Upload app.py

app.py

@@ -1,485 +1,24 @@
-import threading
-import queue
-import time
-import logging
-from typing import List, Dict, Any, Optional
-import random
+from fastapi import FastAPI
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import json
-from pathlib import Path
-from datetime import datetime
+from orchestrator import run_agents
+from memory import init_memory
 
-MODEL_NAME = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).to("cpu").eval()
+app = FastAPI()
+memory = init_memory()
 
-CHANGELOG_FILE = Path("changelog.json")
-CHATLOG_FILE = Path("chatlog.json")
+def agent_interface(goal):
+    return run_agents(goal, memory)
 
-def generate_reply(user_input, chat_history, temperature, max_tokens):
-    prompt = "You are a helpful AI assistant.\n"
-    for user_msg, bot_msg in chat_history:
-        prompt += f"User: {user_msg}\nAssistant: {bot_msg}\n"
-    prompt += f"User: {user_input}\nAssistant:"
+gr_interface = gr.Interface(
+    fn=agent_interface,
+    inputs=gr.Textbox(lines=2, placeholder="Describe your task...", label="Your Task"),
+    outputs=gr.Textbox(lines=20, label="Multi-Agent Output"),
+    title="🧠 Multi-Agent Autonomous AI System",
+    description="Planner → Executor → Critic | Contextual Memory | Logs | Expandable AI System"
+)
 
-    inputs = tokenizer(prompt, return_tensors="pt", padding=True)
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=max_tokens,
-        temperature=temperature,
-        do_sample=True,
-        top_p=0.9,
-        pad_token_id=tokenizer.eos_token_id or tokenizer.pad_token_id,
-        eos_token_id=tokenizer.eos_token_id,
-    )
-    reply = tokenizer.decode(outputs[0][inputs['input_ids'].shape[-1]:], skip_special_tokens=True).strip()
-    chat_history.append((user_input, reply))
+gr.mount_gradio_app(app, gr_interface, path="/gradio")
 
-    # Save chat log after each exchange
-    save_chat_log(chat_history)
-    return "", chat_history
-
-def save_chat_log(chat_history):
-    data = {
-        "timestamp": datetime.now().isoformat(),
-        "chat": chat_history,
-    }
-    if CHATLOG_FILE.exists():
-        old_data = json.loads(CHATLOG_FILE.read_text())
-    else:
-        old_data = []
-    old_data.append(data)
-    CHATLOG_FILE.write_text(json.dumps(old_data, indent=2))
-
-def generate_suggestions(feedback):
-    # Simple mock suggestions based on feedback input
-    # Replace with real model inference if desired
-    suggestions = [
-        f"Improve clarity on: {feedback[:50]}...",
-        "Add context awareness for better follow-up answers.",
-        "Enhance error handling for invalid inputs.",
-    ]
-    # Save suggestions to changelog file
-    save_changelog(suggestions)
-    return "\n- " + "\n- ".join(suggestions)
-
-def save_changelog(suggestions):
-    data = {
-        "timestamp": datetime.now().isoformat(),
-        "suggestions": suggestions,
-    }
-    if CHANGELOG_FILE.exists():
-        old_data = json.loads(CHANGELOG_FILE.read_text())
-    else:
-        old_data = []
-    old_data.append(data)
-    CHANGELOG_FILE.write_text(json.dumps(old_data, indent=2))
-
-with gr.Blocks(title="Autonomous AI Minimal with Self-Improve") as demo:
-    gr.Markdown("# 🤖 Autonomous AI with Feedback & Logging")
-
-    with gr.Tab("Chat"):
-        chatbot = gr.Chatbot(label="Chat")
-        user_input = gr.Textbox(placeholder="Type your message here...", lines=2, label="Your Message")
-        temperature = gr.Slider(0.1, 1.2, value=0.7, step=0.05, label="Temperature")
-        max_tokens = gr.Slider(50, 300, value=150, step=25, label="Max Tokens")
-        send_btn = gr.Button("Send")
-        reset_btn = gr.Button("Reset")
-
-        state = gr.State([])
-
-        send_btn.click(generate_reply, inputs=[user_input, state, temperature, max_tokens], outputs=[user_input, chatbot])
-        reset_btn.click(lambda: [], None, chatbot)
-        reset_btn.click(lambda: [], None, state)
-
-    with gr.Tab("Self-Improve"):
-        feedback = gr.Textbox(label="Enter feedback or observations", lines=3)
-        suggest_btn = gr.Button("Generate Suggestions")
-        suggestions = gr.Textbox(label="Improvement Suggestions", interactive=False, lines=6)
-
-        suggest_btn.click(generate_suggestions, inputs=feedback, outputs=suggestions)
-
-# Logging config for traceability
-logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s:%(message)s')
-
-# ----------------------------
-# 1. Memory Management Module
-# ----------------------------
-
-class MemoryManager:
-    def __init__(self):
-        self.short_term_memory = []
-        self.long_term_memory = []
-
-    def store_short_term(self, data: Any):
-        logging.info("Storing to short-term memory")
-        self.short_term_memory.append(data)
-
-    def store_long_term(self, data: Any):
-        logging.info("Storing to long-term memory")
-        self.long_term_memory.append(data)
-
-    def retrieve(self, query: str) -> List[Any]:
-        # Placeholder: sophisticated semantic retrieval (e.g. embeddings)
-        logging.info(f"Retrieving memory for query: {query}")
-        results = [item for item in self.long_term_memory if query.lower() in str(item).lower()]
-        return results
-
-# ----------------------------
-# 2. Multi-modal Processing Module
-# ----------------------------
-
-class MultiModalProcessor:
-    def process_text(self, text: str) -> Dict:
-        logging.info("Processing text input")
-        # Integrate NLP pipelines here (e.g., HuggingFace transformers)
-        return {"text": text.upper(), "tokens": text.split()}
-
-    def process_image(self, image_data: bytes) -> Dict:
-        logging.info("Processing image input")
-        # Placeholder for image feature extraction (e.g., CLIP, OpenCV)
-        return {"image_features": "image_vector_representation"}
-
-    def process_audio(self, audio_data: bytes) -> Dict:
-        logging.info("Processing audio input")
-        # Placeholder for speech-to-text or audio embeddings
-        return {"audio_features": "audio_vector_representation"}
-
-    def process_video(self, video_data: bytes) -> Dict:
-        logging.info("Processing video input")
-        # Placeholder for video frame extraction + feature analysis
-        return {"video_features": "video_vector_representation"}
-
-# ----------------------------
-# 3. Recursive Reasoning & Self-Prompting Agent
-# ----------------------------
-
-class RecursiveReasoningAgent:
-    def generate_prompts(self, context: Dict) -> List[str]:
-        logging.info("Generating reasoning prompts")
-        # Chain-of-thought prompt generation example
-        base_query = context.get("text", "No context")
-        return [f"Consider the implications of {base_query}", 
-                f"What assumptions are made about {base_query}?", 
-                f"How would changing {base_query} affect the outcome?"]
-
-    def refine_hypotheses(self, hypotheses: List[str]) -> str:
-        logging.info("Refining hypotheses")
-        # Simple heuristic: pick the longest hypothesis
-        if hypotheses:
-            return max(hypotheses, key=len)
-        return "No hypotheses to refine"
-
-# ----------------------------
-# 4. Goal Management and Planning
-# ----------------------------
-
-class GoalPlanner:
-    def __init__(self):
-        self.goals_queue = queue.Queue()
-
-    def add_goal(self, goal: str):
-        logging.info(f"Adding goal: {goal}")
-        self.goals_queue.put(goal)
-
-    def get_next_goal(self) -> Optional[str]:
-        try:
-            goal = self.goals_queue.get_nowait()
-            logging.info(f"Next goal: {goal}")
-            return goal
-        except queue.Empty:
-            logging.info("No goals remaining")
-            return None
-
-# ----------------------------
-# 5. Reinforcement Learning Module (Skeleton)
-# ----------------------------
-
-class ReinforcementLearningAgent:
-    def __init__(self):
-        self.q_table = {}  # state-action value store
-
-    def select_action(self, state: str) -> str:
-        # Simple random policy placeholder
-        action = random.choice(["explore", "exploit", "wait"])
-        logging.info(f"Selected action '{action}' for state '{state}'")
-        return action
-
-    def update_policy(self, state: str, action: str, reward: float):
-        # Placeholder for policy update, e.g. Q-learning
-        logging.info(f"Updating policy for state {state} and action {action} with reward {reward}")
-
-# ----------------------------
-# 6. Neural Architecture Search (NAS) Module
-# ----------------------------
-
-class NeuralArchitectureSearch:
-    def search(self) -> str:
-        # Placeholder for automated NAS optimization (complex in real life)
-        new_architecture = "NAS Architecture v" + str(random.randint(1, 10))
-        logging.info(f"Proposed new neural architecture: {new_architecture}")
-        return new_architecture
-
-# ----------------------------
-# 7. Bias Detection and Mitigation
-# ----------------------------
-
-class BiasMitigation:
-    def detect_bias(self, data: Any) -> bool:
-        # Placeholder bias detection logic
-        bias_found = "bias" in str(data).lower()
-        logging.info(f"Bias detected: {bias_found}")
-        return bias_found
-
-    def mitigate(self, data: Any) -> Any:
-        # Simple mitigation: redact or flag
-        if self.detect_bias(data):
-            return "[Content Redacted for Bias]"
-        return data
-
-# ----------------------------
-# 8. Multi-Agent Collaboration System
-# ----------------------------
-
-class Agent:
-    def __init__(self, name: str):
-        self.name = name
-
-    def execute(self, task: str) -> str:
-        logging.info(f"Agent {self.name} executing task: {task}")
-        return f"{self.name} completed {task}"
-
-class MultiAgentSystem:
-    def __init__(self, agents: List[Agent]):
-        self.agents = agents
-
-    def coordinate(self, task: str) -> List[str]:
-        results = []
-        for agent in self.agents:
-            result = agent.execute(task)
-            results.append(result)
-        return results
-
-# ----------------------------
-# 9. Explainability Module
-# ----------------------------
-
-class Explainability:
-    def generate_explanation(self, decision: str) -> str:
-        explanation = f"Decision '{decision}' was made based on heuristic rules and data analysis."
-        logging.info(f"Generated explanation: {explanation}")
-        return explanation
-
-# ----------------------------
-# 10. Ethics and Safety Guardrails
-# ----------------------------
-
-class EthicsSafety:
-    def check_compliance(self, output: str) -> bool:
-        # Placeholder ethical compliance check
-        compliant = "illegal" not in output.lower()
-        logging.info(f"Output compliance status: {compliant}")
-        return compliant
-
-    def filter_output(self, output: str) -> str:
-        if self.check_compliance(output):
-            return output
-        return "[Output blocked due to ethical concerns]"
-
-# ----------------------------
-# 11. Plugin and API Integration
-# ----------------------------
-
-class PluginManager:
-    def call_plugin(self, name: str, params: Dict[str, Any]) -> Any:
-        logging.info(f"Calling plugin '{name}' with params {params}")
-        # Placeholder: invoke external API or service
-        return f"Plugin '{name}' response with params {params}"
-
-# ----------------------------
-# 12. Multilingual Support and Translation
-# ----------------------------
-
-class LanguageSupport:
-    def detect_language(self, text: str) -> str:
-        # Very naive detection placeholder
-        if any(ord(c) > 128 for c in text):
-            lang = "non-en"
-        else:
-            lang = "en"
-        logging.info(f"Detected language: {lang}")
-        return lang
-
-    def translate(self, text: str, target_lang: str) -> str:
-        logging.info(f"Translating text to {target_lang}")
-        # Placeholder for translation, returns original
-        return text
-
-# ----------------------------
-# 13. Streaming Input and Adaptation
-# ----------------------------
-
-class StreamingProcessor:
-    def stream_input(self, data_stream: Any):
-        logging.info(f"Processing streaming data: {data_stream}")
-        # Placeholder for real-time processing logic
-
-# ----------------------------
-# 14. User Feedback Loop
-# ----------------------------
-
-class FeedbackLoop:
-    def receive_feedback(self, feedback: str):
-        logging.info(f"Received user feedback: {feedback}")
-        # Placeholder: integrate feedback to improve system
-
-# ----------------------------
-# 15. Deployment Automation
-# ----------------------------
-
-class DeploymentManager:
-    def deploy_system(self):
-        logging.info("Deploying system with auto-scaling and monitoring")
-        # Placeholder for actual deployment steps
-
-# ----------------------------
-# 16. Security and Privacy
-# ----------------------------
-
-class SecurityPrivacyManager:
-    def encrypt(self, data: str) -> str:
-        encrypted = f"encrypted({data})"
-        logging.info(f"Data encrypted")
-        return encrypted
-
-    def anonymize(self, data: str) -> str:
-        anonymized = f"anonymized({data})"
-        logging.info(f"Data anonymized")
-        return anonymized
-
-# ----------------------------
-# 17. Dashboard and Monitoring
-# ----------------------------
-
-class Dashboard:
-    def update_status(self, status: str):
-        logging.info(f"Dashboard status updated: {status}")
-
-# ----------------------------
-# 18. Auto Documentation and Logging
-# ----------------------------
-
-class AutoDocumentation:
-    def log_event(self, event: str):
-        logging.info(f"Logged event: {event}")
-
-# ----------------------------
-# 19. Personality & Tone Customization
-# ----------------------------
-
-class PersonalityModule:
-    def set_personality(self, personality: str):
-        logging.info(f"Personality set to: {personality}")
-
-# ----------------------------
-# 20. Robustness & Error Handling
-# ----------------------------
-
-class RobustnessManager:
-    def recover_from_error(self, error: Exception):
-        logging.error(f"Error occurred: {error}")
-        logging.info("Attempting recovery procedures")
-
-# ----------------------------
-# Main OmniAIvX Orchestrator
-# ----------------------------
-
-class OmniAIvX:
-    def __init__(self):
-        self.memory = MemoryManager()
-        self.multi_modal = MultiModalProcessor()
-        self.reasoning_agent = RecursiveReasoningAgent()
-        self.goal_planner = GoalPlanner()
-        self.rl_agent = ReinforcementLearningAgent()
-        self.nas = NeuralArchitectureSearch()
-        self.bias_mitigation = BiasMitigation()
-        self.multi_agent_system = MultiAgentSystem([Agent("RL-Agent"), Agent("NAS-Agent")])
-        self.explainability = Explainability()
-        self.ethics = EthicsSafety()
-        self.plugin_manager = PluginManager()
-        self.language_support = LanguageSupport()
-        self.streaming_processor = StreamingProcessor()
-        self.feedback_loop = FeedbackLoop()
-        self.deployment_manager = DeploymentManager()
-        self.security_privacy = SecurityPrivacyManager()
-        self.dashboard = Dashboard()
-        self.auto_doc = AutoDocumentation()
-        self.personality = PersonalityModule()
-        self.robustness = RobustnessManager()
-        self.task_queue = queue.Queue()
-
-    def process_input(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
-        self.auto_doc.log_event("Input received")
-        processed_data = {}
-
-        # Language detection and translation
-        if "text" in input_data:
-            lang = self.language_support.detect_language(input_data["text"])
-            translated_text = self.language_support.translate(input_data["text"], "en")
-            processed_data["text"] = self.multi_modal.process_text(translated_text)
-
-        if "image" in input_data:
-            processed_data["image"] = self.multi_modal.process_image(input_data["image"])
-
-        if "audio" in input_data:
-            processed_data["audio"] = self.multi_modal.process_audio(input_data["audio"])
-
-        if "video" in input_data:
-            processed_data["video"] = self.multi_modal.process_video(input_data["video"])
-
-        self.memory.store_short_term(processed_data)
-        self.dashboard.update_status("Input processed")
-        return processed_data
-
-    def run_reasoning_cycle(self, context: Dict[str, Any]) -> Dict[str, Any]:
-        prompts = self.reasoning_agent.generate_prompts(context)
-        refined = self.reasoning_agent.refine_hypotheses(prompts)
-        goal = self.goal_planner.get_next_goal()
-        if not goal:
-            return {"message": "No goals to process."}
-
-        action = self.rl_agent.select_action(goal)
-        self.rl_agent.update_policy(goal, action, reward=1.0)  # dummy reward
-        multi_agent_results = self.multi_agent_system.coordinate(goal)
-        explanation = self.explainability.generate_explanation(action)
-        safe_output = self.ethics.filter_output(action)
-
-        self.auto_doc.log_event(f"Goal '{goal}' executed with action '{action}'")
-        self.dashboard.update_status(f"Goal executed: {goal}")
-
-        return {
-            "action": safe_output,
-            "explanation": explanation,
-            "multi_agent_results": multi_agent_results,
-            "refined_prompt": refined
-        }
-
-    def update_architecture(self):
-        new_arch = self.nas.search()
-        self.auto_doc.log_event(f"Neural architecture updated to {new_arch}")
-        self.dashboard.update_status("Neural architecture updated")
-
-    def receive_feedback(self, feedback: str):
-        self.feedback_loop.receive_feedback(feedback)
-        self.auto_doc.log_event("User feedback processed")
-
-    def deploy(self):
-        self.deployment_manager.deploy_system()
-        self.auto_doc.log_event("System deployed")
-
-    def encrypt_and_store(self, data: str):
-        encrypted = self.security_privacy.encrypt(data)
-        self.memory.store_long_term(encrypted)
-if __name__ == "__main__":
-    demo.launch(share=True)
+@app.get("/")
+def root():
+    return {"message": "Multi-Agent AI system with logging and memory."}