From ea01ef08225a811b475aa3fa3fee167cb83f2f83 Mon Sep 17 00:00:00 2001 From: AstraBert Date: Fri, 10 May 2024 01:43:06 +0200 Subject: [PATCH] new features --- .gitignore | 4 +- README.md | 12 +++++ docker/Dockerfile | 5 +- docker/image_classification.py | 5 +- docker/image_to_text.py | 3 +- docker/requirements.txt | 3 +- docker/retrieval_image_search.py | 74 ++++++++++++++++++++++++++++ docker/retrieval_text_generation.py | 5 +- docker/select_and_run.py | 19 +++++-- docker/text_summarization.py | 4 +- docker/utils.py | 72 +++++++++++++++++++++++++++ imgs/everything-ai.drawio.png | Bin 117390 -> 159833 bytes 12 files changed, 192 insertions(+), 14 deletions(-) create mode 100644 docker/retrieval_image_search.py diff --git a/.gitignore b/.gitignore index 2059c7a..18c1818 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,4 @@ flagged/ -scripts/__pycache__ docker/__pycache__ -docker/flagged \ No newline at end of file +docker/flagged +qdrant_storage/ \ No newline at end of file diff --git a/README.md b/README.md index 2c8315f..066e80c 100644 --- a/README.md +++ b/README.md @@ -57,6 +57,18 @@ Choose the task among: - *image-generation-pollinations*: stable diffusion, use Pollinations AI API; if you choose 'image-generation-pollinations', you do not need to specify anything else apart from the task - **MULTILINGUAL** - *image-classification*: classify an image, supports every image-classification model on HF Hub - **ENGLISH ONLY** - *image-to-text*: describe an image, supports every image-to-text model on HF Hub - **ENGLISH ONLY** +- *image-retrieval-search*: search an image database uploading a folder as database input. The folder should have the following structure: + +``` +./ +├── test/ +| ├── label1/ +| └── label2/ +└── train/ + ├── label1/ + └── label2/ +``` +You can query the database starting from your own pictures. ### 6. Go to `localhost:7860` and start using your assistant diff --git a/docker/Dockerfile b/docker/Dockerfile index a111e60..71c96e1 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -7,7 +7,10 @@ WORKDIR /app # Add the current directory contents into the container at /app ADD . /app +# Add new package +RUN python3 -m pip install datasets==2.15.0 + # Expose the port that the application will run on -EXPOSE 7860 +EXPOSE 8760 ENTRYPOINT [ "python3", "select_and_run.py" ] diff --git a/docker/image_classification.py b/docker/image_classification.py index ca4cd6b..a7f0858 100644 --- a/docker/image_classification.py +++ b/docker/image_classification.py @@ -1,6 +1,7 @@ from transformers import AutoModelForImageClassification, AutoImageProcessor, pipeline from PIL import Image from argparse import ArgumentParser +import torch argparse = ArgumentParser() argparse.add_argument( @@ -18,9 +19,11 @@ model_checkpoint = mod -model = AutoModelForImageClassification.from_pretrained(model_checkpoint) +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model = AutoModelForImageClassification.from_pretrained(model_checkpoint).to(device) processor = AutoImageProcessor.from_pretrained(model_checkpoint) + pipe = pipeline("image-classification", model=model, image_processor=processor) def get_results(image, ppln=pipe): diff --git a/docker/image_to_text.py b/docker/image_to_text.py index 97f6554..534310e 100644 --- a/docker/image_to_text.py +++ b/docker/image_to_text.py @@ -19,7 +19,8 @@ model_checkpoint = mod -pipe = pipeline("image-to-text", model=model_checkpoint) +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +pipe = pipeline("image-to-text", model=model_checkpoint, device=device) def get_results(image, ppln=pipe): img = Image.fromarray(image) diff --git a/docker/requirements.txt b/docker/requirements.txt index 486e55c..6768a8a 100644 --- a/docker/requirements.txt +++ b/docker/requirements.txt @@ -11,4 +11,5 @@ diffusers==0.27.2 pydantic==2.6.4 qdrant_client==1.9.0 pillow==10.2.0 -accelerate \ No newline at end of file +datasets==2.15.0 +accelerate diff --git a/docker/retrieval_image_search.py b/docker/retrieval_image_search.py new file mode 100644 index 0000000..732f496 --- /dev/null +++ b/docker/retrieval_image_search.py @@ -0,0 +1,74 @@ +from transformers import AutoImageProcessor, AutoModel +from utils import ImageDB +from PIL import Image +from qdrant_client import QdrantClient +import gradio as gr +from argparse import ArgumentParser +import torch + +argparse = ArgumentParser() +argparse.add_argument( + "-m", + "--model", + help="HuggingFace Model identifier, such as 'google/flan-t5-base'", + required=True, +) + +argparse.add_argument( + "-id", + "--image_dimension", + help="Dimension of the image (e.g. 512, 758, 384...)", + required=False, + default=512, + type=int +) + +argparse.add_argument( + "-d", + "--directory", + help="Directory where all your pdfs of interest are stored", + required=False, + default="No directory" +) + + +args = argparse.parse_args() + + +mod = args.model +dirs = args.directory +imd = args.image_dimension + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +processor = AutoImageProcessor.from_pretrained(mod) +model = AutoModel.from_pretrained(mod).to(device) + +client = QdrantClient(host="host.docker.internal", port=6333) +imdb = ImageDB(dirs, processor, model, client, imd) +print(imdb.collection_name) +imdb.create_dataset() +imdb.to_collection() + + +def see_images(dataset, results): + images = [] + for i in range(len(results)): + img = dataset[results[0].id]['image'] + images.append(img) + return images + +def process_img(image): + global imdb + results = imdb.searchDB(Image.fromarray(image)) + images = see_images(imdb.dataset, results) + return images + + +iface = gr.Interface( + title="everything-ai-retrievalimg", + fn=process_img, + inputs=gr.Image(label="Input Image"), + outputs=gr.Gallery(label="Matching Images"), +) + +iface.launch(server_name="0.0.0.0", share=False) \ No newline at end of file diff --git a/docker/retrieval_text_generation.py b/docker/retrieval_text_generation.py index 1089df4..251eb28 100644 --- a/docker/retrieval_text_generation.py +++ b/docker/retrieval_text_generation.py @@ -4,7 +4,7 @@ from sentence_transformers import SentenceTransformer from argparse import ArgumentParser from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline -import sys +import torch import os argparse = ArgumentParser() @@ -61,7 +61,8 @@ pdfdb.collect_data() pdfdb.qdrant_collection_and_upload() -model = AutoModelForCausalLM.from_pretrained(mod) +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model = AutoModelForCausalLM.from_pretrained(mod).to(device) tokenizer = AutoTokenizer.from_pretrained(mod) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=2048, repetition_penalty=1.2, temperature=0.4) diff --git a/docker/select_and_run.py b/docker/select_and_run.py index a399530..ee5ffe4 100644 --- a/docker/select_and_run.py +++ b/docker/select_and_run.py @@ -1,19 +1,22 @@ import subprocess as sp import gradio as gr -TASK_TO_SCRIPT = {"retrieval-text-generation": "retrieval_text_generation.py", "agnostic-text-generation": "agnostic_text_generation.py", "text-summarization": "text_summarization.py", "image-generation": "image_generation.py", "image-generation-pollinations": "image_generation_pollinations.py", "image-classification": "image_classification.py", "image-to-text": "image_to_text.py"} +TASK_TO_SCRIPT = {"retrieval-text-generation": "retrieval_text_generation.py", "agnostic-text-generation": "agnostic_text_generation.py", "text-summarization": "text_summarization.py", "image-generation": "image_generation.py", "image-generation-pollinations": "image_generation_pollinations.py", "image-classification": "image_classification.py", "image-to-text": "image_to_text.py", "retrieval-image-search": "retrieval_image_search.py"} -def build_command(tsk, mod="None", pdff="None", dirs="None", lan="None"): - if tsk != "retrieval-text-generation" and tsk != "image-generation-pollinations": +def build_command(tsk, mod="None", pdff="None", dirs="None", lan="None", imdim="512"): + if tsk != "retrieval-text-generation" and tsk != "image-generation-pollinations" and tsk != "retrieval-image-search": sp.run(f"python3 {TASK_TO_SCRIPT[tsk]} -m {mod}", shell=True) return f"python3 {TASK_TO_SCRIPT[tsk]} -m {mod}" elif tsk == "retrieval-text-generation": sp.run(f"python3 {TASK_TO_SCRIPT[tsk]} -m {mod} -pf '{pdff}' -d '{dirs}' -l '{lan}'", shell=True) return f"python3 {TASK_TO_SCRIPT[tsk]} -m {mod} -pf '{pdff}' -d '{dirs}' -l '{lan}'" - else: + elif tsk == "image-generation-pollinations": sp.run(f"python3 {TASK_TO_SCRIPT[tsk]}", shell=True) return f"python3 {TASK_TO_SCRIPT[tsk]}" + else: + sp.run(f"python3 {TASK_TO_SCRIPT[tsk]} -d {dirs} -id {imdim} -m {mod}", shell=True) + return f"python3 {TASK_TO_SCRIPT[tsk]} -d {dirs} -id {imdim} -m {mod}" demo = gr.Interface( build_command, @@ -38,7 +41,7 @@ def build_command(tsk, mod="None", pdff="None", dirs="None", lan="None"): ), gr.Textbox( label="Directory", - info="Directory where all your pdfs of interest are stored (only available with 'retrieval-text-generation')", + info="Directory where all your pdfs or images (.jpg, .jpeg, .png) of interest are stored (only available with 'retrieval-text-generation' for pdfs and 'retrieval-image-search' for images)", lines=3, value="None", ), @@ -48,6 +51,12 @@ def build_command(tsk, mod="None", pdff="None", dirs="None", lan="None"): lines=3, value="None", ), + gr.Textbox( + label="Image dimension", + info="Dimension of the image (this is generally model and/or task-dependent!)", + lines=3, + value=f"e.g.: 512, 384, 758...", + ), ], outputs="textbox", theme=gr.themes.Base() diff --git a/docker/text_summarization.py b/docker/text_summarization.py index 3124ecc..0721cd2 100644 --- a/docker/text_summarization.py +++ b/docker/text_summarization.py @@ -5,6 +5,7 @@ from utils import merge_pdfs import gradio as gr import time +import torch histr = [[None, "Hi, I'm **everything-ai-summarization**🤖.\nI'm here to assist you and let you summarize _your_ texts and _your_ pdfs!\nCheck [my website](https://astrabert.github.io/everything-ai/) for troubleshooting and documentation reference\nHave fun!😊"]] @@ -24,7 +25,8 @@ model_checkpoint = mod -summarizer = pipeline("summarization", model=model_checkpoint) +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +summarizer = pipeline("summarization", model=model_checkpoint, device=device) def convert_none_to_str(l: list): newlist = [] diff --git a/docker/utils.py b/docker/utils.py index 29989ec..60da330 100644 --- a/docker/utils.py +++ b/docker/utils.py @@ -6,6 +6,10 @@ from langchain.text_splitter import CharacterTextSplitter from langchain_community.document_loaders import PyPDFLoader import os +from datasets import load_dataset, Dataset +import torch +import numpy as np + def remove_items(test_list, item): res = [i for i in test_list if i != item] @@ -92,3 +96,71 @@ def translatef(self): translation = translator.translate(self.text) return translation +class ImageDB: + def __init__(self, imagesdir, processor, model, client, dimension): + self.imagesdir = imagesdir + self.processor = processor + self.model = model + self.client = client + self.dimension = dimension + if os.path.basename(self.imagesdir) != "": + self.collection_name = os.path.basename(self.imagesdir)+"_ImagesCollection" + else: + if "\\" in self.imagesdir: + self.collection_name = self.imagesdir.split("\\")[-2]+"_ImagesCollection" + else: + self.collection_name = self.imagesdir.split("/")[-2]+"_ImagesCollection" + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.client.recreate_collection( + collection_name=self.collection_name, + vectors_config=models.VectorParams(size=self.dimension, distance=models.Distance.COSINE) + ) + def get_embeddings(self, batch): + inputs = self.processor(images=batch['image'], return_tensors="pt").to(self.device) + with torch.no_grad(): + outputs = self.model(**inputs).last_hidden_state.mean(dim=1).cpu().numpy() + batch['embeddings'] = outputs + return batch + def create_dataset(self): + self.dataset = load_dataset("imagefolder", data_dir=self.imagesdir, split="train") + self.dataset = self.dataset.map(self.get_embeddings, batched=True, batch_size=16) + def to_collection(self): + np.save(os.path.join(self.imagesdir, "vectors"), np.array(self.dataset['embeddings']), allow_pickle=False) + + payload = self.dataset.select_columns([ + "label" + ]).to_pandas().fillna(0).to_dict(orient="records") + + ids = list(range(self.dataset.num_rows)) + embeddings = np.load(os.path.join(self.imagesdir, "vectors.npy")).tolist() + + batch_size = 1000 + + for i in range(0, self.dataset.num_rows, batch_size): + + low_idx = min(i+batch_size, self.dataset.num_rows) + + batch_of_ids = ids[i: low_idx] + batch_of_embs = embeddings[i: low_idx] + batch_of_payloads = payload[i: low_idx] + + self.client.upsert( + collection_name = self.collection_name, + points=models.Batch( + ids=batch_of_ids, + vectors=batch_of_embs, + payloads=batch_of_payloads + ) + ) + def searchDB(self, image): + dtst = {"image": [image], "label": ["None"]} + dtst = Dataset.from_dict(dtst) + dtst = dtst.map(self.get_embeddings, batched=True, batch_size=1) + img = dtst[0] + results = self.client.search( + collection_name=self.collection_name, + query_vector=img['embeddings'], + limit=4 + ) + return results + \ No newline at end of file diff --git a/imgs/everything-ai.drawio.png b/imgs/everything-ai.drawio.png index 52822224573b52427234f80b16cc22f174408e34..a1a7ac0838766494f9dcc8b8d572db4149a50fe9 100644 GIT binary patch literal 159833 zcmeEv2_RJK8+VDKvb5ZR~5d7TRP|$gZ-aLRk_*rL@@h 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