Upload 9 files

agent_editor.py

@@ -0,0 +1,12 @@
+
+def update_agent_code(new_code):
+    with open("agent.py", "w") as f:
+        f.write(new_code)
+    return "✅ agent.py updated. Rebuild or reload your Space to apply changes."
+
+def read_agent_code():
+    try:
+        with open("agent.py", "r") as f:
+            return f.read()
+    except FileNotFoundError:
+        return "# agent.py not found"

app.py

@@ -1,9 +1,16 @@
 
 import gradio as gr
 from agent import autonomous_agent
-from utils import read_memory
+from utils import read_memory, append_feedback
+from agent_editor import update_agent_code, read_agent_code
+import threading, time
+
+current_task = ""
+loop_running = False
 
 def run_task(task):
+    global current_task
+    current_task = task
     result = autonomous_agent(task)
     return result, read_memory()
 
@@ -17,28 +24,46 @@ def view_logs():
     log_entries = []
     if not os.path.exists("logs"):
         return "No logs yet."
-    for filename in sorted(os.listdir("logs"))[-5:]:  # Last 5 logs
+    for filename in sorted(os.listdir("logs"))[-5:]:
         with open(f"logs/{filename}", "r") as f:
             data = json.load(f)
             log_entries.append(f"🕒 {data['timestamp']}\n🔧 Task: {data['task']}\n✅ Result: {data['result'][:100]}...\n---")
     return "\n\n".join(log_entries) if log_entries else "No logs found."
 
+def store_feedback(score):
+    append_feedback(current_task, "See logs for result", score)
+    return "✅ Feedback stored."
+
+def toggle_loop(toggle: bool, interval: int = 30):
+    global loop_running
+    loop_running = toggle
+    if toggle:
+        thread = threading.Thread(target=loop_runner, args=(interval,), daemon=True)
+        thread.start()
+        return "🔁 Loop started."
+    else:
+        return "⏹️ Loop stopped."
+
+def loop_runner(interval):
+    global loop_running
+    while loop_running:
+        run_task("Autonomous self-improvement")
+        time.sleep(interval)
+
 with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 Autonomous AI Agent")
+    gr.Markdown("# 🤖 Autonomous AI Agent — Enhanced")
 
     with gr.Tab("💬 Task Runner"):
         chatbot = gr.Textbox(lines=2, label="What should the AI do?")
         submit = gr.Button("Execute")
         output = gr.Textbox(label="Result")
         memory_out = gr.Textbox(label="Memory Snapshot", lines=6)
-
         submit.click(run_task, inputs=chatbot, outputs=[output, memory_out])
 
     with gr.Tab("🧠 Memory Editor"):
         mem_editor = gr.Textbox(label="Edit memory.txt", lines=10)
         save_mem = gr.Button("Save Changes")
         result = gr.Textbox(label="Status")
-
         save_mem.click(update_memory, inputs=mem_editor, outputs=result)
         demo.load(read_memory, inputs=[], outputs=mem_editor)
 
@@ -48,4 +73,46 @@ with gr.Blocks() as demo:
         refresh_logs.click(view_logs, outputs=log_output)
         demo.load(view_logs, outputs=log_output)
 
+    with gr.Tab("⭐ Feedback"):
+        gr.Markdown("Rate the last task:")
+        feedback_slider = gr.Slider(0, 10, step=0.5, label="Rating")
+        submit_fb = gr.Button("Submit Feedback")
+        fb_result = gr.Textbox(label="Status")
+        submit_fb.click(store_feedback, inputs=feedback_slider, outputs=fb_result)
+
+    with gr.Tab("🔁 Loop Mode"):
+        interval = gr.Slider(10, 300, step=10, label="Loop interval (seconds)", value=60)
+        toggle = gr.Checkbox(label="Enable Autonomous Loop")
+        loop_status = gr.Textbox(label="Loop Status")
+        toggle.change(fn=toggle_loop, inputs=[toggle, interval], outputs=loop_status)
+
+    with gr.Tab("🧬 Agent Code Editor"):
+        code_box = gr.Code(label="Edit agent.py")
+        code_status = gr.Textbox(label="Update Result")
+        code_save = gr.Button("Save agent.py")
+        demo.load(read_agent_code, outputs=code_box)
+        code_save.click(update_agent_code, inputs=code_box, outputs=code_status)
+
 demo.launch()
+
+import gradio as gr
+from goal_manager import save_goal, list_goals
+from multi_agent import multi_agent_brain
+
+with demo:
+    with gr.Tab("🎯 Goal Manager"):
+        goal_input = gr.Textbox(label="Set a New Goal")
+        goal_status = gr.Textbox(label="Save Result")
+        show_goals = gr.Textbox(label="Latest Goals", lines=8)
+        save_button = gr.Button("Save Goal")
+        refresh_goals = gr.Button("Show Recent Goals")
+
+        save_button.click(fn=save_goal, inputs=goal_input, outputs=goal_status)
+        refresh_goals.click(fn=list_goals, outputs=show_goals)
+        demo.load(fn=list_goals, outputs=show_goals)
+
+    with gr.Tab("🤖 Multi-Agent Collaboration"):
+        group_task = gr.Textbox(label="Shared Task")
+        run_group = gr.Button("Collaborate")
+        group_output = gr.Textbox(label="Agent Responses", lines=6)
+        run_group.click(fn=multi_agent_brain, inputs=group_task, outputs=group_output)

goal_manager.py

@@ -0,0 +1,20 @@
+
+import json, os
+
+def save_goal(goal):
+    os.makedirs("goals", exist_ok=True)
+    goal_data = {
+        "goal": goal,
+        "status": "pending"
+    }
+    with open(f"goals/{int(time.time())}.json", "w") as f:
+        json.dump(goal_data, f)
+
+def list_goals():
+    goal_files = sorted(os.listdir("goals")) if os.path.exists("goals") else []
+    goals = []
+    for gf in goal_files[-10:]:  # Show last 10 goals
+        with open(f"goals/{gf}", "r") as f:
+            data = json.load(f)
+            goals.append(f"🎯 {data['goal']} — [{data['status']}]")
+    return "\n".join(goals) if goals else "No goals yet."

multi_agent.py

@@ -0,0 +1,11 @@
+
+import random
+
+def multi_agent_brain(task):
+    agents = [agent1, agent2, agent3]
+    results = [a(task) for a in agents]
+    return f"🤝 Multiple agents responded:\n" + "\n---\n".join(results)
+
+def agent1(task): return f"Agent Alpha completed: {task.upper()}"
+def agent2(task): return f"Agent Beta handled: {task[::-1]}"
+def agent3(task): return f"Agent Gamma response: {task.lower()}"

utils.py

@@ -35,3 +35,15 @@ def run_code(code):
     local_vars = {}
     exec(code, {}, local_vars)
     return "Code executed successfully."
+
+def append_feedback(task, result, feedback_score):
+    import json, os, time
+    feedback_entry = {
+        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+        "task": task,
+        "result": result,
+        "feedback": feedback_score
+    }
+    os.makedirs("feedback", exist_ok=True)
+    with open(f"feedback/{int(time.time())}.json", "w") as f:
+        json.dump(feedback_entry, f, indent=2)