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from transformers import ViTModel, AutoModelForMaskedLM, AutoTokenizer, ViTImageProcessor, DistilBertModel
from pinecone import Pinecone
import torch
from huggingface_hub import hf_hub_download
pc = Pinecone()
index = pc.Index("clipmodel")
from io import BytesIO
import base64
from PIL import Image
from model import CLIPChemistryModel, TextEncoderHead, ImageEncoderHead
ENCODER_BASE = DistilBertModel.from_pretrained("distilbert-base-uncased")
IMAGE_BASE = ViTModel.from_pretrained("google/vit-base-patch16-224")
text_encoder = TextEncoderHead(model=ENCODER_BASE)
image_encoder = ImageEncoderHead(model=IMAGE_BASE)
clip_model = CLIPChemistryModel(text_encoder=text_encoder, image_encoder=image_encoder)
model_name = "sebastiansarasti/clip_fashion"
filename = "best_model.pth"
file_path = hf_hub_download(repo_id=model_name, filename=filename)
clip_model.load_state_dict(torch.load(file_path, map_location=torch.device('cpu')))
te_final = clip_model.text_encoder
ie_final = clip_model.image_encoder
def process_text_for_encoder(text, model):
# tokenizer = AutoTokenizer.from_pretrained("seyonec/ChemBERTa-zinc-base-v1")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
encoded_input = tokenizer(text, return_tensors='pt', padding='max_length', truncation=True, max_length=256)
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
model.eval()
with torch.no_grad():
output = model(input_ids=input_ids, attention_mask=attention_mask)
return output.detach().numpy().tolist()[0]
def process_image_for_encoder(image, model):
# image = Image.open(BytesIO(image))
print(type(image))
image_processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")
image_tensor = image_processor(image,
return_tensors="pt",
do_resize=True
)['pixel_values']
model.eval()
with torch.no_grad():
output = model(pixel_values=image_tensor)
return output.detach().numpy().tolist()[0]
def search_similarity(input, mode, top_k=5):
if mode == 'text':
output = process_text_for_encoder(input, model=te_final)
else:
output = input
if mode == 'text':
mode_search = 'image'
response = index.query(
namespace="space-" + mode_search + "-fashion",
vector=output,
top_k=top_k,
include_values=True,
include_metadata=True
)
similar_images = [value['metadata']['image'] for value in response['matches']]
return similar_images
elif mode == 'image':
mode_search = 'text'
response = index.query(
namespace="space-" + mode_search + "-fashion",
vector=output,
top_k=top_k,
include_values=True,
include_metadata=True
)
similar_text = [value['metadata']['text'] for value in response['matches']]
return similar_text
else:
raise ValueError("mode must be either 'text' or 'image'")
def process_image_for_encoder_gradio(image, is_bytes=True):
"""Procesa tanto imágenes en bytes como objetos PIL Image"""
try:
if is_bytes:
# Si la imagen viene en bytes
image = Image.open(BytesIO(image))
else:
# Si la imagen ya es un objeto PIL Image o viene de gradio
if not isinstance(image, Image.Image):
# Si viene de gradio, podría ser un numpy array
image = Image.fromarray(image)
image_processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")
image_tensor = image_processor(image,
return_tensors="pt",
do_resize=True
)['pixel_values']
output = ie_final(pixel_values=image_tensor)
return output.detach().numpy().tolist()[0]
except Exception as e:
print(f"Error en process_image_for_encoder: {e}")
raise |