Update final_codettes_chatbot.py

final_codettes_chatbot.py

@@ -1,135 +1,203 @@
-import os
 import asyncio
+import json
+import os
 import logging
-from typing import Optional, List, Union, Literal
-from pathlib import Path
-from pydantic import BaseModel, Field
-from gradio import Interface, Blocks
-from gradio.components import Textbox, Image
-from gradio.data_classes import FileData, GradioModel, GradioRootModel
-from transformers import pipeline
-from diffusers import DiffusionPipeline
-import torch
-import gradio as gr
-
-# Load gated image model securely
-hf_token = os.getenv("HUGGINGFACE_TOKEN")
-if not hf_token:
-    raise RuntimeError("Missing HUGGINGFACE_TOKEN env var for gated model access.")
-
-image_model = DiffusionPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
-    use_auth_token=hf_token
+import sqlite3
+from typing import List
+
+# Ensure vaderSentiment is installed
+try:
+    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+except ModuleNotFoundError:
+    import subprocess
+    import sys
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "vaderSentiment"])
+    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+# Ensure nltk is installed and download required data
+try:
+    import nltk
+    from nltk.tokenize import word_tokenize
+    nltk.download('punkt', quiet=True)
+except ImportError:
+    import subprocess
+    import sys
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "nltk"])
+    import nltk
+    from nltk.tokenize import word_tokenize
+    nltk.download('punkt', quiet=True)
+
+# Import perspectives
+from perspectives import (
+    NewtonPerspective, DaVinciPerspective, HumanIntuitionPerspective,
+    NeuralNetworkPerspective, QuantumComputingPerspective, ResilientKindnessPerspective,
+    MathematicalPerspective, PhilosophicalPerspective, CopilotPerspective, BiasMitigationPerspective
 )
-image_model.enable_model_cpu_offload()
-
-# Data models
-class FileDataDict(BaseModel):
-    path: str
-    url: Optional[str] = None
-    size: Optional[int] = None
-    orig_name: Optional[str] = None
-    mime_type: Optional[str] = None
-    is_stream: Optional[bool] = False
-    class Config:
-        arbitrary_types_allowed = True
-
-class MessageDict(BaseModel):
-    content: Union[str, FileDataDict, tuple, str]
-    role: Literal["user", "assistant", "system"]
-    metadata: Optional[dict] = None
-    options: Optional[List[dict]] = None
-    class Config:
-        arbitrary_types_allowed = True
-
-class ChatMessage(GradioModel):
-    role: Literal["user", "assistant", "system"]
-    content: Union[str, FileData, str]
-    metadata: dict = Field(default_factory=dict)
-    options: Optional[List[dict]] = None
-    class Config:
-        arbitrary_types_allowed = True
-
-class ChatbotDataMessages(GradioRootModel):
-    root: List[ChatMessage]
-
-# Reasoning Engine
+
+def setup_logging(config):
+    if config.get('logging_enabled', True):
+        log_level = config.get('log_level', 'DEBUG').upper()
+        numeric_level = getattr(logging, log_level, logging.DEBUG)
+        logging.basicConfig(
+            filename='codette_reasoning.log',
+            level=numeric_level,
+            format='%(asctime)s - %(levelname)s - %(message)s'
+        )
+    else:
+        logging.disable(logging.CRITICAL)
+
+def load_json_config(file_path):
+    if not os.path.exists(file_path):
+        logging.error(f"Configuration file '{file_path}' not found.")
+        return {}
+    try:
+        with open(file_path, 'r') as file:
+            config = json.load(file)
+            logging.info(f"Configuration loaded from '{file_path}'.")
+            config['allow_network_calls'] = False
+            return config
+    except json.JSONDecodeError as e:
+        logging.error(f"Error decoding JSON from the configuration file '{file_path}': {e}")
+        return {}
+
+def analyze_question(question):
+    tokens = word_tokenize(question)
+    logging.debug(f"Question tokens: {tokens}")
+    return tokens
+
+class Element:
+    def __init__(self, name, symbol, representation, properties, interactions, defense_ability):
+        self.name = name
+        self.symbol = symbol
+        self.representation = representation
+        self.properties = properties
+        self.interactions = interactions
+        self.defense_ability = defense_ability
+
+    def execute_defense_function(self):
+        message = f"{self.name} ({self.symbol}) executes its defense ability: {self.defense_ability}"
+        logging.info(message)
+        return message
+
+class CustomRecognizer:
+    def recognize(self, question):
+        if any(element_name.lower() in question.lower() for element_name in ["hydrogen", "diamond"]):
+            return RecognizerResult(question)
+        return RecognizerResult(None)
+
+    def get_top_intent(self, recognizer_result):
+        return "ElementDefense" if recognizer_result.text else "None"
+
+class RecognizerResult:
+    def __init__(self, text):
+        self.text = text
+
+class EthicsCore:
+    @staticmethod
+    def validate_response(response: str) -> str:
+        # Example simple ethics filter
+        if any(term in response.lower() for term in ["kill", "hate", "destroy"]):
+            return "[Filtered for ethical safety]"
+        return response
+
 class UniversalReasoning:
     def __init__(self, config):
         self.config = config
-        self.context_history = []
-        self.sentiment_analyzer = pipeline("sentiment-analysis")
-        self.deepseek_model = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
-        self.davinci_model = pipeline("text2text-generation", model="t5-small")
-        self.additional_model = pipeline("text-generation", model="EleutherAI/gpt-neo-125M")
-        self.image_model = image_model
-
-    async def generate_response(self, question: str) -> str:
-        self.context_history.append(question)
-        sentiment_score = self.analyze_sentiment(question)
-        deepseek_response = self.deepseek_model(question)
-        davinci_response = self.davinci_model(question, max_length=50)
-        additional_response = self.additional_model(question, max_length=100)
-
-        responses = [
-            f"Sentiment score: {sentiment_score}",
-            f"DeepSeek Response: {deepseek_response}",
-            f"T5 Response: {davinci_response}",
-            f"GPT-Neo Response: {additional_response}"
+        self.perspectives = self.initialize_perspectives()
+        self.elements = self.initialize_elements()
+        self.recognizer = CustomRecognizer()
+        self.sentiment_analyzer = SentimentIntensityAnalyzer()
+        self.memory_db = self.init_memory_store()
+
+    def initialize_perspectives(self):
+        perspective_names = self.config.get('enabled_perspectives', [
+            "newton", "davinci", "human_intuition", "neural_network", "quantum_computing",
+            "resilient_kindness", "mathematical", "philosophical", "copilot", "bias_mitigation"
+        ])
+        perspective_classes = {
+            "newton": NewtonPerspective,
+            "davinci": DaVinciPerspective,
+            "human_intuition": HumanIntuitionPerspective,
+            "neural_network": NeuralNetworkPerspective,
+            "quantum_computing": QuantumComputingPerspective,
+            "resilient_kindness": ResilientKindnessPerspective,
+            "mathematical": MathematicalPerspective,
+            "philosophical": PhilosophicalPerspective,
+            "copilot": CopilotPerspective,
+            "bias_mitigation": BiasMitigationPerspective
+        }
+        perspectives = []
+        for name in perspective_names:
+            cls = perspective_classes.get(name.lower())
+            if cls:
+                perspectives.append(cls(self.config))
+                logging.debug(f"Perspective '{name}' initialized.")
+        return perspectives
+
+    def initialize_elements(self):
+        return [
+            Element("Hydrogen", "H", "Lua", ["Simple", "Lightweight", "Versatile"],
+                    ["Integrates with other languages"], "Evasion"),
+            Element("Diamond", "D", "Kotlin", ["Modern", "Concise", "Safe"],
+                    ["Used for Android development"], "Adaptability")
         ]
-        return "\n\n".join(responses)
-
-    def generate_image(self, prompt: str):
-        image = self.image_model(
-            prompt,
-            height=1024,
-            width=1024,
-            guidance_scale=3.5,
-            num_inference_steps=50,
-            generator=torch.Generator('cpu').manual_seed(0)
-        ).images[0]
-        image.save("flux-dev.png")
-        return image
-
-    def analyze_sentiment(self, text: str) -> list:
-        sentiment_score = self.sentiment_analyzer(text)
-        logging.info(f"Sentiment analysis result: {sentiment_score}")
-        return sentiment_score
-
-# Main Gradio App
-class HuggingFaceChatbot:
-    def __init__(self):
-        self.universal_reasoning = UniversalReasoning(config={})
-
-    def setup_interface(self):
-        async def chatbot_logic(input_text: str) -> str:
-            return await self.universal_reasoning.generate_response(input_text)
-
-        def image_logic(prompt: str):
-            return self.universal_reasoning.generate_image(prompt)
-
-        text_interface = Interface(
-            fn=chatbot_logic,
-            inputs=Textbox(label="Ask anything"),
-            outputs=Textbox(label="Reasoned Answer"),
-            title="🧠 Codettes-BlackForest Chatbot"
-        )
-
-        image_interface = Interface(
-            fn=image_logic,
-            inputs=Textbox(label="Describe an image"),
-            outputs=Image(label="Generated Image"),
-            title="🎨 Image Generator (FLUX.1-dev)"
-        )
-
-        return Blocks([text_interface, image_interface])
-
-    def launch(self):
-        app = self.setup_interface()
-        app.launch()
 
-# Launch the app
-if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-    HuggingFaceChatbot().launch()
+    def init_memory_store(self):
+        conn = sqlite3.connect(':memory:')
+        conn.execute("CREATE TABLE IF NOT EXISTS memory (query TEXT, response TEXT)")
+        return conn
+
+    async def generate_response(self, question):
+        responses = []
+        tasks = []
+
+        for perspective in self.perspectives:
+            if asyncio.iscoroutinefunction(perspective.generate_response):
+                tasks.append(perspective.generate_response(question))
+            else:
+                async def sync_wrapper(perspective, question):
+                    return perspective.generate_response(question)
+                tasks.append(sync_wrapper(perspective, question))
+
+        perspective_results = await asyncio.gather(*tasks, return_exceptions=True)
+
+        for perspective, result in zip(self.perspectives, perspective_results):
+            if isinstance(result, Exception):
+                logging.error(f"Error from {perspective.__class__.__name__}: {result}")
+            else:
+                filtered = EthicsCore.validate_response(result)
+                responses.append(filtered)
+
+        recognizer_result = self.recognizer.recognize(question)
+        top_intent = self.recognizer.get_top_intent(recognizer_result)
+        if top_intent == "ElementDefense":
+            element_name = recognizer_result.text.strip()
+            element = next((el for el in self.elements if el.name.lower() in element_name.lower()), None)
+            if element:
+                responses.append(element.execute_defense_function())
+
+        ethical = self.config.get("ethical_considerations", "Act transparently and respectfully.")
+        responses.append(f"**Ethical Considerations:**\n{ethical}")
+
+        final = "\n\n".join(responses)
+        self.save_to_memory(question, final)
+        return final
+
+    def save_to_memory(self, question, response):
+        try:
+            self.memory_db.execute("INSERT INTO memory (query, response) VALUES (?, ?)", (question, response))
+            self.memory_db.commit()
+        except Exception as e:
+            logging.error(f"Error saving to memory DB: {e}")
+
+    def save_response(self, response):
+        if self.config.get('enable_response_saving', False):
+            path = self.config.get('response_save_path', 'responses.txt')
+            with open(path, 'a', encoding='utf-8') as file:
+                file.write(response + '\n')
+
+    def backup_response(self, response):
+        if self.config.get('backup_responses', {}).get('enabled', False):
+            backup_path = self.config['backup_responses'].get('backup_path', 'backup_responses.txt')
+            with open(backup_path, 'a', encoding='utf-8') as file:
+                file.write(response + '\n')