C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\transformers\data\metrics\__init__.py:19: UserWarning: A NumPy version >=1.22.4 and <1.29.0 is required for this version of SciPy (detected version 2.0.2)
  from scipy.stats import pearsonr, spearmanr
Traceback (most recent call last):
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\transformers\utils\import_utils.py", line 1778, in _get_module
    return importlib.import_module("." + module_name, self.__name__)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\importlib\__init__.py", line 90, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<frozen importlib._bootstrap>", line 1387, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1360, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1331, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 935, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 995, in exec_module
  File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\transformers\pipelines\__init__.py", line 64, in <module>
    from .document_question_answering import DocumentQuestionAnsweringPipeline
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\transformers\pipelines\document_question_answering.py", line 29, in <module>
    from .question_answering import select_starts_ends
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\transformers\pipelines\question_answering.py", line 9, in <module>
    from ..data import SquadExample, SquadFeatures, squad_convert_examples_to_features
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\transformers\data\__init__.py", line 27, in <module>
    from .metrics import glue_compute_metrics, xnli_compute_metrics
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\transformers\data\metrics\__init__.py", line 19, in <module>
    from scipy.stats import pearsonr, spearmanr
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\scipy\stats\__init__.py", line 605, in <module>
    from ._stats_py import *
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\scipy\stats\_stats_py.py", line 37, in <module>
    from scipy.spatial.distance import cdist
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\scipy\spatial\__init__.py", line 110, in <module>
    from ._kdtree import *
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\scipy\spatial\_kdtree.py", line 4, in <module>
    from ._ckdtree import cKDTree, cKDTreeNode
  File "_ckdtree.pyx", line 1, in init scipy.spatial._ckdtree
ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "D:\Download\Python\night2.py", line 6, in <module>
    from transformers import pipeline
  File "<frozen importlib._bootstrap>", line 1412, in _handle_fromlist
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\transformers\utils\import_utils.py", line 1766, in __getattr__
    module = self._get_module(self._class_to_module[name])
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\v\AppData\Local\Programs\Python\Python312\Lib\site-packages\transformers\utils\import_utils.py", line 1780, in _get_module
    raise RuntimeError(
RuntimeError: Failed to import transformers.pipelines because of the following error (look up to see its traceback):
numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject

```
import g4f
from g4f.Provider import Blackbox
import asyncio
import logging
import spacy
from transformers import pipeline
from collections import Counter
from typing import List, Tuple, Dict
import multiprocessing
from multiprocessing import Pool
import yaml
import os

# Load configuration from a YAML file
CONFIG_PATH = 'config.yaml'

if not os.path.exists(CONFIG_PATH):
    # Default configuration
    default_config = {
        'providers': ['Blackbox'],
        'num_workers': 5,
        'log_file': 'chatbot.log',
        'nlp_model': 'en_core_web_sm',
        'summarization_model': 'facebook/bart-large-cnn',
        'reasoning_steps': 4,
        'max_history': 5
    }
    with open(CONFIG_PATH, 'w') as file:
        yaml.dump(default_config, file)

with open(CONFIG_PATH, 'r') as file:
    config = yaml.safe_load(file)

# Initialize spaCy model for advanced NLP tasks
try:
    nlp = spacy.load(config['nlp_model'])
except OSError:
    import subprocess
    subprocess.run(["python", "-m", "spacy", "download", config['nlp_model']])
    nlp = spacy.load(config['nlp_model'])

# Initialize summarization pipeline
summarizer = pipeline("summarization", model=config['summarization_model'])

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s",
    handlers=[
        logging.FileHandler(config['log_file']),
        logging.StreamHandler()
    ]
)

class AdvancedChatbot:
    def __init__(self):
        # List of available providers dynamically loaded based on config
        self.providers = self.load_providers(config['providers'])
        
        # Chat history
        self.conversation_history: List[dict] = []
        
        # Provider rotation index
        self.current_provider_index = 0

        # Initialize AI workers pool
        self.num_workers = config['num_workers']  # Number of AI workers
        self.worker_responses: List[Tuple[int, str]] = []
        
        # Initialize multiprocessing pool for reasoning
        self.pool = Pool(processes=multiprocessing.cpu_count())
        
    def load_providers(self, provider_names: List[str]):
        """Dynamically load AI providers based on provider names."""
        available_providers = []
        provider_mapping = {
            'Blackbox': Blackbox
        }
        for name in provider_names:
            provider = provider_mapping.get(name)
            if provider:
                available_providers.append(provider)
            else:
                logging.warning(f"Provider '{name}' is not recognized and will be skipped.")
        if not available_providers:
            logging.error("No valid providers available. Exiting.")
            raise ValueError("No valid AI providers loaded.")
        return available_providers
    
    def get_next_provider(self):
        """Rotate through providers."""
        provider = self.providers[self.current_provider_index]
        self.current_provider_index = (self.current_provider_index + 1) % len(self.providers)
        logging.debug(f"Selected provider: {provider.__name__}")
        return provider
    
    def analyze_sentiment(self, text: str) -> str:
        """Analyze the sentiment of the user input."""
        doc = nlp(text)
        # Placeholder for sentiment analysis; replace with actual analysis if needed
        # Example with TextBlob or VADER for improved sentiment
        sentiment_score = "Neutral"  # Implement actual sentiment analysis
        logging.debug(f"Sentiment analysis result: {sentiment_score}")
        return sentiment_score
    
    def extract_entities(self, text: str) -> List[Tuple[str, str]]:
        """Extract named entities from the user input."""
        doc = nlp(text)
        entities = [(ent.text, ent.label_) for ent in doc.ents]
        logging.debug(f"Extracted entities: {entities}")
        return entities
    
    def resolve_coreferences(self, text: str) -> str:
        """Resolve coreferences in the text for better context understanding."""
        # Example implementation using neuralcoref if available
        # For simplicity, returning the text as-is
        return text
    
    def construct_reasoning_steps(self, prompt: str, sentiment: str, entities: List[Tuple[str, str]]) -> List[str]:
        """Construct reasoning steps based on sentiment and entities."""
        steps = [
            "Interpret the user's intent clearly.",
            "Analyze the emotional tone of the input.",
            "Identify and understand key entities and their relationships.",
            "Synthesize the information to formulate a coherent and comprehensive response."
        ][:config['reasoning_steps']]
        logging.debug("Constructed reasoning steps.")
        return steps
    
    def generate_chain_of_thought(self, prompt: str) -> str:
        """Generate a multi-step chain-of-thought reasoning prompt."""
        sentiment = self.analyze_sentiment(prompt)
        entities = self.extract_entities(prompt)
        resolved_prompt = self.resolve_coreferences(prompt)
        reasoning_steps = self.construct_reasoning_steps(resolved_prompt, sentiment, entities)
        
        cot_prompt = (
            "You are an advanced AI assistant with hierarchical reasoning abilities. "
            "Engage in deep meta-thinking and provide a detailed chain-of-thought leading to the final answer.\n\n"
            f"User's question: {resolved_prompt}\n\n"
            f"Sentiment Analysis: {sentiment}\n"
            f"Extracted Entities: {entities}\n"
            f"Reasoning Steps: {reasoning_steps}\n\n"
            "Your detailed reasoning and answer:"
        )
        logging.debug("Generated chain-of-thought prompt.")
        return cot_prompt
    
    def reasoning_module(self, full_prompt: str) -> str:
        """Multiprocessed reasoning module to handle complex reasoning."""
        try:
            # Split the prompt into parts if needed and process
            response = g4f.ChatCompletion.create(
                model="gpt-4o",  # Using a more advanced model
                provider=self.get_next_provider(),
                messages=[{"role": "user", "content": full_prompt}],
                stream=False
            )
            logging.info("Reasoning module completed successfully.")
            return response
        except Exception as e:
            logging.error(f"Reasoning module encountered an error: {str(e)}")
            return f"An error occurred during reasoning: {str(e)}"
    
    async def ai_worker(self, prompt: str, worker_id: int):
        """An AI worker that processes the prompt asynchronously."""
        try:
            response = await asyncio.to_thread(self.reasoning_module, prompt)
            self.worker_responses.append((worker_id, response))
            logging.info(f"Worker {worker_id} received response.")
        except Exception as e:
            logging.error(f"Worker {worker_id} error: {str(e)}")
            self.worker_responses.append((worker_id, f"Worker {worker_id} encountered an error."))
    
    def aggregate_responses(self) -> str:
        """Aggregate responses from AI workers and produce a final answer."""
        if not self.worker_responses:
            logging.warning("No worker responses to aggregate.")
            return "I'm sorry, I couldn't generate a response at this time."
        
        # Collect and filter valid responses
        responses = [resp for _, resp in self.worker_responses if resp]
        
        if not responses:
            logging.warning("All worker responses are empty or invalid.")
            return "I'm sorry, I couldn't generate a response at this time."
        
        # Summarize responses using the summarizer pipeline
        combined_responses = " ".join(responses)
        try:
            summary = summarizer(combined_responses, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
            logging.info("Aggregated response using summarization.")
            return summary
        except Exception as e:
            logging.error(f"Summarization failed: {str(e)}")
            # Fallback to selecting the most common response
            response_counts = Counter(responses)
            most_common_response, count = response_counts.most_common(1)[0]
            logging.info(f"Falling back to most common response: {most_common_response}")
            return most_common_response
        
    async def chat(self, user_input: str) -> Tuple[str, str]:
        """Process user input and generate response with advanced reasoning."""
        # Add user input to history
        self.conversation_history.append({"role": "user", "content": user_input})
        logging.info(f"User input received: {user_input}")
        
        # Generate context-aware prompt with chain-of-thought
        context = "\n".join(
            [f"{msg['role'].capitalize()}: {msg['content']}" for msg in self.conversation_history[-config['max_history'] * 2:]]
        )  # Last 'max_history' turns for context
        
        thought_prompt = self.generate_chain_of_thought(user_input)
        full_prompt = f"Previous conversation:\n{context}\n\n{thought_prompt}"
        
        # Start AI workers
        self.worker_responses = []
        tasks = [self.ai_worker(full_prompt, i+1) for i in range(self.num_workers)]
        await asyncio.gather(*tasks)
        
        # Aggregate responses
        final_response = self.aggregate_responses()
        
        # Add response to history
        self.conversation_history.append({"role": "assistant", "content": final_response})
        logging.info(f"AI response generated: {final_response}")
        
        return final_response, "Advanced AI Workers"
    
    def clear_history(self) -> str:
        """Clear conversation history."""
        self.conversation_history = []
        logging.info("Conversation history cleared.")
        return "Conversation history cleared."

def initialize():
    """Initialize the chatbot and ensure all components are ready."""
    chatbot = AdvancedChatbot()
    return chatbot

async def main():
    print("Advanced AI Chatbot with Advanced Field of Thought and Multiprocessing Reasoning Initialized")
    print("Type 'quit' to exit or 'clear' to clear conversation history")
    print("-" * 50)
    
    chatbot = initialize()
    
    while True:
        # Use asyncio to get user input without blocking
        user_input = await asyncio.get_event_loop().run_in_executor(None, input, "\nYou: ")
        user_input = user_input.strip()
        
        if user_input.lower() == 'quit':
            print("Goodbye!")
            break
                
        if user_input.lower() == 'clear':
            print(chatbot.clear_history())
            continue
                
        if user_input:
            print("\nProcessing...")
            try:
                response, provider = await chatbot.chat(user_input)
                print(f"\nAI ({provider}):\n{response}")
            except Exception as e:
                logging.error(f"Error during chat processing: {str(e)}")
                print("\nAI: I'm sorry, an error occurred while processing your request.")
            print("-" * 50)

if __name__ == "__main__":
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nGoodbye!")