Add CI/CD pipeline configuration, requirements, and test files

.github/workflows/ci-cd.yml

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+name: NeuroCoder CI/CD Pipeline
+
+on:
+  push:
+    branches:
+      - neurocoder-development
+  pull_request:
+    branches:
+      - neurocoder-development
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+
+    - name: Run tests
+      run: |
+        pytest
+
+  deploy:
+    runs-on: ubuntu-latest
+    needs: build
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v2
+
+    - name: Deploy to Docker
+      run: |
+        docker build -t neurocoder .
+        docker run -d -p 80:80 neurocoder

requirements.txt

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+# NeuroCoder Project Dependencies
+
+# FastAPI for creating APIs
+fastapi==0.78.0
+
+# PyTorch for model development
+torch==1.12.1
+
+# Transformers for advanced architecture
+transformers==4.21.1
+
+# pytest for testing
+pytest==7.1.2
+
+# Docker for containerization
+docker==5.0.3
+
+# Additional dependencies
+requests==2.28.1
+pydantic==1.9.1

tests/test_integration.py

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+import pytest
+import torch
+from src.models.model_training import AdvancedNeuroCoder
+from src.api import app
+from fastapi.testclient import TestClient
+
+@pytest.fixture
+def client():
+    return TestClient(app)
+
+@pytest.fixture
+def model():
+    return AdvancedNeuroCoder(vocab_size=10000)
+
+def test_api_generate_code(client, model):
+    input_data = {
+        "input_ids": [1, 2, 3, 4],
+        "attention_mask": [1, 1, 1, 1],
+        "task": "generate"
+    }
+    response = client.post("/generate-code", json=input_data)
+    assert response.status_code == 200
+    assert "output" in response.json()
+
+def test_api_feedback(client):
+    feedback_data = {
+        "code_id": "1234",
+        "rating": 5,
+        "comments": "Great code generation!"
+    }
+    response = client.post("/feedback", json=feedback_data)
+    assert response.status_code == 200
+    assert response.json()["status"] == "success"
+
+def test_api_benchmarks(client):
+    response = client.get("/benchmarks")
+    assert response.status_code == 200
+    assert "benchmarks" in response.json()
+    assert "comparisons" in response.json()

tests/test_model_training.py

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+import pytest
+import torch
+from src.models.advanced_architecture import AdvancedNeuroCoder
+
+@pytest.fixture
+def model():
+    return AdvancedNeuroCoder(vocab_size=10000)
+
+def test_model_initialization(model):
+    assert model is not None
+    assert model.embedding.num_embeddings == 10000
+
+def test_forward_pass(model):
+    input_ids = torch.tensor([[1, 2, 3, 4]])
+    attention_mask = torch.tensor([[1, 1, 1, 1]])
+    output = model(input_ids, attention_mask)
+    assert output is not None
+    assert output.shape == (1, 4, 10000)  # (batch_size, sequence_length, vocab_size)
+
+def test_model_output_range(model):
+    input_ids = torch.tensor([[1, 2, 3, 4]])
+    attention_mask = torch.tensor([[1, 1, 1, 1]])
+    output = model(input_ids, attention_mask)
+    assert torch.all(output >= 0) and torch.all(output <= 1)  # Assuming output is probabilities
+
+def test_model_parameters(model):
+    total_params = sum(p.numel() for p in model.parameters())
+    assert total_params > 0, "Model should have parameters"
+
+def test_model_training_mode(model):
+    model.train()
+    assert model.training == True
+    model.eval()
+    assert model.training == False