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| import nmslib | |
| import numpy as np | |
| import streamlit as st | |
| from transformers import AutoTokenizer, CLIPProcessor | |
| from model import FlaxHybridCLIP | |
| from PIL import Image | |
| import jax.numpy as jnp | |
| import os | |
| import jax | |
| st.header('Under construction') | |
| st.title("CLIP Reply Demo") | |
| st.sidebar.markdown( | |
| """ | |
| Validation set: 351 images/273 deduped (There are still duplicates) | |
| Example Queries : | |
| """ | |
| ) | |
| def load_model(): | |
| model = FlaxHybridCLIP.from_pretrained("ceyda/clip-reply") | |
| processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") | |
| processor.tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base") | |
| return model, processor | |
| def load_image_index(): | |
| index = nmslib.init(method='hnsw', space='cosinesimil') | |
| index.loadIndex("./features/image_embeddings", load_data=True) | |
| return index | |
| file_names=os.listdir("./imgs") | |
| file_names.sort() | |
| image_index = load_image_index() | |
| model, processor = load_model() | |
| col_count=4 | |
| top_k=10 | |
| show_val=st.sidebar.button("show all validation set images") | |
| if show_val: | |
| cols=st.sidebar.beta_columns(col_count) | |
| for i,im in enumerate(file_names): | |
| j=i%col_count | |
| cols[j].image("./imgs/"+im) | |
| # TODO | |
| def add_image_emb(image): | |
| image = Image.open(image).convert("RGB") | |
| inputs = processor(text=[""], images=image, return_tensors="jax", padding=True) | |
| inputs["pixel_values"] = jnp.transpose(inputs["pixel_values"], axes=[0, 2, 3, 1]) | |
| features = model(**inputs).image_embeds | |
| image_index.addDataPoint(features) | |
| def query_with_images(query_images,query_text): | |
| images = [Image.open(im).convert("RGB") for im in query_images] | |
| inputs = processor(text=[query_text], images=images, return_tensors="jax", padding=True) | |
| inputs["pixel_values"] = jnp.transpose(inputs["pixel_values"], axes=[0, 2, 3, 1]) | |
| outputs = model(**inputs) | |
| logits_per_image = outputs.logits_per_image.reshape(-1) | |
| st.write(logits_per_image) | |
| probs = jax.nn.softmax(logits_per_image) | |
| st.write(probs) | |
| st.write(list(zip(images,probs))) | |
| results = sorted(list(zip(images,probs)),key=lambda x: x[1], reverse=True) | |
| st.write(results) | |
| return zip(*results) | |
| q_cols=st.beta_columns(2) | |
| query_text = q_cols[0].text_input("Input text", value="I love you") | |
| query_images = q_cols[1].file_uploader("(optional) upload query image",type=['jpg','jpeg'], accept_multiple_files=True) | |
| if query_images: | |
| st.write("Ranking uploaded images with respect to input text") | |
| ids, dists = query_with_images(query_images,query_text) | |
| else: | |
| st.write("Finding within validation set") | |
| proc = processor(text=[query_text], images=None, return_tensors="jax", padding=True) | |
| vec = np.asarray(model.get_text_features(**proc)) | |
| ids, dists = image_index.knnQuery(vec, k=top_k) | |
| res_cols=st.beta_columns(col_count) | |
| for i,(id_, dist) in enumerate(zip(ids, dists)): | |
| j=i%col_count | |
| with res_cols[j]: | |
| if isinstance(id_, np.int32): | |
| st.image("./imgs/"+file_names[id_]) | |
| else: | |
| st.image(id_) | |
| # st.write(file_names[id_]) | |
| st.write(dist) | |