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())