app.py

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

MODEL_ID = "meta-llama/Meta-Llama-3.1-8B-Instruct"

app = FastAPI(title="Artist Description Generator")

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    load_in_4bit=torch.cuda.is_available()
)

class ArtistInput(BaseModel):
    artist_name: str
    country: str | None = None
    genres: list[str] = []
    top_tracks: list[str] = []
    metrics: dict = {}

def build_prompt(data: ArtistInput) -> str:
    return f"""
You are writing a factual artist description for a music analytics platform.

Rules:
- Use ONLY the provided data
- Do NOT invent awards, numbers, or events
- If data is missing, omit it
- Keep it concise and neutral
- 3–5 sentences maximum

Artist name: {data.artist_name}
Country: {data.country}
Genres: {", ".join(data.genres)}
Top tracks: {", ".join(data.top_tracks)}
Metrics: {data.metrics}

Artist description:
""".strip()

@app.post("/generate")
def generate_description(data: ArtistInput):
    prompt = build_prompt(data)

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        output = model.generate(
            **inputs,
            max_new_tokens=180,
            temperature=0.3,
            top_p=0.9,
            do_sample=True
        )

    text = tokenizer.decode(output[0], skip_special_tokens=True)

    description = text.split("Artist description:")[-1].strip()

    return {
        "artist": data.artist_name,
        "description": description
    }