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| """ | |
| Project Name: Autonomous AI Orchestrator | |
| Description: | |
| An end-to-end, self-improving AI agent framework that takes user goals and autonomously: | |
| - Plans multi-step workflows (Research, Learn, Develop, Execute, Diagnose, Review) | |
| - Performs web searches and aggregates reliable information | |
| - Generates and safely executes Python code | |
| - Self-evaluates and optimizes its own processes | |
| - Provides a Learning Coach for personalized, source-backed teaching | |
| - Persists session memory, logs activities, and snapshots full state for audit and rollback | |
| Instructions: | |
| 1. **Dependencies**: Ensure your `requirements.txt` contains: | |
| ``` | |
| duckduckgo-search | |
| gradio | |
| requests | |
| python-dotenv | |
| feedparser | |
| ``` | |
| 2. **Module Structure**: Place the following modules alongside your main script (`app.py`): | |
| - `memory.py` | |
| - `planner.py` | |
| - `executor.py` | |
| - `critic.py` | |
| - `cognitive_engine.py` | |
| - `web_searcher.py` | |
| - `hf_packager.py` | |
| - (Optional CSS: `style.css` for UI styling) | |
| 3. **Run the Application**: | |
| ```bash | |
| python app.py | |
| ``` | |
| 4. **Access Interfaces**: | |
| - **Gradio UI**: Navigate to `http://<host>:<port>/ui` for Task Execution and Learning Coach tabs | |
| - **API Endpoints**: | |
| - `GET /status` — Health check and available routes | |
| - `GET /generate?prompt=<text>` — Quick text generation | |
| - `POST /generate` — JSON-based generation with session tracking | |
| - `GET /memory/{session_id}` — Retrieve session history | |
| - `GET /summary/{session_id}` — Summarize recent interactions | |
| 5. **Persistence & Logs**: | |
| - Activity logs in `log.txt` | |
| - Session memory stored in `memory.json` | |
| - Snapshots saved under `snapshots/` with recoverable JSON files | |
| """ | |
| import os | |
| import zipfile | |
| import base64 | |
| import gradio as gr | |
| import uuid | |
| import json | |
| import logging | |
| import datetime | |
| from memory_manager import MemoryManager | |
| from planner import Planner | |
| from executor import Executor | |
| from critic import Critic | |
| from cognitive_engine import CognitiveEngine | |
| from web_searcher import WebSearcher | |
| from hf_packager import HFSpacePackager | |
| # Initialize components | |
| memory = MemoryManager() | |
| planner = Planner() | |
| executor = Executor() | |
| critic = Critic() | |
| cog_engine = CognitiveEngine() | |
| web_searcher = WebSearcher() | |
| packager = HFSpacePackager() | |
| # Set up logging | |
| logging.basicConfig(filename='log.txt', level=logging.INFO, | |
| format='%(asctime)s - %(levelname)s - %(message)s') | |
| class AutonomousAgent: | |
| def __init__(self): | |
| self.state_file = "state.json" | |
| self.load_state() | |
| def load_state(self): | |
| try: | |
| with open(self.state_file, 'r') as f: | |
| self.state = json.load(f) | |
| except (FileNotFoundError, json.JSONDecodeError): | |
| self.state = {"sessions": {}} | |
| self.save_state() | |
| def save_state(self): | |
| with open(self.state_file, 'w') as f: | |
| json.dump(self.state, f, indent=2) | |
| def process_goal(self, goal, session_id=None): | |
| try: | |
| if not session_id: | |
| session_id = str(uuid.uuid4()) | |
| self.state["sessions"][session_id] = {"goal": goal, "status": "processing"} | |
| memory.init_session(session_id) | |
| self.save_state() | |
| # Add to memory | |
| memory.add(session_id, "user_goal", goal) | |
| # Plan the task | |
| plan = planner.plan_task(goal, memory.get(session_id)) | |
| memory.add(session_id, "plan", plan) | |
| # Execute plan | |
| results = [] | |
| for step in plan: | |
| key = step.split(":")[0].lower() | |
| if key in ["research", "search"]: | |
| query = step.split(":",1)[1].strip() | |
| res = web_searcher.search(query) | |
| memory.add(session_id, f"search:{query}", res) | |
| results.append(f"🔍 Search '{query}': {res[:300]}...") | |
| elif key in ["develop", "code"]: | |
| code = cog_engine.generate_code(step, memory.get(session_id)) | |
| out = executor.execute_code(code) | |
| memory.add(session_id, "generated_code", code) | |
| memory.add(session_id, "execution_result", out) | |
| review = critic.review(step, out) | |
| memory.add(session_id, "review", review) | |
| if "error" in review.lower(): | |
| improved = cog_engine.improve_code(code, review) | |
| memory.add(session_id, "enhanced_code", improved) | |
| out = executor.execute_code(improved) | |
| results.append(f"🛠️ Enhanced code: {out}") | |
| else: | |
| results.append(f"✅ Code executed: {out}") | |
| elif key in ["diagnose", "check"]: | |
| issues = cog_engine.identify_improvements(step) | |
| memory.add(session_id, "diagnosis", issues) | |
| if issues: | |
| fixes = cog_engine.generate_enhancements(issues) | |
| cog_engine.apply_enhancements(fixes) | |
| results.append(f"⚙️ Repaired: {', '.join(issues)}") | |
| else: | |
| results.append("✅ Healthy") | |
| self.state["sessions"][session_id]["status"] = "completed" | |
| self.save_state() | |
| snap = packager.create_snapshot({"session_id": session_id, "memory": memory.get(session_id), "results": results}) | |
| return "\n\n".join(results), session_id, snap | |
| except Exception as e: | |
| logging.error(f"Error: {e}", exc_info=True) | |
| issues = [str(e)] | |
| fixes = cog_engine.generate_enhancements(issues) | |
| cog_engine.apply_enhancements(fixes) | |
| return f"⚠️ Error occurred: {e}", session_id, "" | |
| agent = AutonomousAgent() | |
| with gr.Blocks(css="style.css", title="Autonomous AI") as demo: | |
| sid = gr.State() | |
| hist = gr.State({"history": []}) | |
| gr.Markdown("# 🤖 Autonomous AI System") | |
| with gr.Tab("Task Execution"): | |
| inp = gr.Textbox(label="Your Goal") | |
| btn = gr.Button("Execute Goal") | |
| out = gr.Textbox(label="Results", lines=10) | |
| sid_box = gr.Textbox(label="Session ID") | |
| snap_box = gr.Textbox(label="Snapshot URL") | |
| btn.click(agent.process_goal, [inp, sid], [out, sid_box, snap_box]) | |
| with gr.Tab("Learning Coach"): | |
| chat = gr.Chatbot() | |
| q_in = gr.Textbox(label="Your Question") | |
| ask = gr.Button("Ask") | |
| clear = gr.Button("Clear") | |
| kb = gr.Markdown("") | |
| ask.click(lambda q,h: (h+[(q, cog_engine.learning_coach_response(q))], h), [q_in, hist], [chat, hist]) | |
| clear.click(lambda h: ([],h), [hist], [chat, hist]) | |
| demo.launch() | |