Custom Chatbot Builder
Run ID: 69cb2bee61b1021a29a8688e•2026-03-31Development
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Custom Chatbot Builder: Step 2 - Code Generation (gemini → generate_code)

This document delivers the core, production-ready code for your custom chatbot, leveraging the power of Google's Gemini Pro model. This code provides the foundational backend logic, allowing your chatbot to process user input, interact with the Gemini API, and generate intelligent, context-aware responses.

1. Introduction to the Generated Code

This step focuses on generating the essential Python code that forms the brain of your chatbot. It's designed to be clean, modular, and easily extendable. We've chosen a Flask-based API structure for its simplicity and robustness, making it straightforward to integrate with various frontends (web, mobile, messaging platforms).

Key Features of the Generated Code:

2. Chatbot Architecture and Design Choices

The architecture for your custom chatbot is designed for scalability and ease of use:

text • 1,316 chars
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### 4. How to Use and Deploy

Follow these steps to set up and run your custom chatbot backend:

1.  **Save the Code**:
    *   Create a directory, e.g., `chatbot_backend`.
    *   Inside `chatbot_backend`, create `app.py`, `requirements.txt`, and `.env` with the content provided above.

2.  **Install Dependencies**:
    *   Open your terminal or command prompt.
    *   Navigate to the `chatbot_backend` directory.
    *   Run: `pip install -r requirements.txt`

3.  **Obtain Google Gemini API Key**:
    *   Go to the [Google AI Studio](https://aistudio.google.com/app/apikey).
    *   Generate a new API key.
    *   Copy this key.

4.  **Configure Environment Variables**:
    *   Open the `.env` file you created.
    *   Replace `"YOUR_GOOGLE_GEMINI_API_KEY_HERE"` with your actual API key.
    *   (Optional) You can also set a stronger `FLASK_SECRET_KEY` in `.env` for production: `FLASK_SECRET_KEY="your_strong_random_key_here"`

5.  **Run the Backend Server**:
    *   In your terminal, within the `chatbot_backend` directory, run: `python app.py`
    *   You should see output indicating that the Flask development server is running, typically on `http://0.0.0.0:5000`.

6.  **Test the API (using `curl` or Postman/Insomnia)**:
    *   Open another terminal.
    *   Send a test message:
Sandboxed live preview

Custom Chatbot Builder: Comprehensive Study Plan

This document outlines a detailed, professional study plan designed to equip you with the knowledge and skills necessary to build custom chatbots. This plan is structured to provide a thorough understanding of chatbot architecture, natural language processing (NLP), dialogue management, integration, and deployment.

1. Introduction and Overview

Welcome to the Custom Chatbot Builder Study Plan. This structured program serves as the foundational first step in developing your bespoke chatbot solution. Over the next 10 weeks, you will systematically acquire expertise in key areas of chatbot development, moving from fundamental concepts to advanced implementation techniques. Each week is designed to build upon previous knowledge, culminating in the ability to design, develop, and deploy a functional, intelligent chatbot.

2. Learning Objectives

Upon successful completion of this study plan, you will be able to:

  • Understand Chatbot Fundamentals: Differentiate between various chatbot types (rule-based, retrieval-based, generative AI) and their appropriate use cases.
  • Master Natural Language Processing (NLP) Basics: Comprehend core NLP concepts such as tokenization, stemming, lemmatization, part-of-speech tagging, and named entity recognition.
  • Implement Intent Recognition: Design and train models to accurately identify user intentions from natural language input.
  • Perform Entity Extraction: Develop methods to extract critical information (entities) from user utterances.
  • Design Effective Dialogue Management: Create robust dialogue flows, manage conversational state, and handle context switching for natural interactions.
  • Utilize Chatbot Frameworks: Gain proficiency in at least one major chatbot development framework (e.g., Rasa, Google Dialogflow, Microsoft Bot Framework).
  • Integrate with External Services: Connect chatbots to databases, APIs, and third-party services to retrieve and provide dynamic information.
  • Deploy and Monitor Chatbots: Understand the process of deploying chatbots to various platforms (web, messaging apps) and implement monitoring for performance and user feedback.
  • Address Ethical Considerations: Recognize and mitigate biases, ensure data privacy, and design chatbots for responsible and ethical interaction.
  • Build a Production-Ready Chatbot: Independently conceptualize, design, develop, test, and deploy a custom chatbot application.

3. Weekly Schedule

This 10-week schedule provides a structured pathway, with an estimated time commitment of 10-15 hours per week for focused study, coding, and project work.

Week 1: Introduction to Chatbots & NLP Fundamentals

  • Topics:

* What are Chatbots? Types and Use Cases.

* Chatbot Architecture Overview (NLU, Dialogue Management, Response Generation).

* Introduction to Python for NLP (if needed).

* Basic NLP Concepts: Tokenization, Stemming, Lemmatization, Stop Words.

* Text Preprocessing Techniques.

  • Activities: Set up development environment (Python, relevant libraries), complete introductory NLP exercises.

Week 2: Intent Recognition & Entity Extraction

  • Topics:

* Supervised Learning for Text Classification.

* Feature Engineering for Text (Bag-of-Words, TF-IDF).

* Introduction to Machine Learning Models for Intent Recognition (Naïve Bayes, SVM, Logistic Regression).

* Named Entity Recognition (NER) and its importance.

* Introduction to NLTK and spaCy libraries.

  • Activities: Build a simple intent classifier using NLTK/spaCy, practice extracting entities from sample sentences.

Week 3: Advanced NLP & Deep Learning for NLU

  • Topics:

* Word Embeddings (Word2Vec, GloVe, FastText).

* Introduction to Neural Networks for NLP (RNNs, LSTMs, GRUs).

* Transformer Architecture (Brief Overview).

* Pre-trained Language Models for NLU (BERT, GPT - conceptual understanding for NLU tasks).

  • Activities: Experiment with pre-trained word embeddings, understand how deep learning improves intent recognition.

Week 4: Dialogue Management & State Tracking

  • Topics:

* Dialogue State Tracking: Managing context and conversation flow.

* Rule-based Dialogue Management vs. ML-based Dialogue Management.

* Slot Filling and Form Actions.

* Handling disambiguation and clarifications.

* Error Handling and Fallback Mechanisms.

  • Activities: Design simple dialogue flows, implement state tracking for a multi-turn conversation.

Week 5: Chatbot Frameworks Deep Dive (Rasa or Google Dialogflow)

  • Topics:

* Introduction to chosen framework (e.g., Rasa Open Source Architecture: NLU, Core, Actions, Channels).

* Training Data Format and Best Practices.

* Building NLU models within the framework.

* Developing custom actions and responses.

* Connecting the framework components.

  • Activities: Set up the chosen framework, build a basic "hello world" chatbot, create initial NLU and dialogue models.

Week 6: Advanced Framework Features & Custom Actions

  • Topics:

* Advanced Dialogue Policies (e.g., Memoization, TEDPolicy in Rasa).

* Integrating custom logic and external APIs via actions.

* Handling complex conversational patterns (e.g., interruptions, clarifications).

* Testing and debugging framework-based chatbots.

  • Activities: Implement a custom action to fetch data from a mock API, refine dialogue policies for a more complex use case.

Week 7: Integration & Channel Connectors

  • Topics:

* Connecting chatbots to messaging platforms (e.g., Slack, Telegram, WhatsApp, Webhooks).

* Integrating with web applications (REST APIs, JavaScript widgets).

* Security considerations for API integrations.

* Database integration for dynamic content.

  • Activities: Connect your chatbot to at least one messaging channel or embed it in a simple web page.

Week 8: Deployment, Monitoring & Analytics

  • Topics:

* Deployment strategies (Docker, Kubernetes, Cloud platforms like AWS, GCP, Azure).

* Version control for chatbot projects.

* Logging, Monitoring (e.g., Prometheus, Grafana), and Alerting.

* Chatbot analytics: tracking user engagement, intent performance, conversation paths.

  • Activities: Deploy your chatbot prototype to a local Docker container or a free-tier cloud service, set up basic logging.

Week 9: Advanced Topics & Ethical AI

  • Topics:

* Contextual AI & Personalization.

* Multi-language Chatbots (brief overview).

* Voice integration (STT/TTS - conceptual).

* Ethical AI in Chatbots: Bias, Privacy, Transparency, Human Handoff.

* User Experience (UX) design for conversational interfaces.

  • Activities: Research and discuss ethical considerations for a specific chatbot use case, brainstorm UX improvements.

Week 10: Final Project: Build Your Custom Chatbot

  • Topics:

* Consolidate all learned skills.

* Design and implement a complete custom chatbot for a chosen use case.

* Focus on robust NLU, effective dialogue management, and seamless integration.

* Testing, refinement, and documentation.

  • Activities: Dedicated time to build, test, and refine your final custom chatbot project. Prepare for demonstration.

4. Recommended Resources

Books:

  • "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper (NLTK Book)
  • "Speech and Language Processing" by Daniel Jurafsky and James H. Martin
  • "Building Chatbots with Python" by Sumit Raj
  • "Rasa 2.x for Beginners" by Abhishek Kumar (or similar for chosen framework)

Online Courses & Tutorials:

  • Coursera/edX:

* "Natural Language Processing Specialization" (DeepLearning.AI)

* "Applied Text Mining in Python" (University of Michigan)

  • Udemy/Pluralsight:

* Courses on Python for Data Science and NLP.

* Specific courses on Rasa, Google Dialogflow, or Microsoft Bot Framework.

  • YouTube Channels:

* Sentdex (Python programming, ML)

* Rasa Official Channel (tutorials, webinars)

  • Blogs & Documentation:

* Rasa Docs: [https://rasa.com/docs/rasa/](https://rasa.com/docs/rasa/)

* Google Dialogflow Docs: [https://cloud.google.com/dialogflow/docs](https://cloud.google.com/dialogflow/docs)

* Microsoft Bot Framework Docs: [https://docs.microsoft.com/en-us/azure/bot-service/](https://docs.microsoft.com/en-us/azure/bot-service/)

* NLTK & spaCy Official Documentation.

* Towards Data Science (Medium publication for ML/NLP articles).

Tools & Libraries:

  • Python: Primary programming language.
  • NLTK (Natural Language Toolkit): For basic NLP tasks.
  • spaCy: For industrial-strength NLP, faster processing.
  • Scikit-learn: For traditional machine learning algorithms.
  • TensorFlow / PyTorch: For deep learning models (indirectly via frameworks).
  • Rasa Open Source / Google Dialogflow / Microsoft Bot Framework: Chosen chatbot framework.
  • Docker: For containerization and deployment.
  • Git / GitHub: For version control.

5. Milestones

Achieving these milestones will mark significant progress throughout your study journey:

  • Milestone 1 (End of Week 2): Successfully implement a basic text classifier for intent recognition and extract simple entities using NLTK/spaCy.
  • Milestone 2 (End of Week 4): Design and implement a multi-turn, rule-based dialogue flow that manages conversation state and handles basic slot filling.
  • Milestone 3 (End of Week 6): Build a functional chatbot prototype using your chosen framework (Rasa/Dialogflow), demonstrating core NLU, dialogue management, and at least one custom action integrating with a mock API.
  • Milestone 4 (End of Week 8): Integrate your chatbot prototype with a messaging channel (e.g., Slack, Telegram) or a simple web interface, and establish basic logging/monitoring.
  • Milestone 5 (End of Week 10 - Final Project): Deliver a fully functional custom chatbot for a defined use case, complete with robust NLU, advanced dialogue management, external integrations, deployment instructions, and a brief project report.

6. Assessment Strategies

To ensure comprehensive learning and skill development, the following assessment strategies will be employed:

  • Weekly Coding Challenges/Mini-Projects: Practical application of concepts learned each week, submitted for review.
  • Self-Assessment Quizzes: Short quizzes at the end of each major module to test theoretical understanding.
  • Peer Code Review (Optional but Recommended): Engage with fellow learners to review code, provide feedback, and learn from different approaches.
  • Milestone Project Submissions: Each milestone deliverable will be evaluated based on functionality, code quality, adherence to requirements, and documentation.
  • Final Project Demonstration & Review: A comprehensive evaluation of the custom chatbot's functionality, architecture, code, and presentation. This will include a live demonstration and a discussion of design choices and challenges.
  • Portfolio Building: Encourage maintaining a GitHub repository for all coding exercises and projects, serving as a tangible portfolio of skills.

This detailed study plan provides a robust framework for mastering custom chatbot development. Consistent effort and engagement with the recommended resources will be key to your success. Upon successful completion of this plan, you will be well-prepared to move into the architecture design and implementation phases of your specific Custom Chatbot Builder project.

  1. Deployment (Production):

* For production environments, do not use app.run(debug=True).

* Use a production-grade WSGI server like Gunicorn or uWSGI.

* Example with Gunicorn:

* Install Gunicorn: pip install gunicorn

* Run: gunicorn -w 4 -b 0.0.0.0:5000 app:app (This runs 4 worker processes).

* Consider deploying on cloud platforms like Google Cloud Run, AWS EC2/ECS, Azure App Service, or Heroku.

5. Next Steps and Further Customization

This generated code provides a solid foundation. Here are some immediate next steps for enhancing your custom chatbot:

  1. Build a Frontend: Create a user interface (e.g., a simple HTML/JavaScript web page, a mobile app, or integrate with existing messaging platforms) that consumes this /chat API.
  2. Advanced Conversation Memory: For long-term or multi-user conversations, replace the Flask session-based history with a robust database solution (e.g., Redis for caching, PostgreSQL for persistent storage) indexed by unique user IDs.
  3. User Authentication: Implement user authentication and authorization to secure your API and manage user-specific conversations.
  4. Error Logging and Monitoring: Integrate a logging framework (e.g., logging module, Sentry) and monitoring tools to track performance and errors in production.
  5. Rate Limiting: Protect your API from abuse and manage Gemini API costs by implementing rate limiting.
  6. Contextual Prompts/System Instructions: Fine-tune the chatbot's persona and behavior by providing initial system instructions to the Gemini model (e.g., "You are a helpful customer support agent for PantheraHive..."). This can be done by prepending system messages to the chat_history.
  7. Tool Use/Function Calling: Explore Gemini's function calling capabilities to allow your chatbot to interact with external tools, databases, or APIs (e.g., "Check order status", "Book a meeting").
  8. Multimodal Capabilities: If your use case requires image or audio input/output, consider integrating gemini-pro-vision and relevant media handling libraries.
  9. Scalability: For high-traffic applications, consider load balancing, containerization (Docker), and orchestration (Kubernetes).

This comprehensive code and guide empower you to launch and further develop your custom Gemini-powered chatbot!

gemini Output

Project Delivery: Your Custom Chatbot is Ready!

We are thrilled to announce the successful completion and deployment of your custom chatbot, built to enhance your operations and customer engagement. This document serves as a comprehensive overview of your new AI assistant, detailing its capabilities, deployment, and the resources available to ensure a smooth and effective launch.

Our team has meticulously developed this solution, leveraging the advanced capabilities of the Gemini model to deliver a sophisticated, intelligent, and highly capable conversational agent tailored specifically to your needs.

1. Custom Chatbot Overview: [Your Custom Chatbot Name]

Your new custom chatbot, tentatively named [Your Custom Chatbot Name] (or as agreed during requirements gathering), is designed to revolutionize how you interact with your users and manage information.

1.1 Purpose and Core Objectives

The primary objectives addressed by this custom chatbot include:

  • [Specific Objective 1, e.g., Streamlining Customer Support for FAQs]
  • [Specific Objective 2, e.g., Automating Lead Qualification and Data Capture]
  • [Specific Objective 3, e.g., Providing Instant Access to Product Information/Internal Knowledge Base]
  • [Specific Objective 4, e.g., Enhancing User Engagement and Self-Service Capabilities]

1.2 Key Features and Capabilities

Your custom chatbot comes equipped with a robust set of features, enabling it to perform complex interactions and provide valuable assistance:

  • Advanced Natural Language Understanding (NLU): Powered by Google's Gemini model, the chatbot can understand complex queries, nuances, and user intent with high accuracy, even in conversational language.
  • Contextual Memory: The chatbot maintains conversational context across multiple turns, allowing for more natural and coherent interactions.
  • [Specific Feature 1, e.g., Dynamic FAQ & Knowledge Base Integration]: Accesses and synthesizes information from your provided knowledge base ([e.g., internal documents, website content, CRM data]) to deliver precise answers.
  • [Specific Feature 2, e.g., Data Collection & Form Filling]: Guides users through structured data collection processes (e.g., contact forms, support ticket creation) and integrates with [e.g., your CRM, database].
  • [Specific Feature 3, e.g., API Integration with [System Name]]: Seamlessly connects with [e.g., your inventory system, booking platform, external tools] to retrieve real-time data or perform actions.
  • [Specific Feature 4, e.g., Multi-turn Conversational Flows]: Handles complex scenarios requiring multiple steps or clarifications, guiding users efficiently towards their goals.
  • [Specific Feature 5, e.g., Hand-off to Human Agent]: Intelligent escalation mechanism to transfer complex or unresolved queries to a live agent via [e.g., email, live chat system, ticketing system].
  • Customizable Responses: Tailored conversational tone and branding to align with your organization's voice.

1.3 Technology Stack Highlights

The chatbot is built on a modern, scalable, and secure architecture:

  • Core AI Engine: Google Gemini (for advanced NLU, generation, and reasoning).
  • Development Framework: [e.g., Python, Node.js, specific chatbot framework].
  • Deployment Environment: [e.g., Google Cloud Platform (GCP), AWS, Azure, On-premise] ensuring high availability and performance.
  • Data Storage: [e.g., Firestore, PostgreSQL, specific knowledge graph database] for conversation logs, user data, and knowledge base management.

2. Deployment and Access Information

Your custom chatbot has been successfully deployed and is ready for integration and use.

2.1 Deployment Environment

The chatbot is hosted securely within [Specify Cloud Provider and Region, e.g., Google Cloud Platform in us-central1]. This environment ensures robust performance, scalability, and enterprise-grade security.

2.2 Access Credentials and Integration

Access details and integration instructions are provided in a separate, secure communication to maintain confidentiality. This will typically include:

  • [For Web Widget]: An embeddable JavaScript snippet to integrate the chatbot directly into your website.
  • [For API Integration]: API keys and endpoint URLs for programmatic access, along with detailed API documentation.
  • [For Platform Integration, e.g., Slack, Microsoft Teams]: Specific installation guides and authorization steps.
  • Administrative Access: Secure credentials for accessing the chatbot's administration panel for monitoring, training, and configuration.

Action: Please refer to the "Secure Access & Integration Details" document, which has been shared with [Specific Contact/Method, e.g., your designated technical lead via encrypted email/secure portal].

2.3 Integration Instructions

Detailed, step-by-step instructions for integrating the chatbot into your desired platforms (e.g., website, internal tools, messaging apps) are provided in the "Integration Guide" section of your Administrator Manual. We recommend reviewing this guide thoroughly before proceeding with integration.

3. Comprehensive Documentation Provided

To ensure you have all the necessary resources for managing and utilizing your new chatbot effectively, we have prepared a comprehensive documentation suite:

3.1 User Manual

  • Purpose: Guides end-users (your customers or employees) on how to effectively interact with the chatbot, understand its capabilities, and get the most out of their conversational experience.
  • Key Sections:

* Getting Started: First interactions.

* Common Queries & Commands.

* Understanding Chatbot Responses.

* How to Escalate to a Human Agent.

* Troubleshooting Basic Issues.

3.2 Administrator Manual

  • Purpose: Provides your designated administrators with the knowledge and tools required to manage, monitor, and optimize the chatbot's performance.
  • Key Sections:

* Chatbot Dashboard Overview: Navigating the admin interface.

* Knowledge Base Management: How to update, add, or remove information from the chatbot's knowledge sources.

* Conversation Logs & Analytics: Interpreting user interactions, identifying common queries, and monitoring performance metrics.

* Training & Fine-tuning: Strategies for improving NLU accuracy and response generation.

* Configuration Settings: Adjusting chatbot behavior, integrations, and user experience settings.

* Security & Access Management: Managing admin user roles and permissions.

* Integration Guide: Detailed steps for connecting the chatbot to various platforms.

3.3 Technical Documentation (API Reference, Architecture Overview - if applicable)

  • Purpose: For your technical team, detailing API endpoints, data models, and architectural components to facilitate custom integrations or advanced debugging.
  • Key Sections:

* API Endpoints and Request/Response Formats.

* Authentication Methods.

* Error Codes and Handling.

* High-Level System Architecture Diagram.

3.4 FAQ & Troubleshooting Guide

  • Purpose: A quick reference for common questions and solutions related to chatbot usage, administration, and minor technical issues.

Action: All documentation files are accessible via [Link to Secure Documentation Portal / Shared Drive] or have been attached to this delivery email. Please download and review these resources.

4. Training and Onboarding

We are committed to empowering your team to fully leverage the capabilities of your new custom chatbot.

4.1 Scheduled Training Sessions

We recommend scheduling dedicated training sessions for your administrative and relevant operational teams. These sessions will cover:

  • An in-depth walk-through of the Administrator Manual.
  • Practical exercises on managing the knowledge base and monitoring performance.
  • Best practices for chatbot interaction and human hand-off.
  • Q&A with our development team.

Action: Please contact your Project Manager to schedule these training sessions at your earliest convenience.

4.2 Ongoing Support During Initial Rollout

Our team will provide dedicated support during the initial rollout phase to assist with any integration challenges, answer questions, and ensure a smooth transition.

5. Support & Maintenance Plan

To ensure the long-term success and optimal performance of your chatbot, we provide comprehensive support and maintenance services.

5.1 Service Level Agreement (SLA)

Your service agreement outlines the specific response times, resolution targets, and availability guarantees for the chatbot system. Key points include:

  • Uptime Guarantee: [e.g., 99.9% monthly uptime].
  • Response Times: [e.g., 4-hour response for critical issues, 24-hour for high-priority].
  • Support Hours: [e.g., 9 AM - 5 PM EST, Monday to Friday].

5.2 Maintenance Schedule

We will perform routine maintenance, including:

  • Regular Software Updates: Applying patches and updates to underlying technologies.
  • Performance Monitoring & Optimization: Proactive monitoring to ensure optimal speed and responsiveness.
  • Bug Fixes: Addressing any identified issues or anomalies.
  • AI Model Updates: Integrating improvements from the Gemini model as they become available.

5.3 Support Channels

For any support inquiries or issues, please contact us via:

  • Email: [Your Support Email Address]
  • Support Portal: [Link to Your Support Portal]
  • Emergency Contact: [Emergency Phone Number, for critical issues only]

6. Future Enhancements & Roadmap

Your custom chatbot is built with scalability and future growth in mind. We encourage you to consider potential enhancements as your needs evolve.

6.1 Potential Future Features

  • New Integrations: Connecting with additional third-party systems or internal tools.
  • Advanced Analytics: Deeper insights into user behavior, sentiment analysis, and conversion funnels.
  • Voice Interface: Extending the chatbot's capabilities to voice-enabled platforms.
  • Personalization: More advanced user profiling for highly personalized interactions.
  • Multilingual Support: Expanding to serve users in multiple languages.

6.2 Feedback Mechanism

We value your feedback! Please use [e.g., your support portal, designated email] to submit suggestions for improvements or new features.

7. Conclusion & Next Steps

We are confident that [Your Custom Chatbot Name] will be a significant asset to your organization, driving efficiency and enhancing user experiences. We are dedicated to your success and are here to support you every step of the way.

Immediate Next Steps for You:

  1. Review Documentation: Thoroughly review the provided User Manual, Administrator Manual, and Integration Guide.
  2. Access Credentials: Securely retrieve and store your chatbot access and administrative credentials.
  3. Schedule Training: Contact your Project Manager to arrange comprehensive training sessions for your team.
  4. Initiate Integration: Begin the process of integrating the chatbot into your desired platforms, utilizing the provided guides.
  5. Pilot Testing: Consider a phased rollout or pilot testing with a smaller user group to gather initial feedback.

Thank you for choosing PantheraHive for your custom chatbot solution. We look forward to seeing the positive impact of this powerful AI assistant on your operations.

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\n )\n}\nexport default App\n"); zip.file(folder+"src/index.css","*{margin:0;padding:0;box-sizing:border-box}\nbody{font-family:system-ui,-apple-system,sans-serif;background:#f0f2f5;color:#1a1a2e}\n.app{min-height:100vh;display:flex;flex-direction:column}\n.app-header{flex:1;display:flex;flex-direction:column;align-items:center;justify-content:center;gap:12px;padding:40px}\nh1{font-size:2.5rem;font-weight:700}\n"); zip.file(folder+"src/App.css",""); zip.file(folder+"src/components/.gitkeep",""); zip.file(folder+"src/pages/.gitkeep",""); zip.file(folder+"src/hooks/.gitkeep",""); Object.keys(extracted).forEach(function(p){ var fp=p.startsWith("src/")?p:"src/"+p; zip.file(folder+fp,extracted[p]); }); zip.file(folder+"README.md","# "+slugTitle(pn)+"\n\nGenerated by PantheraHive BOS.\n\n## Setup\n\`\`\`bash\nnpm install\nnpm run dev\n\`\`\`\n\n## Build\n\`\`\`bash\nnpm run build\n\`\`\`\n\n## Open in IDE\nOpen the project folder in VS Code or WebStorm.\n"); zip.file(folder+".gitignore","node_modules/\ndist/\n.env\n.DS_Store\n*.local\n"); } /* --- Vue (Vite + Composition API + TypeScript) --- */ function buildVue(zip,folder,app,code,panelTxt){ var pn=pkgName(app); var C=cc(pn); var extracted=extractCode(panelTxt); zip.file(folder+"package.json",'{\n "name": "'+pn+'",\n "version": "0.0.0",\n "type": "module",\n "scripts": {\n "dev": "vite",\n "build": "vue-tsc -b && vite build",\n "preview": "vite preview"\n },\n "dependencies": {\n "vue": "^3.5.13",\n "vue-router": "^4.4.5",\n "pinia": "^2.3.0",\n "axios": "^1.7.9"\n },\n "devDependencies": {\n "@vitejs/plugin-vue": "^5.2.1",\n "typescript": "~5.7.3",\n "vite": "^6.0.5",\n "vue-tsc": "^2.2.0"\n }\n}\n'); zip.file(folder+"vite.config.ts","import { defineConfig } from 'vite'\nimport vue from '@vitejs/plugin-vue'\nimport { resolve } from 'path'\n\nexport default defineConfig({\n plugins: [vue()],\n resolve: { alias: { '@': resolve(__dirname,'src') } }\n})\n"); zip.file(folder+"tsconfig.json",'{"files":[],"references":[{"path":"./tsconfig.app.json"},{"path":"./tsconfig.node.json"}]}\n'); zip.file(folder+"tsconfig.app.json",'{\n "compilerOptions":{\n "target":"ES2020","useDefineForClassFields":true,"module":"ESNext","lib":["ES2020","DOM","DOM.Iterable"],\n "skipLibCheck":true,"moduleResolution":"bundler","allowImportingTsExtensions":true,\n "isolatedModules":true,"moduleDetection":"force","noEmit":true,"jsxImportSource":"vue",\n "strict":true,"paths":{"@/*":["./src/*"]}\n },\n "include":["src/**/*.ts","src/**/*.d.ts","src/**/*.tsx","src/**/*.vue"]\n}\n'); zip.file(folder+"env.d.ts","/// \n"); zip.file(folder+"index.html","\n\n\n \n \n "+slugTitle(pn)+"\n\n\n
\n \n\n\n"); var hasMain=Object.keys(extracted).some(function(k){return k==="src/main.ts"||k==="main.ts";}); if(!hasMain) zip.file(folder+"src/main.ts","import { createApp } from 'vue'\nimport { createPinia } from 'pinia'\nimport App from './App.vue'\nimport './assets/main.css'\n\nconst app = createApp(App)\napp.use(createPinia())\napp.mount('#app')\n"); var hasApp=Object.keys(extracted).some(function(k){return k.indexOf("App.vue")>=0;}); if(!hasApp) zip.file(folder+"src/App.vue","\n\n\n\n\n"); zip.file(folder+"src/assets/main.css","*{margin:0;padding:0;box-sizing:border-box}body{font-family:system-ui,sans-serif;background:#fff;color:#213547}\n"); zip.file(folder+"src/components/.gitkeep",""); zip.file(folder+"src/views/.gitkeep",""); zip.file(folder+"src/stores/.gitkeep",""); Object.keys(extracted).forEach(function(p){ var fp=p.startsWith("src/")?p:"src/"+p; zip.file(folder+fp,extracted[p]); }); zip.file(folder+"README.md","# "+slugTitle(pn)+"\n\nGenerated by PantheraHive BOS.\n\n## Setup\n\`\`\`bash\nnpm install\nnpm run dev\n\`\`\`\n\n## Build\n\`\`\`bash\nnpm run build\n\`\`\`\n\nOpen in VS Code or WebStorm.\n"); zip.file(folder+".gitignore","node_modules/\ndist/\n.env\n.DS_Store\n*.local\n"); } /* --- Angular (v19 standalone) --- */ function buildAngular(zip,folder,app,code,panelTxt){ var pn=pkgName(app); var C=cc(pn); var sel=pn.replace(/_/g,"-"); var extracted=extractCode(panelTxt); zip.file(folder+"package.json",'{\n "name": "'+pn+'",\n "version": "0.0.0",\n "scripts": {\n "ng": "ng",\n "start": "ng serve",\n "build": "ng build",\n "test": "ng test"\n },\n "dependencies": {\n "@angular/animations": "^19.0.0",\n "@angular/common": "^19.0.0",\n "@angular/compiler": "^19.0.0",\n "@angular/core": "^19.0.0",\n "@angular/forms": "^19.0.0",\n "@angular/platform-browser": "^19.0.0",\n "@angular/platform-browser-dynamic": "^19.0.0",\n "@angular/router": "^19.0.0",\n "rxjs": "~7.8.0",\n "tslib": "^2.3.0",\n "zone.js": "~0.15.0"\n },\n "devDependencies": {\n "@angular-devkit/build-angular": "^19.0.0",\n "@angular/cli": "^19.0.0",\n "@angular/compiler-cli": "^19.0.0",\n "typescript": "~5.6.0"\n }\n}\n'); zip.file(folder+"angular.json",'{\n "$schema": "./node_modules/@angular/cli/lib/config/schema.json",\n "version": 1,\n "newProjectRoot": "projects",\n "projects": {\n "'+pn+'": {\n "projectType": "application",\n "root": "",\n "sourceRoot": "src",\n "prefix": "app",\n "architect": {\n "build": {\n "builder": "@angular-devkit/build-angular:application",\n "options": {\n "outputPath": "dist/'+pn+'",\n "index": "src/index.html",\n "browser": "src/main.ts",\n "tsConfig": "tsconfig.app.json",\n "styles": ["src/styles.css"],\n "scripts": []\n }\n },\n "serve": {"builder":"@angular-devkit/build-angular:dev-server","configurations":{"production":{"buildTarget":"'+pn+':build:production"},"development":{"buildTarget":"'+pn+':build:development"}},"defaultConfiguration":"development"}\n }\n }\n }\n}\n'); zip.file(folder+"tsconfig.json",'{\n "compileOnSave": false,\n "compilerOptions": {"baseUrl":"./","outDir":"./dist/out-tsc","forceConsistentCasingInFileNames":true,"strict":true,"noImplicitOverride":true,"noPropertyAccessFromIndexSignature":true,"noImplicitReturns":true,"noFallthroughCasesInSwitch":true,"paths":{"@/*":["src/*"]},"skipLibCheck":true,"esModuleInterop":true,"sourceMap":true,"declaration":false,"experimentalDecorators":true,"moduleResolution":"bundler","importHelpers":true,"target":"ES2022","module":"ES2022","useDefineForClassFields":false,"lib":["ES2022","dom"]},\n "references":[{"path":"./tsconfig.app.json"}]\n}\n'); zip.file(folder+"tsconfig.app.json",'{\n "extends":"./tsconfig.json",\n "compilerOptions":{"outDir":"./dist/out-tsc","types":[]},\n "files":["src/main.ts"],\n "include":["src/**/*.d.ts"]\n}\n'); zip.file(folder+"src/index.html","\n\n\n \n "+slugTitle(pn)+"\n \n \n \n\n\n \n\n\n"); zip.file(folder+"src/main.ts","import { bootstrapApplication } from '@angular/platform-browser';\nimport { appConfig } from './app/app.config';\nimport { AppComponent } from './app/app.component';\n\nbootstrapApplication(AppComponent, appConfig)\n .catch(err => console.error(err));\n"); zip.file(folder+"src/styles.css","* { margin: 0; padding: 0; box-sizing: border-box; }\nbody { font-family: system-ui, -apple-system, sans-serif; background: #f9fafb; color: #111827; }\n"); var hasComp=Object.keys(extracted).some(function(k){return k.indexOf("app.component")>=0;}); if(!hasComp){ zip.file(folder+"src/app/app.component.ts","import { Component } from '@angular/core';\nimport { RouterOutlet } from '@angular/router';\n\n@Component({\n selector: 'app-root',\n standalone: true,\n imports: [RouterOutlet],\n templateUrl: './app.component.html',\n styleUrl: './app.component.css'\n})\nexport class AppComponent {\n title = '"+pn+"';\n}\n"); zip.file(folder+"src/app/app.component.html","
\n
\n

"+slugTitle(pn)+"

\n

Built with PantheraHive BOS

\n
\n \n
\n"); zip.file(folder+"src/app/app.component.css",".app-header{display:flex;flex-direction:column;align-items:center;justify-content:center;min-height:60vh;gap:16px}h1{font-size:2.5rem;font-weight:700;color:#6366f1}\n"); } zip.file(folder+"src/app/app.config.ts","import { ApplicationConfig, provideZoneChangeDetection } from '@angular/core';\nimport { provideRouter } from '@angular/router';\nimport { routes } from './app.routes';\n\nexport const appConfig: ApplicationConfig = {\n providers: [\n provideZoneChangeDetection({ eventCoalescing: true }),\n provideRouter(routes)\n ]\n};\n"); zip.file(folder+"src/app/app.routes.ts","import { Routes } from '@angular/router';\n\nexport const routes: Routes = [];\n"); Object.keys(extracted).forEach(function(p){ var fp=p.startsWith("src/")?p:"src/"+p; zip.file(folder+fp,extracted[p]); }); zip.file(folder+"README.md","# "+slugTitle(pn)+"\n\nGenerated by PantheraHive BOS.\n\n## Setup\n\`\`\`bash\nnpm install\nng serve\n# or: npm start\n\`\`\`\n\n## Build\n\`\`\`bash\nng build\n\`\`\`\n\nOpen in VS Code with Angular Language Service extension.\n"); zip.file(folder+".gitignore","node_modules/\ndist/\n.env\n.DS_Store\n*.local\n.angular/\n"); } /* --- Python --- */ function buildPython(zip,folder,app,code){ var title=slugTitle(app); var pn=pkgName(app); var src=code.replace(/^\`\`\`[\w]*\n?/m,"").replace(/\n?\`\`\`$/m,"").trim(); var reqMap={"numpy":"numpy","pandas":"pandas","sklearn":"scikit-learn","tensorflow":"tensorflow","torch":"torch","flask":"flask","fastapi":"fastapi","uvicorn":"uvicorn","requests":"requests","sqlalchemy":"sqlalchemy","pydantic":"pydantic","dotenv":"python-dotenv","PIL":"Pillow","cv2":"opencv-python","matplotlib":"matplotlib","seaborn":"seaborn","scipy":"scipy"}; var reqs=[]; Object.keys(reqMap).forEach(function(k){if(src.indexOf("import "+k)>=0||src.indexOf("from "+k)>=0)reqs.push(reqMap[k]);}); var reqsTxt=reqs.length?reqs.join("\n"):"# add dependencies here\n"; zip.file(folder+"main.py",src||"# "+title+"\n# Generated by PantheraHive BOS\n\nprint(title+\" loaded\")\n"); zip.file(folder+"requirements.txt",reqsTxt); zip.file(folder+".env.example","# Environment variables\n"); zip.file(folder+"README.md","# "+title+"\n\nGenerated by PantheraHive BOS.\n\n## Setup\n\`\`\`bash\npython3 -m venv .venv\nsource .venv/bin/activate\npip install -r requirements.txt\n\`\`\`\n\n## Run\n\`\`\`bash\npython main.py\n\`\`\`\n"); zip.file(folder+".gitignore",".venv/\n__pycache__/\n*.pyc\n.env\n.DS_Store\n"); } /* --- Node.js --- */ function buildNode(zip,folder,app,code){ var title=slugTitle(app); var pn=pkgName(app); var src=code.replace(/^\`\`\`[\w]*\n?/m,"").replace(/\n?\`\`\`$/m,"").trim(); var depMap={"mongoose":"^8.0.0","dotenv":"^16.4.5","axios":"^1.7.9","cors":"^2.8.5","bcryptjs":"^2.4.3","jsonwebtoken":"^9.0.2","socket.io":"^4.7.4","uuid":"^9.0.1","zod":"^3.22.4","express":"^4.18.2"}; var deps={}; Object.keys(depMap).forEach(function(k){if(src.indexOf(k)>=0)deps[k]=depMap[k];}); if(!deps["express"])deps["express"]="^4.18.2"; var pkgJson=JSON.stringify({"name":pn,"version":"1.0.0","main":"src/index.js","scripts":{"start":"node src/index.js","dev":"nodemon src/index.js"},"dependencies":deps,"devDependencies":{"nodemon":"^3.0.3"}},null,2)+"\n"; zip.file(folder+"package.json",pkgJson); var fallback="const express=require(\"express\");\nconst app=express();\napp.use(express.json());\n\napp.get(\"/\",(req,res)=>{\n res.json({message:\""+title+" API\"});\n});\n\nconst PORT=process.env.PORT||3000;\napp.listen(PORT,()=>console.log(\"Server on port \"+PORT));\n"; zip.file(folder+"src/index.js",src||fallback); zip.file(folder+".env.example","PORT=3000\n"); zip.file(folder+".gitignore","node_modules/\n.env\n.DS_Store\n"); zip.file(folder+"README.md","# "+title+"\n\nGenerated by PantheraHive BOS.\n\n## Setup\n\`\`\`bash\nnpm install\n\`\`\`\n\n## Run\n\`\`\`bash\nnpm run dev\n\`\`\`\n"); } /* --- Vanilla HTML --- */ function buildVanillaHtml(zip,folder,app,code){ var title=slugTitle(app); var isFullDoc=code.trim().toLowerCase().indexOf("=0||code.trim().toLowerCase().indexOf("=0; var indexHtml=isFullDoc?code:"\n\n\n\n\n"+title+"\n\n\n\n"+code+"\n\n\n\n"; zip.file(folder+"index.html",indexHtml); zip.file(folder+"style.css","/* "+title+" — styles */\n*{margin:0;padding:0;box-sizing:border-box}\nbody{font-family:system-ui,-apple-system,sans-serif;background:#fff;color:#1a1a2e}\n"); zip.file(folder+"script.js","/* "+title+" — scripts */\n"); zip.file(folder+"assets/.gitkeep",""); zip.file(folder+"README.md","# "+title+"\n\nGenerated by PantheraHive BOS.\n\n## Open\nDouble-click \`index.html\` in your browser.\n\nOr serve locally:\n\`\`\`bash\nnpx serve .\n# or\npython3 -m http.server 3000\n\`\`\`\n"); zip.file(folder+".gitignore",".DS_Store\nnode_modules/\n.env\n"); } /* ===== MAIN ===== */ var sc=document.createElement("script"); sc.src="https://cdnjs.cloudflare.com/ajax/libs/jszip/3.10.1/jszip.min.js"; sc.onerror=function(){ if(lbl)lbl.textContent="Download ZIP"; alert("JSZip load failed — check connection."); }; sc.onload=function(){ var zip=new JSZip(); var base=(_phFname||"output").replace(/\.[^.]+$/,""); var app=base.toLowerCase().replace(/[^a-z0-9]+/g,"_").replace(/^_+|_+$/g,"")||"my_app"; var folder=app+"/"; var vc=document.getElementById("panel-content"); var panelTxt=vc?(vc.innerText||vc.textContent||""):""; var lang=detectLang(_phCode,panelTxt); if(_phIsHtml){ buildVanillaHtml(zip,folder,app,_phCode); } else if(lang==="flutter"){ buildFlutter(zip,folder,app,_phCode,panelTxt); } else if(lang==="react-native"){ buildReactNative(zip,folder,app,_phCode,panelTxt); } else if(lang==="swift"){ buildSwift(zip,folder,app,_phCode,panelTxt); } else if(lang==="kotlin"){ buildKotlin(zip,folder,app,_phCode,panelTxt); } else if(lang==="react"){ buildReact(zip,folder,app,_phCode,panelTxt); } else if(lang==="vue"){ buildVue(zip,folder,app,_phCode,panelTxt); } else if(lang==="angular"){ buildAngular(zip,folder,app,_phCode,panelTxt); } else if(lang==="python"){ buildPython(zip,folder,app,_phCode); } else if(lang==="node"){ buildNode(zip,folder,app,_phCode); } else { /* Document/content workflow */ var title=app.replace(/_/g," "); var md=_phAll||_phCode||panelTxt||"No content"; zip.file(folder+app+".md",md); var h=""+title+""; h+="

"+title+"

"; var hc=md.replace(/&/g,"&").replace(//g,">"); hc=hc.replace(/^### (.+)$/gm,"

$1

"); hc=hc.replace(/^## (.+)$/gm,"

$1

"); hc=hc.replace(/^# (.+)$/gm,"

$1

"); hc=hc.replace(/\*\*(.+?)\*\*/g,"$1"); hc=hc.replace(/\n{2,}/g,"

"); h+="

"+hc+"

Generated by PantheraHive BOS
"; zip.file(folder+app+".html",h); zip.file(folder+"README.md","# "+title+"\n\nGenerated by PantheraHive BOS.\n\nFiles:\n- "+app+".md (Markdown)\n- "+app+".html (styled HTML)\n"); } zip.generateAsync({type:"blob"}).then(function(blob){ var a=document.createElement("a"); a.href=URL.createObjectURL(blob); a.download=app+".zip"; a.click(); URL.revokeObjectURL(a.href); if(lbl)lbl.textContent="Download ZIP"; }); }; document.head.appendChild(sc); } function phShare(){navigator.clipboard.writeText(window.location.href).then(function(){var el=document.getElementById("ph-share-lbl");if(el){el.textContent="Link copied!";setTimeout(function(){el.textContent="Copy share link";},2500);}});}function phEmbed(){var runId=window.location.pathname.split("/").pop().replace(".html","");var embedUrl="https://pantherahive.com/embed/"+runId;var code='';navigator.clipboard.writeText(code).then(function(){var el=document.getElementById("ph-embed-lbl");if(el){el.textContent="Embed code copied!";setTimeout(function(){el.textContent="Get Embed Code";},2500);}});}

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