Feature/biodocklab v2 fullstack start

.github/workflows/biodocklab-ci.yml

@@ -0,0 +1,27 @@
+name: BioDockLab CI
+
+on:
+  push:
+    branches:
+      - main
+      - feature/biodocklab-v2-fullstack-start
+  pull_request:
+
+jobs:
+  validate-node:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: 22
+
+      - name: Validate bio experiment data
+        run: node scripts/validate_bio_data.js
+
+      - name: Generate sample report
+        run: node scripts/generate_report_node.js

Dockerfile

@@ -0,0 +1,9 @@
+FROM node:22-alpine
+
+WORKDIR /app
+
+COPY . .
+
+EXPOSE 5173
+
+CMD ["node", "scripts/biodocklab_node_server.js"]

ai/experiment_analyzer.py

@@ -0,0 +1,42 @@
+import json
+from pathlib import Path
+
+
+DATA_PATH = Path("../data/sample/bio_experiments.json")
+
+
+def load_experiments():
+    with DATA_PATH.open("r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def classify_priority(exp):
+    success = exp.get("success_rate", 0)
+    risk = exp.get("risk_level", "Medium")
+
+    if success >= 80 and risk == "Low":
+        return "High Priority"
+    if success >= 70:
+        return "Review"
+    return "Needs Improvement"
+
+
+def analyze():
+    experiments = load_experiments()
+
+    results = []
+    for exp in experiments:
+        results.append({
+            "id": exp["id"],
+            "domain": exp["domain"],
+            "success_rate": exp["success_rate"],
+            "risk_level": exp["risk_level"],
+            "priority": classify_priority(exp)
+        })
+
+    return results
+
+
+if __name__ == "__main__":
+    for item in analyze():
+        print(item)

ai/models/torch_success_predictor.py

@@ -0,0 +1,38 @@
+"""
+BioDockLab PyTorch scaffold.
+
+Goal:
+Predict experiment success probability from structured bio experiment features.
+This file is a model skeleton and will be implemented after Python environment setup.
+"""
+
+try:
+    import torch
+    import torch.nn as nn
+except ImportError:
+    torch = None
+    nn = None
+
+
+if torch and nn:
+    class ExperimentSuccessModel(nn.Module):
+        def __init__(self, input_dim: int = 4):
+            super().__init__()
+            self.network = nn.Sequential(
+                nn.Linear(input_dim, 16),
+                nn.ReLU(),
+                nn.Linear(16, 8),
+                nn.ReLU(),
+                nn.Linear(8, 1),
+                nn.Sigmoid()
+            )
+
+        def forward(self, x):
+            return self.network(x)
+else:
+    class ExperimentSuccessModel:
+        def __init__(self, input_dim: int = 4):
+            self.input_dim = input_dim
+
+        def explain(self):
+            return "PyTorch is not installed. This is a scaffold model."