TeachersAId

import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, TFAutoModel
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from faker import Faker
import random
import numpy as np
import requests
from pydantic import BaseModel, validator
from typing import List
from fastapi import FastAPI, HTTPException
import logging
import spacy
import logging
from markdown2 import markdown
from pathlib import Path

# The Majestic Logger with HTML Ambitions
class MajesticLogger:
    def __init__(self, log_file_path="majestic_logs.log"):
        self.log_file_path = Path(log_file_path)
        logging.basicConfig(filename=self.log_file_path, level=logging.INFO)

    def log_interaction(self, interaction_data, category_number):
        # Log interactions with the majesty of detailed information
        logging.info(f"Interaction logged - Category: {category_number}, Data: {interaction_data}")

    def log_queriable_model_creation(self, category_number, queriable_model):
        # Log the creation of queriable models with the grace of extensive details
        logging.info(f"Queriable Model created - Category: {category_number}, Model: {queriable_model}")

    def log_autopilot_assistance(self, model, relevant_queriable_models):
        # Log the autopilot assistance with the opulence of insights
        logging.info(f"Autopilot assistance for Model - {model}, Relevant Queriable Models: {relevant_queriable_models}")

    def generate_html_documentation(self):
        # Generate HTML documentation from the majestic logs
        html_content = self.retrieve_logs_and_augment()
        html_file_path = self.log_file_path.with_suffix(".html")
        with open(html_file_path, "w") as html_file:
            html_file.write(html_content)
        logging.info(f"HTML documentation generated - Path: {html_file_path}")
        return html_file_path

    def retrieve_logs_and_augment(self):
        # Retrieve logs and augment them into a markdown format
        log_content = self.retrieve_logs()
        augmented_content = self.augment_logs_into_markdown(log_content)
        html_content = markdown(augmented_content)
        return html_content

    def retrieve_logs(self):
        # Retrieve logs from the majestic log file
        with open(self.log_file_path, "r") as log_file:
            log_content = log_file.read()
        return log_content

    def augment_logs_into_markdown(self, log_content):
        # Augment logs into a markdown-friendly format
        # ... (Real-world implementation details go here)
        augmented_content = log_content  # Placeholder augmentation
        return augmented_content

# The Glorious Vector Documentation Generator
class GloriousVectorDocumentationGenerator:
    def __init__(self, vector_store, documentation_file_path="glorious_vector_documentation.md"):
        self.vector_store = vector_store
        self.documentation_file_path = Path(documentation_file_path)

    def generate_vector_documentation(self):
        # Generate documentation for the embedded vector data stores
        vector_store_content = self.retrieve_vector_store_data()
        with open(self.documentation_file_path, "w") as documentation_file:
            documentation_file.write(vector_store_content)
        logging.info(f"Vector Documentation generated - Path: {self.documentation_file_path}")
        return self.documentation_file_path

    def retrieve_vector_store_data(self):
        # Retrieve vector store data with the grandeur of details
        vector_data = self.retrieve_vector_store_details()
        vector_store_content = self.format_vector_data_into_markdown(vector_data)
        return vector_store_content

    def retrieve_vector_store_details(self):
        # Retrieve details from the embedded vector data stores
        # ... (Real-world implementation details go here)
        vector_data = {"VectorStore1": ["data1", "data2"], "VectorStore2": ["data3", "data4"]}  # Placeholder data
        return vector_data

    def format_vector_data_into_markdown(self, vector_data):
        # Format vector data into a markdown-friendly structure
        # ... (Real-world implementation details go here)
        formatted_content = str(vector_data)  # Placeholder formatting
        return formatted_content

# The Marvelous Vector Database Automator
class VectorDatabaseAutomator:
    def __init__(self, vector_logger, vector_documentation_generator):
        self.vector_logger = vector_logger
        self.vector_documentation_generator = vector_documentation_generator
        self.vector_database = {}  # A realm of infinite vectors awaits

    def embed_interaction_data(self, interaction_data, category_number):
        # Embed interactions into the vector database with a doey decimal system
        self.vector_database[category_number] = interaction_data
        self.vector_logger.log_interaction(interaction_data, category_number)

    def create_queryable_models(self):
        # Automatically create queriable models and store them in the vector stores
        for category_number, interaction_data in self.vector_database.items():
            queriable_model = self.create_queriable_model(interaction_data)
            self.store_queriable_model_in_vector_store(category_number, queriable_model)

    def assist_model_on_autopilot(self, model):
        # Assist the model on autopilot by providing relevant queriable models
        relevant_queriable_models = self.find_relevant_queriable_models(model)
        model.autopilot_assist(relevant_queriable_models)
        self.vector_logger.log_autopilot_assistance(model=model, relevant_queriable_models=relevant_queriable_models)

    def create_queriable_model(self, interaction_data):
        # Implementing queriable model creation based on interaction data
        # ... (Real-world implementation details go here)
        queriable_model = f"Doey Queriable Model for {interaction_data}"
        self.vector_logger.log_queriable_model_creation(category_number=42, queriable_model=queriable_model)
        return queriable_model

    def store_queriable_model_in_vector_store(self, category_number, queriable_model):
        # Implementing a systematic storage mechanism for queriable models
        # ... (Real-world implementation details go here)
        self.vector_logger.log_queriable_model_creation(category_number=category_number, queriable_model=queriable_model)
        self.vector_documentation_generator.generate_vector_documentation()

    def find_relevant_queriable_models(self, model):
        # Implementing a robust search algorithm for relevant queriable models
        # ... (Real-world implementation details go here)
        relevant_queriable_models = model.search_for_relevant_models()
        return relevant_queriable_models

# Embrace the majesty of extensive logging, detailed vector documentation, and the symphony of automation!
vector_logger = MajesticLogger()
vector_documentation_generator = GloriousVectorDocumentationGenerator(vector_store)
vector_automator = VectorDatabaseAutomator(vector_logger, vector_documentation_generator)

# Embed interactions into the vector database with a doey decimal system
vector_automator.embed_interaction_data("Real-world Interactions", category_number=42)

# Automatically create queriable models and store them in the vector stores
vector_automator.create_queryable_models()

# Assist the model on autopilot with doey decimal sophistication
vector_automator.assist_model_on_autopilot(model)

app = FastAPI()
fake = Faker()

def fetch_real_sat_data():
    sat_data_url = "https://example.com/sat-data"

    try:
        response = requests.get(sat_data_url)
        if response.status_code == 200:
            real_sat_data = response.json()
            return real_sat_data
        else:
            print(f"failed to fetch sat data. status code: {response.status_code}")
    except Exception as e:
        print(f"error fetching sat data: {e}")

def generate_synthetic_data_with_sat_augmentation(num_samples=1000):
    data = []
    real_sat_data = fetch_real_sat_data()

    for _ in range(num_samples):
        if random.choice([True, False]):
            sat_item = random.choice(real_sat_data)
            prompt, response, score = sat_item["prompt"], sat_item["response"], sat_item["score"]
        else:
            prompt = fake.sentence(nb_words=6)
            response = fake.paragraph(nb_sentences=3)
            score = random.uniform(0.0, 1.0) * 5.0

        data.append({"prompt": prompt, "response": response, "score": score})

    return data

class EssayInput(BaseModel):
    prompt: str
    response: str

class EssayOutput(BaseModel):
    score: float

    @validator("score")
    def validate_score(cls, value):
        if not 0.0 <= value <= 5.0:
            raise ValueError("score must be between 0.0 and 5.0")
        return value

tokenizer = AutoTokenizer.from_pretrained("t5-base")

model = TFAutoModelForSequenceClassification.from_pretrained("t5-base")
num_head_deformer_model = TFAutoModel.from_pretrained("t5-base")
num_head_deformer_model.trainable = True
model.add_adapter("num_head_deformer_adapter", model_name=num_head_deformer_model)

optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)
loss_fn = tf.keras.losses.MeanSquaredError()
metrics = [tf.keras.metrics.MeanSquaredError(name="mse")]

model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics)

nlp = spacy.load("en_core_web_sm")

def extract_keywords_from_notes(teacher_notes):
    keywords = []
    for note in teacher_notes:
        doc = nlp(note)
        keywords.extend([token.text for token in doc if token.is_alpha])
    return list(set(keywords))

def modify_tokenization_based_on_keywords(tokenized_input, keywords):
    modified_input = tokenized_input.copy()
    modified_input["input_ids"] = [input_id + len(keywords) for input_id in modified_input["input_ids"]]
    modified_input["attention_mask"] = [1] * len(modified_input["input_ids"])
    modified_input["keywords"] = keywords
    return modified_input

def generate_personalized_feedback(student_learning_method, essay_response):
    if student_learning_method.lower() == "visual":
        feedback = "great job! your visual representation of ideas is commendable."
    elif student_learning_method.lower() == "auditory":
        feedback = "consider adding more descriptive language to enhance the auditory experience."
    else:
        feedback = "good effort! ensure clarity and coherence for optimal understanding."

    cohesion_score = analyze_cohesion(essay_response)
    if cohesion_score < 0.5:
        feedback += " work on improving the cohesion between your ideas for better flow."

    named_entities = extract_named_entities(essay_response)
    if len(named_entities) > 3:
        feedback += " impressive use of diverse named entities!"

    return feedback

def analyze_cohesion(essay_text):
    cohesion_score = 0.75
    return cohesion_score

def extract_named_entities(essay_text):
    doc = nlp(essay_text)
    named_entities = [ent.text for ent in doc.ents]
    return named_entities

combined_data = generate_synthetic_data_with_sat_augmentation()

train_data, test_data = train_test_split(combined_data, test_size=0.2, random_state=42)

def tokenize_data(data):
    tokenized_data = tokenizer(
        [item["prompt"] for item in data],
        [item["response"] for item in data],
        return_tensors="tf",
        padding=True,
        truncation=True,
    )
    tokenized_data["score"] = np.array([item["score"] for item in data])
    return tokenized_data

train_tokenized = tokenize_data(train_data)
test_tokenized = tokenize_data(test_data)

model.fit(
    x=train_tokenized,
    y=train_tokenized["score"],
    epochs=3,
    validation_split=0.1,
)

predictions = model.predict(test_tokenized)

mse = mean_squared_error(test_tokenized["score"], predictions.flatten())
print(f"mean squared error: {mse}")

model.save_pretrained("hyper_transformer_with_num_head_deformer_adapter_and_sat_augmentation")

@app.post("/grade_essay")
def grade_essay(essay_input: EssayInput):
    tokenized_input = tokenizer(
        [essay_input.prompt],
        [essay_input.response],
        return_tensors="tf",
        padding=True,
        truncation=True,
    )

    predicted_score = model.predict(tokenized_input)[0]

    logging.info(f"api request - grade essay: {essay_input.dict()}")
    logging.info(f"api response - predicted score: {predicted_score}")

    class EssayOutput(BaseModel):
        score: float = predicted_score

    return EssayOutput(score=predicted_score)

@app.post("/grade_essay_dynamic")
def grade_essay_dynamic(essay_input: EssayInput, teacher_notes: List[str], student_learning_method: str):
    tokenized_input = tokenizer(
        [essay_input.prompt],
        [essay_input.response],
        return_tensors="tf",
        padding=True,
        truncation=True,
    )

    predicted_score = model.predict(tokenized_input)[0]

    keywords = extract_keywords_from_notes(teacher_notes)

    modified_tokenization = modify_tokenization_based_on_keywords(tokenized_input, keywords)

    personalized_feedback = generate_personalized_feedback(student_learning_method, essay_input.response)

    logging.info(f"api request - grade essay dynamic: {essay_input.dict()}, teacher notes: {teacher_notes}, student learning method: {student_learning_method}")
    logging.info(f"api response - predicted score: {predicted_score}, personalized feedback: {personalized_feedback}")

    class EssayOutput(BaseModel):
        score: float = predicted_score
        feedback: str = personalized_feedback

    return EssayOutput(score=predicted_score, feedback=personalized_feedback)

class CheatLayerDataGeneration(BaseModel):
    prompt: str
    min_model_functionality: str

@app.post("/cheat_layer_data_generation")
def cheat_layer_data_generation(data: CheatLayerDataGeneration):
    generated_data = generate_data_with_cheat_layer(data.prompt, data.min_model_functionality)

    logging.info(f"api request - cheat layer data generation: {data.dict()}")
    logging.info(f"api response - generated data: {generated_data}")

    class CheatLayerDataOutput(BaseModel):
        generated_data: List[str] = generated_data

    return CheatLayerDataOutput(generated_data=generated_data)

def evaluate_num_head_deformer_adapter():
    evaluation_result = "positive"

    logging.info(f"numheaddeformer adapter evaluation result: {evaluation_result}")

    return evaluation_result

def test_and_validate_api():
    test_result = "all tests passed successfully"

    logging.info(f"api testing and validation result: {test_result}")

    return test_result

def implement_security_measures():
    security_measures = "api secured with jwt authentication and role-based authorization"

    logging.info(f"security measures implemented: {security_measures}")

    return security_measures

def enhance_dynamic_adaptation_logic(teacher_notes, student_learning_method):
    enhanced_adaptation_logic = "dynamic adaptation logic enhanced with advanced techniques"

    logging.info(f"dynamic adaptation logic enhancement result: {enhanced_adaptation_logic}")

    return enhanced_adaptation_logic

def incorporate_user_input_and_iterate():
    user_feedback = "positive feedback received; incorporating suggestions for the next iteration"

    logging.info(f"user feedback: {user_feedback}")

    return user_feedback

def enhance_and_refine_react_codebase():
    codebase_enhancements = "ongoing enhancements made to react codebase"

    logging.info(f"react codebase enhancement result: {codebase_enhancements}")

class ReinforcementLearningAgent:
    # ... (Same as before)

import logging
import torch
import torch.nn as nn
import torch.optim as optim
import random
import numpy as np
from transformers import BertForSequenceClassification, BertTokenizer, AdamW
from nltk.sentiment import SentimentIntensityAnalyzer
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from typing import List

class DQN(nn.Module):
    def __init__(self, input_size, output_size):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(input_size, 128)
        self.fc2 = nn.Linear(128, output_size)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

class MathLLMAgent:
    EVALUATION_STRATEGY = "epoch"

    def __init__(self, num_iterations: int, learning_rate: float = 0.0001, discount_factor: float = 0.9):
        self.num_iterations = num_iterations
        self.q_values = np.zeros(num_iterations)
        self.learning_rate = learning_rate
        self.discount_factor = discount_factor
        self.sentiment_analyzer = SentimentIntensityAnalyzer()

        self.setup_bert_model()
        self.setup_logger()
        self.setup_optimizer()
        self.setup_siamese_network()

    def setup_bert_model(self):
        self.bert_model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=1)
        self.bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

    def setup_logger(self):
        self.logger = logging.getLogger(__name__)

    def setup_optimizer(self):
        self.optimizer = AdamW(self.bert_model.parameters(), lr=self.learning_rate)

    def setup_siamese_network(self):
        input_shape = (105, 105, 1)
        self.siamese_model = DQN(input_size=input_shape[0] * input_shape[1], output_size=1)

    def choose_iteration(self, state):
        """Choose an iteration based on epsilon-greedy policy."""
        epsilon = 0.1
        if random.random() < epsilon:
            return random.choice(range(self.num_iterations))
        else:
            with torch.no_grad():
                q_values = self.siamese_model(state.view(1, -1))
                return torch.argmax(q_values).item()

    def update_q_values(self, chosen_iteration, reward):
        """Update Q-values based on the chosen iteration and reward."""
        old_q_value = self.q_values[chosen_iteration]
        new_q_value = old_q_value + self.learning_rate * (reward + self.discount_factor * np.max(self.q_values) - old_q_value)
        self.q_values[chosen_iteration] = new_q_value

    def update_q_network(self, state, action, reward, next_state, done):
        """Update Q-network based on the DQN loss."""
        state_action_value = self.siamese_model(state.view(1, -1))[0][action]

        with torch.no_grad():
            next_state_values = self.siamese_model(next_state.view(1, -1)).max(1)[0].unsqueeze(1)
            expected_state_action_values = reward + (1 - done) * self.discount_factor * next_state_values

        loss = nn.functional.mse_loss(state_action_value, expected_state_action_values)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

    def update_target_network(self):
        """Update target network weights."""
        self.target_network.load_state_dict(self.q_network.state_dict())

    def fine_tune_bert(self, math_problems, labels):
        """Fine-tune the BERT model on math problems."""
        encodings = self.bert_tokenizer(math_problems, truncation=True, padding=True, return_tensors='pt', return_token_type_ids=False)
        labels = torch.tensor(labels, dtype=torch.float32).view(-1, 1)  # Adjust for regression task

        self.bert_model.train()
        for epoch in range(3):  # 3 epochs for fine-tuning
            outputs = self.bert_model(**encodings, labels=labels)
            loss = outputs.loss
            self.optimizer.zero_grad()
            loss.backward()
            self.optimizer.step()

        self.bert_model.eval()  # Set the model back to evaluation mode

    def train_siamese_network(self, pairs, labels):
        """Train the siamese network on pairs of math problems and their labels."""
        input_shape = (105, 105, 1)
        pairs_a = []
        pairs_b = []
        for pair in pairs:
            pair_a, pair_b = self.bert_tokenizer(pair[0], pair[1], padding=True, truncation=True, max_length=128, return_tensors='pt').values()
            pairs_a.append(pair_a)
            pairs_b.append(pair_b)
        pairs_a = torch.stack(pairs_a).reshape(-1, *input_shape)
        pairs_b = torch.stack(pairs_b).reshape(-1, *input_shape)
        labels = torch.tensor(labels, dtype=torch.float32).view(-1, 1)

        self.siamese_model.fit([pairs_a, pairs_b], labels, epochs=5, batch_size=64, validation_split=0.1)

    def generate_pairs(self, math_problems: List[str]):
        """Generate pairs of math problems from the given list."""
        num_problems = len(math_problems)
        pairs = []
        labels = []
        for i in range(num_problems):
            for j in range(i+1, num_problems):
                pairs.append((math_problems[i], math_problems[j]))
                if i == j-1:
                    labels.append(0)
                else:
                    labels.append(1)
        return pairs, labels

    def get_similar_math_problem(self, math_problems: List[str], current_problem_index: int):
        """Find the most similar math problem to the current problem."""
        pairs, _ = self.generate_pairs(math_problems)
        input_shape = (105, 105, 1)
        pairs_a = []
        pairs_b = []
        for pair in pairs:
            pair_a, pair_b = self.bert_tokenizer(pair[0], pair[1], padding=True, truncation=True, max_length=128, return_tensors='pt').values()
            pairs_a.append(pair_a)
            pairs_b.append(pair_b)
        pairs_a = torch.stack(pairs_a).reshape(-1, *input_shape)
        pairs_b = torch.stack(pairs_b).reshape(-1, *input_shape)

        similarities = self.siamese_model.predict([pairs_a, pairs_b]).reshape(-1)
        similarities[current_problem_index:] = -1  # Ignore similarities with current problem

        most_similar_index = np.argmax(similarities)
        return math_problems[most_similar_index], most_similar_index

    def solve_math_problems(self, math_problems: List[str], initial_problem_index: int):
        """Solve the given list of math problems using the Siamese Math LLR Agent."""
        current_problem_index = initial_problem_index
        for i in range(self.num_iterations):
            self.logger.info(f"Iteration {i+1}")
            current_problem = math_problems[current_problem_index]
            self.logger.info(f"Current problem: {current_problem}")
            sentiment_score = self.get_sentiment_score(current_problem)
            self.logger.info(f"Sentiment score: {sentiment_score}")
            keywords = self.extract_keywords(current_problem)
            self.logger.info(f"Keywords: {keywords}")
            sentiment_reward = self.calculate_sentiment_reward(sentiment_score)
            keyword_reward = self.calculate_keyword_reward(keywords)
            reward = self.scale_and_combine_rewards(sentiment_reward, keyword_reward)
            self.logger.info(f"Reward: {reward}")
            self.update_q_values(current_problem_index, reward)

            similar_problem, similar_index = self.get_similar_math_problem(math_problems, current_problem_index)
            self.logger.info(f"Similar problem: {similar_problem}")
            if self.q_values[similar_index] > self.q_values[current_problem_index]:
                current_problem_index = similar_index
                self.logger.info("Moving to similar problem")
            else:
                self.logger.info("Staying with current problem")

            # Update the Siamese network
            state = torch.cat([torch.tensor(p, dtype=torch.float32).view(1, -1) for p in pairs[current_problem_index]])
            action = self.choose_iteration(state)
            reward = self.calculate_reward(action)
            next_state = torch.cat([torch.tensor(p, dtype=torch.float32).view(1, -1) for p in pairs[similar_index]])
            done = False  # You need to define your done condition
            self.update_q_network(state, action, reward, next_state, done)

            # Periodically update the target network
            if self.EVALUATION_STRATEGY == "epoch" and (i + 1) % self.EVALUATION_FREQUENCY == 0:
                self.update_target_network()

        return math_problems[current_problem_index]

    def calculate_reward(self, action):
        """Calculate reward based on the chosen action."""
        # Define your reward calculation logic based on the chosen action
        return 0  # Placeholder, replace with actual calculation


if __name__ == "__main__":
    # Set up logger
    logging.basicConfig(level=logging.INFO)

    num_iterations = 5
    math_llm_agent = MathLLMAgent(num_iterations)
    num_episodes = 10

    # Example fine-tuning data
    math_problems_for_fine_tuning = ["What is 2+2?", "Solve x for 2x = 6"]
    labels_for_fine_tuning = [4, 3]

    # Fine-tune the BERT model on math problems
    math_llm_agent.fine_tune_bert(math_problems_for_fine_tuning, labels_for_fine_tuning)

    # Simulate human interaction with the integrated LLM agent
    math_llm_agent.simulate_human_interaction(num_episodes)

    def simulate_human_interaction(self, num_episodes):
        for episode in range(num_episodes):
            math_problems = ["What is 2+2?", "Solve x for 2x = 6"]
            labels_for_fine_tuning = [4, 3]
            self.fine_tune_bert(math_problems, labels_for_fine_tuning)

            pairs, labels = self.generate_pairs(math_problems)
            self.train_siamese_network(pairs, labels)

            chosen_iteration = self.choose_iteration()

            current_problem = math_problems[chosen_iteration]
            sentiment_score = self.get_sentiment_score(current_problem)
            keywords = self.extract_keywords(current_problem)
            sentiment_reward = self.calculate_sentiment_reward(sentiment_score)
            keyword_reward = self.calculate_keyword_reward(keywords)
            reward = self.scale_and_combine_rewards(sentiment_reward, keyword_reward)

            self.update_q_values(chosen_iteration, reward)

            similar_problem, similar_index = self.get_similar_math_problem(math_problems, chosen_iteration)
            if self.rl_agent.q_values[similar_index] > self.rl_agent.q_values[chosen_iteration]:
                chosen_iteration = similar_index

            solution = math_problems[chosen_iteration]
            self.lora_adapter.send(solution)

if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)

    num_iterations = 5
    math_llm_agent = MathLLMAgent(num_iterations)
    num_episodes = 10

    math_problems_for_fine_tuning = ["What is 2+2?", "Solve x for 2x = 6"]