Update final_codettes_chatbot.py

final_codettes_chatbot.py

@@ -1,203 +1,28 @@
-import asyncio
-import json
-import os
 import logging
-import sqlite3
-from typing import List
+import gradio as gr
+import asyncio
+from codette_reasoning import UniversalReasoning, load_json_config, setup_logging
 
-# 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)
+class HuggingFaceChatbot:
+    def __init__(self):
+        config = load_json_config("config.json")
+        setup_logging(config)
+        self.reasoning_engine = UniversalReasoning(config)
 
-# Import perspectives
-from perspectives import (
-    NewtonPerspective, DaVinciPerspective, HumanIntuitionPerspective,
-    NeuralNetworkPerspective, QuantumComputingPerspective, ResilientKindnessPerspective,
-    MathematicalPerspective, PhilosophicalPerspective, CopilotPerspective, BiasMitigationPerspective
-)
+    def setup_interface(self):
+        async def chatbot_logic(user_input: str) -> str:
+            return await self.reasoning_engine.generate_response(user_input)
 
-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'
+        text_interface = gr.Interface(
+            fn=chatbot_logic,
+            inputs=gr.Textbox(label="Ask Codette Anything"),
+            outputs=gr.Textbox(label="Codette's Thoughts"),
+            title="🧠 Codette: Multimodal Reasoning Chatbot"
         )
-    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.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")
-        ]
-
-    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')
+        return text_interface
 
-    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')
+    def launch(self):
+        app = self.setup_interface()
+        app.launch()