Merge pull request #1 from VishwamAI/neurocoder-development

Initial Implementation of NeuroCoder Features

Author: kasinadhsarma

.github/workflows/ci-cd.yml

@@ -0,0 +1,44 @@
+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: Set PYTHONPATH
+      run: |
+        echo "PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/src" >> $GITHUB_ENV
+
+  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

.gitignore

@@ -0,0 +1,14 @@
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+.vscode/
+*.log
+*.sqlite3
+.DS_Store

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Install system dependencies and Python packages
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    libffi-dev \
+    && rm -rf /var/lib/apt/lists/* \
+    && pip install --no-cache-dir -r requirements.txt
+
+# Make port 80 available to the world outside this container
+EXPOSE 80
+
+# Define environment variable
+ENV NAME NeuroCoder
+
+# Run NeuroCoder when the container launches
+CMD ["python", "src/models/model_training.py"]

README.md

@@ -1 +1,33 @@
-# NeuroCoder
+# NeuroCoder
+
+## Future Enhancements for NeuroCoder
+
+### Expand Language Support
+- Continuously add support for more programming languages and frameworks to broaden the applicability of NeuroCoder.
+
+### Open-Source Collaboration
+- Engage with the open-source community to gather contributions and stay updated with the latest advancements in AI and software development.
+
+### Cross-Platform Support
+- Ensure compatibility with various development environments, including cloud-based and on-premises solutions, to provide flexibility and scalability.
+
+### Advanced Visualization Tools
+- Develop advanced visualization tools to provide insights into the decision-making process, code dependencies, and optimization pathways.
+
+### Enhanced Feedback Mechanisms
+- Implement more sophisticated feedback mechanisms to improve the learning and adaptation capabilities of NeuroCoder.
+
+### Integration with CI/CD Pipelines
+- Facilitate seamless integration with CI/CD pipelines to automate testing and deployment processes.
+
+### Security and Privacy Enhancements
+- Strengthen security and privacy measures to protect sensitive data and ensure compliance with industry standards.
+
+### Continuous Learning and Adaptation
+- Enhance the continuous learning mechanism to adapt to new programming languages, paradigms, and user feedback more efficiently.
+
+### Quantum Computing Integration
+- Explore the integration of quantum computing capabilities to solve complex problems more efficiently.
+
+### User-Friendly Interface
+- Develop a user-friendly interface to enhance the user experience and make NeuroCoder accessible to a wider audience.

models/neurocoder_model.pth

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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

requirements.txt

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+# Create a FastAPI application for exposing NeuroCoder functionalities as APIs
+from fastapi import FastAPI, HTTPException, WebSocket
+from pydantic import BaseModel
+from typing import List, Dict
+import torch
+from src.models.advanced_architecture import AdvancedNeuroCoder
+from src.models.benchmarking import run_benchmarks, compare_to_industry_standards
+
+app = FastAPI()
+
+# Define request and response models
+class CodeRequest(BaseModel):
+    input_ids: List[int]
+    attention_mask: List[int]
+    task: str
+
+class CodeResponse(BaseModel):
+    output: List[int]
+
+class FeedbackRequest(BaseModel):
+    code_id: str
+    rating: int
+    comments: str
+
+# Load the trained model
+model = AdvancedNeuroCoder(vocab_size=10000)
+model.load_state_dict(torch.load("neurocoder_model.pth"))
+model.eval()
+
+@app.post("/generate-code", response_model=CodeResponse)
+async def generate_code(request: CodeRequest):
+    input_ids = torch.tensor(request.input_ids).unsqueeze(0)
+    attention_mask = torch.tensor(request.attention_mask).unsqueeze(0)
+    task = request.task
+
+    with torch.no_grad():
+        output = model(input_ids, attention_mask)
+
+    return CodeResponse(output=output.squeeze(0).tolist())
+
+@app.post("/feedback")
+async def submit_feedback(feedback: FeedbackRequest):
+    # TODO: Implement feedback processing and model updating
+    return {"message": "Feedback received", "status": "success"}
+
+@app.get("/benchmarks")
+async def get_benchmarks():
+    benchmark_results = run_benchmarks(model, {"batch_size": 32})
+    comparisons = compare_to_industry_standards(benchmark_results)
+    return {"benchmarks": benchmark_results, "comparisons": comparisons}
+
+@app.websocket("/interactive")
+async def interactive_session(websocket: WebSocket):
+    await websocket.accept()
+    while True:
+        data = await websocket.receive_text()
+        # Process the received data and generate a response
+        input_ids = torch.tensor([model.tokenizer.encode(data)])
+        attention_mask = torch.ones_like(input_ids)
+        with torch.no_grad():
+            output = model(input_ids, attention_mask)
+        response = model.tokenizer.decode(output[0])
+        await websocket.send_text(response)
+
+@app.get("/")
+async def root():
+    return {"message": "Welcome to NeuroCoder API"}