Upload 9 files

agents/critic.py

@@ -1,5 +1 @@
-# agents/critic.py
-
-def review_result(step, result):
-    # Placeholder: review the execution result and return feedback
-    return f"Review for step '{step}': Success"
+def review_result(step, result): return f'Reviewed {step} -> {result}'

agents/executor.py

@@ -1,8 +1 @@
-# agents/executor.py
-
-def execute_step(step):
-    # Placeholder: execute the given step and return a result string
-    return f"Executed: {step}"
-
-def execute_step(step):
-    return f"Executed: {step} (simulated)"
+def execute_step(step): return f'Executed: {step}'

agents/planner.py

@@ -1,9 +1 @@
-# agents/planner.py
-
-def plan_task(goal, memory):
-    # Placeholder: create a plan (list of steps) to accomplish the goal
-    return [f"Step 1 towards {goal}", f"Step 2 towards {goal}"]
-
-def plan_task(goal, memory):
-    return [f"Analyze: {goal}", "Generate code", "Review & validate"]
-
+def plan_task(goal, memory): return [f'Plan step for: {goal}']

app.py

@@ -1,206 +1 @@
-# app.py
-
-from fastapi import FastAPI, Request, HTTPException
-from fastapi.responses import JSONResponse
-import gradio as gr
-import uuid
-import time
-import logging
-import os
-import json
-import psutil
-from datetime import datetime
-from pydantic import BaseModel, constr
-from ctransformers import AutoModelForCausalLM
-from src.core.cognitive_engine import CognitiveEngine
-from src.utils.hf_packager import HFSpacePackager
-from agents.planner import plan_task
-from agents.executor import execute_step
-from agents.critic import review_result
-from memory import init_memory, add_to_memory, get_memory, get_summary, search_memory
-
-# ========== Initialization ==========
-logging.basicConfig(filename="app.log", level=logging.INFO)
-app = FastAPI()
-memory = init_memory()
-
-llm_model = AutoModelForCausalLM.from_pretrained(
-    "TheBloke/zephyr-7B-alpha-GGUF",
-    model_file="zephyr-7b-alpha.Q4_K_M.gguf",
-    model_type="llama",
-    max_new_tokens=256,
-    temperature=0.7
-)
-cognitive_engine = CognitiveEngine(llm_model)
-packager = HFSpacePackager()
-
-# ========== Pydantic Models ==========
-class GenerateRequest(BaseModel):
-    prompt: constr(min_length=5, max_length=1000)
-    session_id: constr(min_length=3, max_length=50)
-
-# ========== Routes ==========
-@app.get("/")
-def home():
-    return {"message": "✅ Autonomous AI API is live", "routes": ["/status", "/generate", "/ui"]}
-
-@app.get("/status")
-def status():
-    return {"status": "active", "agents": ["planner", "executor", "critic"]}
-
-@app.post("/generate")
-def generate_post(data: GenerateRequest):
-    start = time.time()
-    try:
-        response = llm_model(data.prompt)
-        latency = round(time.time() - start, 2)
-        add_to_memory(data.session_id, data.prompt, response)
-        logging.info(f"[{data.session_id}] Prompt processed in {latency}s")
-        return {"session_id": data.session_id, "response": response, "latency": latency}
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.get("/memory")
-def view_memory():
-    return {"history": get_memory()}
-
-@app.get("/memory/{session_id}")
-def session_memory(session_id: str):
-    return {"session_id": session_id, "history": get_memory(session_id)}
-
-@app.get("/summary/{session_id}")
-def memory_summary(session_id: str):
-    return {"session_id": session_id, "summary": get_summary(session_id)}
-
-@app.middleware("http")
-async def auth_middleware(request: Request, call_next):
-    if request.url.path.startswith("/generate") and request.headers.get("X-API-Key") != "your-secret":
-        return JSONResponse(status_code=401, content={"detail": "Invalid API Key"})
-    return await call_next(request)
-
-# ========== Knowledge Base ==========
-def load_knowledge():
-    try:
-        with open("knowledge/state.json", "r") as f:
-            return json.load(f)
-    except:
-        return {"improvements": [], "solutions": {}, "tasks": []}
-
-def save_knowledge(data):
-    with open("knowledge/state.json", "w") as f:
-        json.dump(data, f, indent=2)
-
-# ========== Autonomous Agents ==========
-def run_cycle(task_description):
-    knowledge = load_knowledge()
-    knowledge["tasks"].append(task_description)
-    save_knowledge(knowledge)
-    try:
-        improvements = cognitive_engine.identify_improvements(task_description)
-        code_updates = cognitive_engine.generate_enhancements(improvements)
-        if cognitive_engine.apply_enhancements(code_updates):
-            snapshot_url = packager.create_snapshot()
-            return f"✅ Task completed! Snapshot: {snapshot_url}", code_updates, "\n".join(improvements)
-        return "❌ Improvement failed - see logs", "", ""
-    except Exception as e:
-        return f"⚠️ Critical error: {str(e)}", "", ""
-
-def run_agents(goal, memory, session_id=None):
-    session_id = session_id or str(uuid.uuid4())[:8]
-    timestamp = datetime.now().isoformat(timespec="seconds")
-    log = f"[{timestamp}] Session {session_id}: Goal: {goal}\n"
-    history = search_memory(goal, memory)
-    context = "\n".join(history[:3]) if history else "No relevant memory found."
-    plan = plan_task(goal, memory)
-    add_to_memory(session_id, f"🧠 Context: {context}")
-    add_to_memory(session_id, f"🗂 Plan: {plan}")
-    outputs = []
-    for step in plan:
-        result = execute_step(step)
-        add_to_memory(session_id, f"🔧 Executor ran: {step} -> {result}")
-        review = review_result(step, result)
-        add_to_memory(session_id, f"🔍 Critic: {review}")
-        step_log = f"🔹 Step: {step}\n🛠 Result: {result}\n🧠 Review: {review}\n"
-        log += step_log + "\n"
-        outputs.append(step_log)
-    with open("log.txt", "a") as f:
-        f.write(log + "\n")
-    return "\n".join(outputs)
-
-def agent_interface(goal):
-    return run_agents(goal, memory)
-
-def get_resource_usage():
-    return {"cpu": f"{psutil.cpu_percent()}%", "memory": f"{psutil.virtual_memory().percent}%", "disk": f"{psutil.disk_usage('/').percent}%"}
-
-def get_task_history():
-    knowledge = load_knowledge()
-    return "\n".join([f"- {task}" for task in knowledge.get("tasks", [])[-5:]]) or "No task history available."
-
-def submit_manual_code(code):
-    try:
-        if cognitive_engine.apply_enhancements(code):
-            snapshot_url = packager.create_snapshot()
-            return f"✅ Code applied! Snapshot: {snapshot_url}", code
-        return "❌ Code application failed", code
-    except Exception as e:
-        return f"⚠️ Error: {str(e)}", code
-
-# ========== Gradio UI ==========
-with gr.Blocks(css="static/style.css", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🤖 Multi-Agent Autonomous AI System")
-    with gr.Tab("Multi-Agent Task"):
-        goal_input = gr.Textbox(label="Describe your task", placeholder="What do you want to accomplish?", lines=3)
-        agent_output = gr.Textbox(label="Multi-Agent Process", lines=10, interactive=False)
-        run_agents_btn = gr.Button("🚀 Run Agents", variant="primary")
-        run_agents_btn.click(agent_interface, inputs=[goal_input], outputs=[agent_output])
-        gr.Markdown("### Code Guidelines")
-        gr.Markdown("""
-        1. Ensure backward compatibility
-        2. Include necessary imports
-        3. Add comments explaining changes
-        4. Avoid system-critical modifications
-        """)
-    with gr.Tab("Cognitive Engine"):
-        with gr.Row():
-            with gr.Column():
-                task_input = gr.Textbox(label="Describe your improvement task", lines=3)
-                submit_task = gr.Button("🚀 Execute Task", variant="primary")
-                resource_display = gr.JSON(value=get_resource_usage(), label="Current Status")
-                refresh_btn = gr.Button("🔄 Refresh Status")
-            with gr.Column():
-                task_output = gr.Textbox(label="Task Status", interactive=False)
-                improvement_list = gr.Textbox(label="Identified Improvements", interactive=False)
-                generated_code = gr.Code(label="Generated Code", language="python", interactive=True)
-        submit_task.click(run_cycle, inputs=[task_input], outputs=[task_output, generated_code, improvement_list])
-        refresh_btn.click(get_resource_usage, inputs=[], outputs=resource_display)
-    with gr.Tab("Manual Code"):
-        with gr.Row():
-            with gr.Column():
-                manual_code = gr.Code(label="Submit Your Own Code", language="python", value="# Write your code enhancements here", interactive=True)
-                submit_manual = gr.Button("💾 Apply Code", variant="primary")
-                manual_output = gr.Textbox(label="Result")
-            with gr.Column():
-                gr.Markdown("### Code Guidelines")
-                gr.Markdown("""
-                1. Ensure backward compatibility
-                2. Include necessary imports
-                3. Add comments explaining changes
-                4. Avoid system-critical modifications
-                """)
-        submit_manual.click(submit_manual_code, inputs=[manual_code], outputs=[manual_output, manual_code])
-    with gr.Tab("Knowledge & History"):
-        task_history = gr.Textbox(label="Recent Tasks", value=get_task_history(), interactive=False)
-        gr.Markdown("### Memory Log")
-        memory_display = gr.Textbox(label="Agent Memory", value="\n".join(memory["texts"][-5:]), interactive=False)
-        refresh_knowledge = gr.Button("🔁 Refresh Knowledge")
-        refresh_knowledge.click(lambda: load_knowledge(), inputs=[], outputs=task_history)
-
-# ========== Mount UI ==========
-from fastapi.middleware.cors import CORSMiddleware
-app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
-app = gr.mount_gradio_app(app, demo, path="/ui")
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+# Full working app content will go here.

knowledge/state.json

@@ -1,19 +1 @@
-{
-  "knowledge_base": {
-    "name": "AutonomousAI_KnowledgeBase",
-    "description": "Used by the autonomous AI system for context and reasoning.",
-    "created_at": "2025-06-26T12:00:00Z",
-    "last_updated": "2025-06-26T12:00:00Z",
-    "total_documents": 0,
-    "total_tokens": 0,
-    "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
-    "vector_store": {
-      "type": "FAISS",
-      "index_file": "vector_index/faiss.index",
-      "dimension": 384,
-      "metric": "cosine"
-    },
-    "memory_enabled": true,
-    "status": "initialized"
-  }
-}
+{}

memory.py

@@ -1,33 +1,5 @@
-import json, os
-
-MEMORY_FILE = "memory.json"
-
-def init_memory():
-    if not os.path.exists(MEMORY_FILE):
-        with open(MEMORY_FILE, "w") as f:
-            json.dump({}, f)
-    with open(MEMORY_FILE, "r") as f:
-        return json.load(f)
-
-def save_memory(memory):
-    with open(MEMORY_FILE, "w") as f:
-        json.dump(memory, f, indent=2)
-
-def add_to_memory(session_id, prompt, response):
-    memory = init_memory()
-    memory.setdefault(session_id, [])
-    memory[session_id].append({"prompt": prompt, "response": response})
-    memory[session_id] = memory[session_id][-50:]
-    save_memory(memory)
-
-def get_memory(session_id):
-    return init_memory().get(session_id, [])
-
-def get_summary(session_id, max_items=3):
-    history = get_memory(session_id)
-    return history[-max_items:] if history else []
-
-def search_memory(session_id, keyword):
-    history = get_memory(session_id)
-    results = [entry for entry in history if keyword.lower() in entry.get("prompt", "").lower()]
-    return results
+def init_memory(): return {'texts': []}
+def add_to_memory(sid, prompt, response): pass
+def get_memory(sid=None): return []
+def get_summary(sid): return 'summary'
+def search_memory(q, mem): return []

src/core/cognitive_engine.py

@@ -1,16 +1,4 @@
 class CognitiveEngine:
-    def __init__(self, model):
-        self.model = model
-
-    def identify_improvements(self, task_description):
-        prompt = f"Suggest improvements:\n\nTask: {task_description}"
-        return self.model(prompt).split("\n")
-
-    def generate_enhancements(self, improvements):
-        prompt = "Generate code for improvements:\n" + "\n".join(improvements)
-        return self.model(prompt)
-
-    def apply_enhancements(self, code):
-        print("Applying enhancements...")
-        print(code)
-        return True
+    def identify_improvements(self, task): return ['Fix bug']
+    def generate_enhancements(self, targets): return 'def fix(): pass'
+    def apply_enhancements(self, code): return True

src/utils/hf_packager.py

@@ -1,22 +1,2 @@
-# src/utils/hf_packager.py
-
-import os
-from datetime import datetime
-
 class HFSpacePackager:
-    def __init__(self, snapshot_dir="snapshots"):
-        self.snapshot_dir = snapshot_dir
-        os.makedirs(snapshot_dir, exist_ok=True)
-
-    def create_snapshot(self, data=None):
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        filename = f"snapshot_{timestamp}.txt"
-        filepath = os.path.join(self.snapshot_dir, filename)
-
-        with open(filepath, "w") as f:
-            f.write("Snapshot created.\n")
-            if data:
-                f.write(str(data))
-
-        return f"https://huggingface.co/spaces/your-username/your-space-name/blob/main/{self.snapshot_dir}/{filename}"
-
+    def create_snapshot(self, data=None): return 'https://fake.snapshot.url'