+    transitivePeerDependencies:
+      - '@types/react'
+      - immer
+
   recast@0.23.11:
     dependencies:
       ast-types: 0.16.1
@@ -8306,3 +8675,11 @@ snapshots:
   zod@3.25.76: {}
 
   zod@4.3.6: {}
+
+  zustand@4.5.7(@types/react@19.2.9)(immer@11.1.3)(react@19.2.3):
+    dependencies:
+      use-sync-external-store: 1.6.0(react@19.2.3)
+    optionalDependencies:
+      '@types/react': 19.2.9
+      immer: 11.1.3
+      react: 19.2.3