app/models/animal_vision.py

# app/models/animal_vision.py

import faiss
import torch
import open_clip
import numpy as np
from PIL import Image

from app.models.llm import explain_species
from app.utils.config import (
    DEVICE,
    BIOCLIP_MODEL_ID,
    BIOCLIP_INDEX_PATH,
    ANIMAL_SPECIES_LIST,
    TOP_K_ANIMALS
)



model, _, preprocess = open_clip.create_model_and_transforms(
    f"hf-hub:{BIOCLIP_MODEL_ID}"
)

model = model.to(DEVICE)
model.eval()




index = faiss.read_index(str(BIOCLIP_INDEX_PATH))

with open(ANIMAL_SPECIES_LIST, "r", encoding="utf-8") as f:
    SPECIES = [line.strip() for line in f]




@torch.no_grad()
def predict_animal(image: Image.Image):
    """
    Returns:
    {
        "species": str,
        "common_name": str | None,
        "confidence": float,
        "top_k": list,
        "description": str
    }
    """

    image_tensor = preprocess(image.convert("RGB"))
    image_tensor = image_tensor.unsqueeze(0).to(DEVICE)

    image_features = model.encode_image(image_tensor)
    image_features = image_features / image_features.norm(dim=-1, keepdim=True)

    image_np = image_features.cpu().numpy().astype("float32")
    scores, indices = index.search(image_np, TOP_K_ANIMALS)

    results = []
    for idx, score in zip(indices[0], scores[0]):
        results.append({
            "species": SPECIES[idx],
            "similarity": float(score)
        })

    best = results[0]

    llm_result = explain_species(
        species=best["species"],
        confidence=best["similarity"],
        domain="animal",
        top_k=results
    )

    return {
        "species": best["species"],
        "common_name": llm_result["common_name"],
        "confidence": best["similarity"],
        #"top_k": results,
       # "description": llm_result["description"],
    }