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πŸ” Add AutoExec AI full application files for Hugging Face Space

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Files changed (4) hide show
  1. Dockerfile +3 -2
  2. README.md +63 -23
  3. git +0 -0
  4. start.sh +13 -0
Dockerfile CHANGED
@@ -7,6 +7,7 @@ RUN apt-get update && apt-get install -y redis-server && pip install --no-cache-
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  COPY . .
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- EXPOSE 7860
 
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- CMD service redis-server start && uvicorn app.main:app --host 0.0.0.0 --port 7860 & celery -A celery_worker worker --loglevel=info & streamlit run app.py --server.port 7861
 
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  COPY . .
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+ COPY start.sh /start.sh
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+ RUN chmod +x /start.sh
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+ ENTRYPOINT ["/start.sh"]
README.md CHANGED
@@ -1,34 +1,74 @@
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- # πŸš€ AutoExec AI: Full Stack AI Business Launcher (Streamlit + FastAPI + Celery + LangGraph)
 
 
 
 
 
 
 
 
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- ## Overview
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- This Hugging Face Docker Space contains a unified AI-powered platform to generate and optimize digital businesses autonomously using LLMs.
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- ### What's Inside
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- - Streamlit UI
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- - FastAPI backend
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- - Celery worker (auto-task scheduling)
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- - Redis server
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- - SQLite memory DB
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- - LangGraph JSON config
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- - Dashboard for agent logs
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- ## Running Locally
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```bash
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- uvicorn app.main:app --reload
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- streamlit run dashboard.py
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- celery -A celery_worker worker --loglevel=info
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  ```
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- ## Hugging Face Space
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- Upload the full repo to a **Docker Space** and Hugging Face will build and run it for you.
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- Make sure to set secrets for:
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- ```
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- GEMINI_API_KEY
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- OPENAI_API_KEY
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- ```
 
 
 
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- Streamlit runs on port 7861. FastAPI on port 7860.
 
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+ ---
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+ title: AutoExec AI
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+ emoji: πŸš€
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+ colorFrom: purple
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+ colorTo: indigo
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+ sdk: docker
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+ app_file: app.py
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+ pinned: true
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+ ---
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+ # πŸš€ AutoExec AI: Your Autonomous AI Business Builder
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+ [![Hugging Face Spaces](https://img.shields.io/badge/Launch%20on-HuggingFace-%23f6f6f6?logo=huggingface&labelColor=black)](https://huggingface.co/spaces/mgbam/AICEO)
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+ ---
 
 
 
 
 
 
 
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+ AutoExec AI is a fully autonomous AI-powered startup generator that builds, optimizes, and scales digital businesses for you using:
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+
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+ - πŸ€– **LLMs** (Gemini Pro + GPT-4 fallback)
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+ - πŸ“ˆ **LangGraph-style multi-agent coordination**
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+ - πŸ” **Auto-optimization loops** (LoopAgent)
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+ - 🧠 **Persistent agent memory** using SQLite
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+ - βš™οΈ **FastAPI + Celery + Redis** for orchestration
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+ - πŸ–₯️ **Streamlit UI** for interactive control panel
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+
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+ ---
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+
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+ ## 🎯 Key Features
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+
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+ βœ… AI-driven product ideation by niche
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+ βœ… Copy generation: product descriptions, landing pages, emails
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+ βœ… Ad campaign builder with budget constraints
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+ βœ… LoopAgent: re-optimizes your strategy daily
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+ βœ… Scales to manage multiple businesses in parallel
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+ βœ… Includes a dashboard to inspect every agent run
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+ βœ… Full Docker deployment on Hugging Face Spaces
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+
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+ ---
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+
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+ ## πŸ”§ Tech Stack
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+
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+ | Layer | Tech |
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+ |--------------|----------------------------------------|
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+ | UI | `Streamlit` |
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+ | Backend API | `FastAPI` |
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+ | Scheduler | `Celery` + `Redis` |
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+ | AI Models | `Gemini Pro`, `GPT-4` fallback (OpenAI) |
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+ | Memory | `SQLite` (persistent logging) |
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+ | Multi-Agent | `LangGraph-style config` |
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+ | Hosting | `Docker` via Hugging Face Spaces |
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+
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+ ---
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+
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+ ## πŸš€ Quickstart (Dev Mode)
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  ```bash
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+ uvicorn app.main:app --reload # Start FastAPI
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+ celery -A celery_worker worker # Background optimization
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+ streamlit run dashboard.py # View agent memory logs
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  ```
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+ ---
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+ ## 🌍 Keywords for Discovery
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+ `AI startup launcher`, `dropshipping AI`, `auto business generator`, `LangGraph AI`, `FastAPI Streamlit startup`, `Hugging Face Docker Spaces`, `Celery Redis orchestrator`, `GPT-4 product copy`, `autonomous agents`, `AutoExec AI`
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+
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+ ---
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+
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+ ## 🀝 Contributing
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+
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+ Feel free to fork, star, and contribute! PRs welcome.
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+ For collaborations, reach out via Hugging Face or [LinkedIn](https://www.linkedin.com/in/your-profile).
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git ADDED
File without changes
start.sh ADDED
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+ #!/bin/bash
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+
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+ # Start Redis server
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+ redis-server &
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+
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+ # Start FastAPI backend
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+ uvicorn app.main:app --host 0.0.0.0 --port 7860 &
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+
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+ # Start Celery worker
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+ celery -A celery_worker worker --loglevel=info &
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+
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+ # Start Streamlit app
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+ streamlit run app.py --server.port 7861