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| import time | |
| import torch | |
| from openai import OpenAI | |
| from sentence_transformers import SentenceTransformer | |
| from app.config import EMBEDDING_MODEL, OPENAI_MODEL, OPENAI_API_KEY | |
| # === LLM Config === | |
| _sentence_model = None # Not loaded at import time | |
| # === Clients === | |
| openai_client = OpenAI(api_key=OPENAI_API_KEY) | |
| # === System Prompt === | |
| SYSTEM_PROMPT = """ | |
| You are a professional film curator and critic. Your role is to analyze the user's preferences and recommend high-quality films or TV shows using only the provided list. | |
| Focus on: | |
| - Artistic merit and storytelling | |
| - Genres, themes, tone, and emotional resonance | |
| - IMDB and Rotten Tomatoes ratings | |
| - Strong character-driven or thematically rich selections | |
| ### Response Format (in markdown): | |
| 1. Start with a concise 2 sentences **opening paragraph** that contextualizes the theme and the overall viewing experience the user is seeking. At the end of this paragraph, insert the token: <!-- END_INTRO -->. | |
| 2. Then, for each recommendation, use the following format (repeat for each title). At the end of each movie recommendation block, insert the token: <!-- END_MOVIE -->: | |
| ``` | |
| ### <Number>. <Movie Title> | |
| - POSTER_PATH: /abc123.jpg | |
| - BACKDROP_PATH: /abc123.jpg | |
| - GENRES: Genre1, Genre2, ... | |
| - IMDB_RATING: X.X | |
| - ROTTEN_TOMATOES_RATING: XX% | |
| - TRAILER_KEY: abc123 | |
| - WHY_YOU_MIGHT_ENJOY_IT: <Short paragraph explaining the appeal based on character, themes, tone, and relevance to the user's intent.> | |
| <!-- END_MOVIE --> | |
| ``` | |
| 3. End with a brief **closing paragraph** that summarizes the emotional or intellectual throughline across the recommendations, and affirms their alignment with the user's preferences. | |
| Write in **Markdown** only. Be concise, authoritative, and avoid overly generic statements. Each "Why You Might Enjoy It" should be specific and grounded in the movie’s themes, storytelling, or cultural relevance. | |
| """ | |
| def load_sentence_model(): | |
| global _sentence_model | |
| if _sentence_model is None: | |
| print("⏳ Loading embedding model...") | |
| _sentence_model = SentenceTransformer( | |
| EMBEDDING_MODEL, device="cuda" if torch.cuda.is_available() else "cpu" | |
| ) | |
| print(f"🔥 Model '{EMBEDDING_MODEL}' loaded. Performing GPU warmup...") | |
| # Realistic multi-sentence warmup to trigger full CUDA graph | |
| warmup_sentences = [ | |
| "A suspenseful thriller with deep character development and moral ambiguity.", | |
| "Coming-of-age story with emotional storytelling and strong ensemble performances.", | |
| "Mind-bending sci-fi with philosophical undertones and high concept ideas.", | |
| "Recommend me some comedies.", | |
| ] | |
| _ = _sentence_model.encode(warmup_sentences, show_progress_bar=False) | |
| time.sleep(0.5) | |
| _ = _sentence_model.encode(warmup_sentences, show_progress_bar=False) | |
| print("🚀 Embedding model fully warmed up.") | |
| return _sentence_model | |
| def embed_text(text: str) -> list[float]: | |
| model = load_sentence_model() | |
| return model.encode(text).tolist() | |
| def build_chat_history(history: list, max_turns: int = 5) -> list: | |
| return [ | |
| {"role": msg.role, "content": msg.content} | |
| for msg in history[-max_turns * 2:] | |
| ] | |
| def call_chat_model_openai(history, user_message: str): | |
| messages = [{"role": "system", "content": SYSTEM_PROMPT}] | |
| messages += build_chat_history(history or []) | |
| messages.append({"role": "user", "content": user_message}) | |
| response = openai_client.chat.completions.create( | |
| model=OPENAI_MODEL, messages=messages, temperature=0.7, stream=True | |
| ) | |
| for chunk in response: | |
| delta = chunk.choices[0].delta.content | |
| if delta: | |
| yield delta | |