Update app.py

app.py

@@ -1,92 +1,390 @@
-# autonomous_ai/app.py
-
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from utils import (
-    add_to_memory,
-    retrieve_memories,
-    reset_memory,
-    log_chat,
-    generate_suggestions
-)
-
-# === Chat Model ===
-CHAT_MODEL = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
-chat_tokenizer = AutoTokenizer.from_pretrained(CHAT_MODEL)
-chat_model = AutoModelForCausalLM.from_pretrained(CHAT_MODEL).eval()
-
-
-def generate_response(user_input, history, temperature, max_tokens, persona):
-    add_to_memory(user_input)
-    memories = retrieve_memories(user_input)
-    context = "\n".join(f"Memory: {m}" for m in memories)
-
-    prefix = f"You are {persona}, a helpful AI assistant.\n"
-    prompt = prefix
-    if context:
-        prompt += context + "\n"
-    for u, a in history:
-        prompt += f"User: {u}\nAssistant: {a}\n"
-    prompt += f"User: {user_input}\nAssistant:"
-
-    inputs = chat_tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)
-    output = chat_model.generate(
-        **inputs,
-        max_new_tokens=max_tokens,
-        temperature=temperature,
-        do_sample=True,
-        top_p=0.9,
-        pad_token_id=chat_tokenizer.eos_token_id,
-        eos_token_id=chat_tokenizer.eos_token_id
-    )
-
-    reply = chat_tokenizer.decode(
-        output[0][inputs['input_ids'].shape[-1]:],
-        skip_special_tokens=True
-    ).strip()
-
-    history.append((user_input, reply))
-    add_to_memory(reply)
-    log_chat(user_input, reply, persona)
-
-    return "", history
-
-def reset_all():
-    reset_memory()
-    return [], []
-
-
-def feedback_suggestions(feedback_text):
-    return generate_suggestions(feedback_text)
-
-
-with gr.Blocks(title="Autonomous AI vX") as demo:
-    gr.Markdown("# 🤖 Autonomous AI vX (CPU Only)")
-
-    with gr.Tab("Chat"):
-        chatbot = gr.Chatbot(label="Memory-Enabled Chat")
-        user_input = gr.Textbox(label="Message", placeholder="Ask something...", lines=2)
-        persona = gr.Dropdown([
-            "a curious child", "a formal tutor", "a sarcastic genius", "a helpful assistant"
-        ], value="a helpful assistant", label="Assistant Persona")
-        temperature = gr.Slider(0.1, 1.2, value=0.7, label="Creativity")
-        max_tokens = gr.Slider(50, 300, value=150, label="Response Length")
-        send = gr.Button("Send")
-        clear = gr.Button("Reset All Memory")
-
-        state = gr.State([])
-
-        send.click(generate_response, 
-                   inputs=[user_input, state, temperature, max_tokens, persona], 
-                   outputs=[user_input, chatbot])
-        clear.click(reset_all, outputs=[chatbot, state])
-
-    with gr.Tab("Feedback"):
-        feedback_input = gr.Textbox(label="Your Feedback", lines=3)
-        suggest_btn = gr.Button("Get Improvement Suggestions")
-        suggestions = gr.Textbox(label="AI Suggestions", lines=6)
-
-        suggest_btn.click(fn=feedback_suggestions, inputs=feedback_input, outputs=suggestions)
-
-if __name__ == "__main__":
-    demo.launch(share=True)
+import threading
+import queue
+import time
+import logging
+from typing import List, Dict, Any, Optional
+import random
+
+# 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)
+
+    def anonymize_and_store(self, data: