you thought i was finished :O we have more gpl2.0

!pip install nest-asyncio
!pip install nest_asyncio
!pip install aiosqlite
!pip install asyncio
!pip install aiosqlite
!pip install tensorflow
!pip install tensorflow-hub
!pip install nest_asyncio
!pip install nltk
!pip install transformers
!pip install scipy
!pip install pennylane
  
  import logging
import asyncio
import aiosqlite
import json
import tensorflow as tf
import tensorflow_hub as hub
from llama_cpp import Llama
from concurrent.futures import ThreadPoolExecutor
import nest_asyncio
import nltk
from nltk.tokenize import sent_tokenize
from transformers import BertTokenizer, AutoProcessor, MusicgenForConditionalGeneration
import scipy
import time
import os
import numpy as np
import pennylane as qml
import pickle

nltk.download('punkt')
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

try:
    llm = Llama(
        model_path="llama-2-7b-chat.ggmlv3.q8_0.bin",
        n_gpu_layers=-1,
        n_ctx=3900,
    )
except Exception as e:
    logger.error(f"Failed to initialize LLaMA model: {e}")
    raise

try:
    use_model = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
except Exception as e:
    logger.error(f"Failed to load Universal Sentence Encoder: {e}")
    raise

candidate_labels = ["action", "attention", "neutral"]
bert_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
custom_tokens = [
    "[TinkerTech gadget]", "[Quantum Flux Capacitor]", "[Nano-enhanced suit]",
    "[Virtual Reality Matrix]", "[Superhuman strength]", "[Telekinetic blast]",
    "[Cybernetic enhancement]", "[Invisibility cloak]", "[Dark Matter Cannon]",
    "[Mind-control serum]", "[Robot army]", "[Reality-warping device]",
    "[Interdimensional portal]", "[Parallel timeline]", "[Temporal paradox]",
    "[Multiverse convergence]", "[Explosive showdown]", "[High-speed chase]",
    "[Aerial dogfight]", "[Epic team-up]", "[Villain lair]", "[Heroic entrance]",
    "[Epic battle music]", "[Secret hideout]", "[Mysterious artifact]",
    "[Dramatic reveal]", "[Heart-pounding suspense]", "[Legendary weapon]",
    "[Alien invasion]", "[Ancient prophecy]", "[Time-travel dilemma]",
    "[Futuristic cityscape]", "[Space exploration]", "[Cyberpunk underworld]",
    "[Mythical creature]", "[Hidden treasure]", "[Robotic uprising]",
    "[Mega explosion]", "[Zero-gravity fight]", "[Supernatural phenomenon]",
    "[Cosmic entity]", "[Technological singularity]", "[Final showdown]",
    "[Time distortion]", "[Hyperdrive activation]", "[Energy surge]",
    "[Quantum anomaly]", "[Mind transfer]", "[Data corruption]",
    "[Ancient artifact]", "[Galactic council]", "[Parallel dimension]",
    "[Alien technology]", "[Lost civilization]", "[Genetic mutation]",
    "[Biomechanical creature]", "[Neural interface]", "[Nanobot swarm]",
    "[Temporal vortex]", "[Space-time rupture]", "[Bioengineered virus]",
    "[Ancient ruins]", "[Dimensional breach]", "[Gravity anomaly]",
    "[Solar flare]", "[Asteroid collision]", "[Neutrino storm]",
    "[Magnetic disruption]", "[Temporal loop]", "[Holographic interface]",
    "[Futuristic weaponry]", "[AI Innovation Safely]", "[Quantum encryption]"
]

def generate_classification_prompt(text, candidate_labels):
    prompt = (
        "You are an AI model designed to classify text into categories. "
        "Given a piece of text, you will determine the most appropriate label from the following options:\n"
        f"{', '.join(candidate_labels)}.\n"
        "Additionally, the text may contain custom tokens which you should recognize and utilize. Examples of custom tokens include:\n"
        f"{', '.join(custom_tokens[:5])}, etc.\n"
        "Classify the following text:\n"
        f"Text: {text}\n"
        "Label:"
    )
    return prompt

def classify_text_with_llama(text, candidate_labels):
    prompt = generate_classification_prompt(text, candidate_labels)
    try:
        result = llm(prompt)
        classification = result['choices'][0]['text'].strip()
        return classification
    except Exception as e:
        logger.error(f"Failed to classify text with LLaMA: {e}")
        raise

def convert_to_vector(text):
    try:
        embeddings = use_model([text])
        vector = embeddings.numpy()[0]
        logger.debug(f"Converted text to vector: {vector}")
        return vector
    except Exception as e:
        logger.error(f"Failed to convert text to vector: {e}")
        raise

async def initialize_db():
    try:
        async with aiosqlite.connect("movie_frames.db") as db:
            await db.execute("""
                CREATE TABLE IF NOT EXISTS VectorData (
                    frame_num INTEGER PRIMARY KEY,
                    vector_representation BLOB,
                    text TEXT,
                    color_wheel BLOB
                );
            """)
            await db.commit()
        logger.info("Database initialized")
    except Exception as e:
        logger.error(f"Failed to initialize database: {e}")
        raise

async def insert_into_db(frame_num, vector_representation, text, color_wheel):
    try:
        async with aiosqlite.connect("movie_frames.db") as db:
            await db.execute("INSERT INTO VectorData (frame_num, vector_representation, text, color_wheel) VALUES (?, ?, ?, ?)",
                             (frame_num, vector_representation.tobytes(), text, color_wheel.tobytes()))
            await db.commit()
        logger.info(f"Inserted frame {frame_num} into the database")
    except Exception as e:
        logger.error(f"Failed to insert frame {frame_num} into the database: {e}")
        raise

async def retrieve_from_db(frame_num):
    try:
        async with aiosqlite.connect("movie_frames.db") as db:
            async with db.execute("SELECT vector_representation, text, color_wheel FROM VectorData WHERE frame_num = ?", (frame_num,)) as cursor:
                row = await cursor.fetchone()
                if row:
                    vector_representation = np.frombuffer(row[0], dtype=np.float32)
                    text = row[1]
                    color_wheel = np.frombuffer(row[2], dtype=np.float32)
                    return vector_representation, text, color_wheel
        logger.info(f"Retrieved frame {frame_num} from the database")
        return None, None, None
    except Exception as e:
        logger.error(f"Failed to retrieve frame {frame_num} from the database: {e}")
        raise

def tokenize_and_add_custom_tokens(text):
    try:
        sentences = sent_tokenize(text)
        enhanced_sentences = []

        for sentence in sentences:

            tokens = nltk.word_tokenize(sentence)
            tokenized_text = bert_tokenizer.tokenize(" ".join(tokens))
            label = classify_text_with_llama(" ".join(tokenized_text), candidate_labels)

            if label == "action":
                enhanced_sentences.append(f"[action] {sentence}")
            elif label == "attention":
                enhanced_sentences.append(f"[attention] {sentence}")
            else:
                for token in custom_tokens:
                    if token.lower() in sentence.lower():
                        enhanced_sentences.append(sentence.replace(token.lower(), token))
                        break
                else:
                    enhanced_sentences.append(sentence)

        enhanced_text = ' '.join(enhanced_sentences)
        logger.debug(f"Enhanced text with custom tokens: {enhanced_text}")
        return enhanced_text
    except Exception as e:
        logger.error(f"Failed to tokenize and add custom tokens: {e}")
        raise

def apply_color_wheel_to_frame_template(frame_text, color_wheel):
    """
    Apply color wheel values to the frame template.

    Args:
    - frame_text (str): The text of the frame template.
    - color_wheel (numpy.ndarray): Array of color values.

    Returns:
    - str: Frame template with color applied.
    """
    # For illustration purposes, let's assume we simply append the color values to the frame text
    color_values = ", ".join([str(value) for value in color_wheel])
    return f"{frame_text}\nColor Wheel Values: {color_values}"

async def generate_and_summarize_frame(frame_num, frames, executor, lock):
    try:
        topic = "sci-fi"  # Replace with user input or a random topic
        llama_reply = llm(
            f"As an AI specialized in creating scripts, generate the opening scene for a movie about {topic}.",
            max_tokens=700
        )
        if llama_reply is None or 'choices' not in llama_reply:
            logger.error("LLM did not return a valid response.")
            return
        initial_prompt = llama_reply['choices'][0]['text']

        new_frame = tokenize_and_add_custom_tokens(initial_prompt)
        frames[f"{frame_num}"] = {"text": new_frame}
        vector_representation = convert_to_vector(new_frame)
        color_wheel = np.random.rand(10)  # Just for illustration, replace with actual generation logic
        async with lock:
            await insert_into_db(frame_num, vector_representation, new_frame, color_wheel)
        print(f"LLaMA Reply for Frame {frame_num}: {llama_reply}")
        logger.info(f"Generated and summarized frame {frame_num}")
    except Exception as e:
        logger.error(f"Failed to generate and summarize frame {frame_num}: {e}")
        frames[f"{frame_num}"] = {"text": "Failed to generate frame."}

async def main():
    try:
        await initialize_db()

        frames = {}
        executor = ThreadPoolExecutor(max_workers=4)
        lock = asyncio.Lock()

        num_frames = 10
        frame_tasks = []

        for frame_num in range(num_frames):
            task = generate_and_summarize_frame(frame_num, frames, executor, lock)
            frame_tasks.append(task)

        await asyncio.gather(*frame_tasks)

        ping, jitter, download_speed, upload_speed = 38, 81, 1000, 500
        color_wheel = np.random.rand(10)  # Just for illustration, replace with actual generation logic

        for frame_num in range(num_frames):
            frames[f"{frame_num}"]["template"] = apply_color_wheel_to_frame_template(frames[f"{frame_num}"]["text"], color_wheel)

        logger.info("Completed movie frame generation and linkage")
    except Exception as e:
        logger.error(f"An error occurred in the main function: {e}")

nest_asyncio.apply()
asyncio.run(main())