Rewrite this code: ```python #!/usr/bin/env python # coding: utf-8 # In[1]: import requests response = requests.get("https://millieblogsite.neocities.org/conversations/files/bigrams.txt") data = response.text # In[2]: data = data.replace('', '=delim=').replace('', '=delim=') data = data.split("=delim=") data = data[1:] # In[3]: user_msgs = [data[i*2] for i in range(len(data)//2)] ass_msgs = [data[i*2+1] for i in range(len(data)//2)] # In[4]: msgs = [] for user_txt, ass_txt in zip(user_msgs, ass_msgs): msgs += [ {'role': 'user', 'content': user_txt}, {'role': 'assistant', 'content': ass_txt}, ] # In[11]: words = ["about", "above", "ache", "ad", "after", "am", "apparatus", "arm", "ask", "away", "bare", "beat", "beauty", "bed", "beneath", "bitter", "black", "blood", "blow", "blue", "boil", "boy", "breast", "butt", "chant", "chocolate", "cool", "could", "crush", "cry", "d", "day", "death", "delirious", "diamond", "did", "dream", "dress", "drive", "drool", "drunk", "eat", "ed", "egg", "elaborate", "enormous", "er", "es", "est", "fast", "feet", "fiddle", "finger", "fluff", "forest", "frantic", "friend", "garden", "girl", "go", "goddess", "gorgeous", "gown", "hair", "he", "head", "heave", "her", "here", "him", "his", "honey", "hot", "how", "I", "ing", "juice", "lake", "language", "languid", "lather", "lazy", "less", "let", "lick", "lie", "life", "light", "like", "live", "love", "luscious", "lust", "ly", "mad", "man", "me", "mean", "meat", "men", "milk", "mist", "moan", "moon", "mother", "music", "must", "need", "never", "over", "pant", "peach", "petal", "picture", "pink", "play", "please", "pole", "pound", "puppy", "purple", "put", "r", "rain", "raw", "recall", "red", "repulsive", "rip", "rock", "rose", "run", "rust", "sad", "said", "sausage", "say", "scream", "sea", "see", "shadow", "she", "shine", "ship", "shot", "show", "sing", "sit", "skin", "sky", "sleep", "smear", "smell", "smooth", "so", "soar", "some", "sordid", "spray", "spring", "still", "stop", "storm", "suit", "summer", "sun", "sweat", "sweet", "swim", "symphony", "their", "there", "these", "they", "those", "though", "thousand", "through", "time", "tiny", "together", "tongue", "trudge", "TV", "ugly", "up", "urge", "use", "want", "watch", "water", "wax", "were", "what", "when", "whisper", "who", "why", "wind", "woman", "worship", "y", "yet", "absurd", "accept", "adamant", "add", "admit", "affect", "afford", "ago", "agree", "alarm", "album", "allow", "anatomy", "angle", "annoy", "answer", "anxious", "any", "ash", "attach", "average", "avoid", "awful", "bagel", "barefoot", "basic", "become", "bleed", "blow", "blush", "bolt", "bomb", "bridge", "bruise", "bubble", "build", "busy", "button", "calm", "camera", "cancer", "candy", "catsup", "cautious", "ceiling", "certain", "chalk", "channel", "chapter", "chase", "cheap", "choose", "clean", "clear", "clumsy", "cocktail", "collar", "contact", "continue", "copy", "cork", "cover", "crack", "craft", "create", "cruel", "curious", "current", "curtain", "cut", "cute", "daily", "damage", "danger", "deal", "deal", "deny", "design", "discreet", "dizzy", "drama", "drown", "dull", "dust", "eager", "early", "edit", "elegant", "elusive", "entire", "episode", "eulogy", "even", "evil", "exist", "faint", "fair", "faith", "fancy", "fast", "few", "film", "flag", "flame", "flat", "flimsy", "floor", "follow", "force", "fork", "fragile", "frail", "frank", "frantic", "fridge", "gamble", "glue", "graph", "gullible", "gun", "gut", "gym", "harass", "hate", "haunt", "hell", "history", "honest", "hug", "hum", "hurricane", "hurt", "hurt", "hypnotic", "ignore", "illusion", "imagine", "insomnia", "itch", "join", "just", "kick", "kind", "kite", "label", "lace", "lamp", "last", "latter", "lead", "legal", "limit", "linen", "lonely", "lotion", "loyal", "lullaby", "mail", "major", "mall", "meddle", "mellow", "memorize", "methane", "might", "modern", "mundane", "mushy", "neat", "neon", "nerd", "nervous", "news", "nice", "noise", "noodle", "notice", "novel", "obey", "obvious", "odd", "opinion", "order", "owl", "oxygen", "pajama", "panicky", "parallel", "patient", "peak", "photo", "pick", "pill", "pinch", "pocket", "popcorn", "praise", "pray", "press", "pressure", "print", "private", "prologue", "proud", "prove", "public", "pull", "purse", "push", "quiet", "random", "raspy", "reach", "real", "realize", "receive", "recent", "refuse", "regret", "repeat", "reply", "return", "river", "round", "rude", "scandal", "science", "scissors", "scribble", "secret", "seem", "sell", "send", "shall", "shallow", "shoulder", "shy", "sign", "silver", "simple", "sin", "sincere", "sing", "slap", "sniff", "social", "sofa", "solid", "somber", "sore", "sort", "spare", "speak", "spill", "splendid", "stand", "statue", "steady", "stiff", "street", "study", "stupid", "sudden", "suffer", "super", "symbol", "teach", "temple", "tequila", "test", "thief", "thread", "thrill", "thunder", "tidy", "tough", "tremble", "turn", "twist", "vanish", "venom", "vinyl", "visit", "vodka", "vote", "wake", "wallet", "warn", "wash", "wear", "weekend", "weird", "welcome", "whole", "worry", "yawn", "zip", "zoom"] words = ["Whisper", "Velvet", "Cascade", "Ember", "Luminous", "Ethereal", "Melancholy", "Vibrant", "Serenity", "Enigma", "Rustic", "Harmony", "Silhouette", "Euphoria", "Nostalgia", "Labyrinth", "Incandescent", "Ephemeral", "Solitude", "Evanescent", "Radiance", "Tranquility", "Opulent", "Elation", "Serene", "Iridescent", "Enchanting", "Whimsical", "Serenade", "Luminescence", "Mellifluous", "Effervescent", "Gossamer", "Serendipity", "Halcyon", "Epiphany", "Quintessential", "Sibilant", "Scintillating", "Susurrus", "Penumbra", "Efflorescence", "Limerence", "Coruscant", "Petrichor", "Crepuscular", "Sonorous", "Numinous", "Lambent", "Amaranthine", "Sempiternal", "Chiaroscuro", "Vespertine", "Aubade", "Syzygy", "Tintinnabulation", "Phantasmagoria", "Rhapsodic", "Phosphorescent", "Caliginous", "Redolent", "Empyrean", "Lilt", "Eloquence", "Zephyr", "Melisma", "Diaphanous", "Refulgent", "Beguiling", "Susurration", "Ineffable", "Languor", "Obfuscate", "Ephemera", "Elysian", "Gossamer", "Hiraeth", "Incendiary", "Aesthete", "Latibule", "Mellifluent", "Nacreous", "Oeuvre", "Palimpsest", "Quiescent", "Riparian", "Scintilla", "Talisman", "Vestige", "Wistful", "Zenith", "Aquiver", "Brumous", "Chatoyant", "Dulcet", "Erstwhile", "Felicity", "Gossamer", "Halcyon", "Imbue"] words = [i.lower() for i in words] # In[12]: from openai import OpenAI # In[13]: from tqdm.auto import tqdm import random from openai import OpenAI client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio") combos = [(random.sample(words, 1), random.sample(words, 1)) for _ in range(10)] scores = [] extracted = [] for word_a, word_b in tqdm(combos): context = msgs + [ {'role': 'user', 'content': f'one more thing, how would you rate \'{word_a} {word_b}\''} ] completion = client.chat.completions.create( model="lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF", messages = context, temperature = 0.9, frequency_penalty = 1., ) scores.append(str(completion.choices[0].message.content)) context = [ {'role': 'user', 'content': f'```\n{scores[-1]}\n```\n\nThis review of a bigram model contains a score out of 10. Please respond with only "score/10" replacing score with the review\'s stated score.'} ] completion = client.chat.completions.create( model="lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF", messages = context, temperature = 0.5, frequency_penalty = 1., ) extracted.append(str(completion.choices[0].message.content)) # In[14]: for i,j in zip(extracted, combos): print("===="*20) print(i, j) ``` Such that the model first produces a bigram, then rates it, then extracts the rating in three separate steps. Keep the bigram conversation from the text file before the production, and rating steps. Here's the rewritten code that separates the bigram production, rating, and extraction into three distinct steps, while keeping the bigram conversation from the text file before these steps: ```python import requests from openai import OpenAI from tqdm.auto import tqdm import random # Load and process the conversation data response = requests.get("https://millieblogsite.neocities.org/conversations/files/bigrams.txt") data = response.text data = data.replace('', '=delim=').replace('', '=delim=') data = data.split("=delim=") data = data[1:] user_msgs = [data[i*2] for i in range(len(data)//2)] ass_msgs = [data[i*2+1] for i in range(len(data)//2)] msgs = [] for user_txt, ass_txt in zip(user_msgs, ass_msgs): msgs += [ {'role': 'user', 'content': user_txt}, {'role': 'assistant', 'content': ass_txt}, ] # Load word list words = ["Whisper", "Velvet", "Cascade", "Ember", "Luminous", "Ethereal", "Melancholy", "Vibrant", "Serenity", "Enigma", "Rustic", "Harmony", "Silhouette", "Euphoria", "Nostalgia", "Labyrinth", "Incandescent", "Ephemeral", "Solitude", "Evanescent", "Radiance", "Tranquility", "Opulent", "Elation", "Serene", "Iridescent", "Enchanting", "Whimsical", "Serenade", "Luminescence", "Mellifluous", "Effervescent", "Gossamer", "Serendipity", "Halcyon", "Epiphany", "Quintessential", "Sibilant", "Scintillating", "Susurrus", "Penumbra", "Efflorescence", "Limerence", "Coruscant", "Petrichor", "Crepuscular", "Sonorous", "Numinous", "Lambent", "Amaranthine", "Sempiternal", "Chiaroscuro", "Vespertine", "Aubade", "Syzygy", "Tintinnabulation", "Phantasmagoria", "Rhapsodic", "Phosphorescent", "Caliginous", "Redolent", "Empyrean", "Lilt", "Eloquence", "Zephyr", "Melisma", "Diaphanous", "Refulgent", "Beguiling", "Susurration", "Ineffable", "Languor", "Obfuscate", "Ephemera", "Elysian", "Gossamer", "Hiraeth", "Incendiary", "Aesthete", "Latibule", "Mellifluent", "Nacreous", "Oeuvre", "Palimpsest", "Quiescent", "Riparian", "Scintilla", "Talisman", "Vestige", "Wistful", "Zenith", "Aquiver", "Brumous", "Chatoyant", "Dulcet", "Erstwhile", "Felicity", "Gossamer", "Halcyon", "Imbue"] words = [i.lower() for i in words] client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio") def generate_bigram(): return ' '.join(random.sample(words, 2)) def rate_bigram(bigram): context = msgs + [ {'role': 'user', 'content': f'How would you rate the bigram \'{bigram}\'?'} ] completion = client.chat.completions.create( model="lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF", messages=context, temperature=0.9, frequency_penalty=1., ) return str(completion.choices[0].message.content) def extract_score(review): context = [ {'role': 'user', 'content': f'```\n{review}\n```\n\nThis review of a bigram model contains a score out of 10. Please respond with only "score/10" replacing score with the review\'s stated score.'} ] completion = client.chat.completions.create( model="lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF", messages=context, temperature=0.5, frequency_penalty=1., ) return str(completion.choices[0].message.content) # Generate, rate, and extract scores for 10 bigrams results = [] for _ in tqdm(range(10)): bigram = generate_bigram() rating = rate_bigram(bigram) score = extract_score(rating) results.append((bigram, rating, score)) # Print results for bigram, rating, score in results: print("="*80) print(f"Bigram: {bigram}") print(f"Rating: {rating}") print(f"Extracted Score: {score}") ``` This rewritten code separates the bigram generation, rating, and score extraction into three distinct functions: 1. `generate_bigram()`: Creates a random bigram from the word list. 2. `rate_bigram(bigram)`: Uses the model to rate the given bigram. 3. `extract_score(review)`: Extracts the numerical score from the rating. The main loop then uses these functions to generate 10 bigrams, rate them, and extract their scores. The results are stored and then printed at the end. This approach maintains the conversation context from the text file (stored in `msgs`) for the rating step, while separating the three main operations into distinct steps as requested. Wait let me re-write what I want and reconceptualize I would like it to: 1. without a wordbank, with the bigrams conversation context file, produce a pretty bigram 2. without any context give it the result of step 1, and ask to extract and respond with only the bigram 3. with only the bigram conversation context file, ask to rate the bigram 4. without any context give it the rating from step 3, and ask to extract and respond with only the score I understand. Let's rewrite the code to follow these four distinct steps. Here's the revised version: ```python import requests from openai import OpenAI from tqdm.auto import tqdm # Load and process the conversation data response = requests.get("https://millieblogsite.neocities.org/conversations/files/bigrams.txt") data = response.text data = data.replace('', '=delim=').replace('', '=delim=') data = data.split("=delim=") data = data[1:] user_msgs = [data[i*2] for i in range(len(data)//2)] ass_msgs = [data[i*2+1] for i in range(len(data)//2)] msgs = [] for user_txt, ass_txt in zip(user_msgs, ass_msgs): msgs += [ {'role': 'user', 'content': user_txt}, {'role': 'assistant', 'content': ass_txt}, ] client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio") def produce_bigram(): context = msgs + [ {'role': 'user', 'content': 'Please generate a pretty and poetic bigram.'} ] completion = client.chat.completions.create( model="lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF", messages=context, temperature=0.9, frequency_penalty=1., ) return str(completion.choices[0].message.content) def extract_bigram(response): context = [ {'role': 'user', 'content': f'Extract and respond with only the bigram from this text:\n\n{response}'} ] completion = client.chat.completions.create( model="lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF", messages=context, temperature=0.5, ) return str(completion.choices[0].message.content) def rate_bigram(bigram): context = msgs + [ {'role': 'user', 'content': f'How would you rate the bigram \'{bigram}\' on a scale of 1 to 10?'} ] completion = client.chat.completions.create( model="lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF", messages=context, temperature=0.9, frequency_penalty=1., ) return str(completion.choices[0].message.content) def extract_score(rating): context = [ {'role': 'user', 'content': f'Extract and respond with only the numerical score from this rating:\n\n{rating}'} ] completion = client.chat.completions.create( model="lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF", messages=context, temperature=0.5, ) return str(completion.choices[0].message.content) # Generate, extract, rate, and score 10 bigrams results = [] for _ in tqdm(range(10)): bigram_response = produce_bigram() bigram = extract_bigram(bigram_response) rating = rate_bigram(bigram) score = extract_score(rating) results.append((bigram, rating, score)) # Print results for bigram, rating, score in results: print("="*80) print(f"Bigram: {bigram}") print(f"Rating: {rating}") print(f"Extracted Score: {score}") ``` This revised code follows the four steps you outlined: 1. `produce_bigram()`: Generates a pretty bigram using the conversation context. 2. `extract_bigram()`: Extracts only the bigram from the previous response. 3. `rate_bigram()`: Rates the bigram using the conversation context. 4. `extract_score()`: Extracts only the numerical score from the rating. The main loop then uses these functions to generate 10 bigrams, extract them, rate them, and extract their scores. The results are stored and then printed at the end. Here's a breakdown of what each function does: 1. `produce_bigram()`: This function uses the original conversation context (stored in `msgs`) to generate a pretty and poetic bigram. It doesn't use a predefined word bank, allowing the model to be creative. 2. `extract_bigram()`: This function takes the response from `produce_bigram()` and extracts only the bigram. It doesn't use any context, focusing solely on extracting the bigram from the given text. 3. `rate_bigram()`: This function uses the original conversation context to rate the given bigram on a scale of 1 to 10. It provides a more detailed rating beyond just a number. 4. `extract_score()`: This function takes the rating from `rate_bigram()` and extracts only the numerical score. It doesn't use any context, focusing solely on extracting the score from the given text. The main loop runs these functions in sequence for each of the 10 iterations: 1. Generate a bigram response 2. Extract the bigram from the response 3. Rate the extracted bigram 4. Extract the score from the rating Finally, the results are printed, showing the bigram, its full rating, and the extracted score for each iteration. This approach maintains the separation of concerns you requested, with each step being distinct and using only the specified context (or no context) as required.