From 17994421233d57ff20ec56f5027ee716f90ddaa0 Mon Sep 17 00:00:00 2001 From: Ethan Steininger Date: Tue, 19 Mar 2024 12:13:45 -0400 Subject: [PATCH] commenting out bandit --- src/api/utilities/code.py | 36 +- src/inference/embed/modalities/audio.py | 60 +- src/inference/embed/modalities/image.py | 56 +- src/inference/embed/modalities/video.py | 114 +-- src/inference/embed/service.py | 25 +- src/inference/poetry.lock | 966 +----------------------- src/inference/pyproject.toml | 5 - 7 files changed, 182 insertions(+), 1080 deletions(-) diff --git a/src/api/utilities/code.py b/src/api/utilities/code.py index f1347ca..670837f 100644 --- a/src/api/utilities/code.py +++ b/src/api/utilities/code.py @@ -13,25 +13,25 @@ def check_code_security(code, max_code_length=3000, language="python"): status_code=400, detail=f"Code length exceeds {max_code_length} characters", ) - extensions = {"python": ".py"} - with tempfile.NamedTemporaryFile(suffix=".py") as temp: - temp.write(code.encode()) - temp.flush() - result = subprocess.run( - ["bandit", temp.name], capture_output=True, text=True - ) + # extensions = {"python": ".py"} + # with tempfile.NamedTemporaryFile(suffix=".py") as temp: + # temp.write(code.encode()) + # temp.flush() + # result = subprocess.run( + # ["bandit", temp.name], capture_output=True, text=True + # ) - # Check if bandit found any issues - if "No issues identified." in result.stdout: - return True - else: - # Parse the issues into a JSON - # Skip the first two lines, which are not issues - issues = result.stdout.split("\n")[2:] - issues_json = [ - {"issue": issue} for issue in issues if issue - ] # Ignore empty lines - return json.dumps(issues_json[1]["issue"]) + # # Check if bandit found any issues + # if "No issues identified." in result.stdout: + # return True + # else: + # # Parse the issues into a JSON + # # Skip the first two lines, which are not issues + # issues = result.stdout.split("\n")[2:] + # issues_json = [ + # {"issue": issue} for issue in issues if issue + # ] # Ignore empty lines + # return json.dumps(issues_json[1]["issue"]) @staticmethod def check_for_function(code): diff --git a/src/inference/embed/modalities/audio.py b/src/inference/embed/modalities/audio.py index 97dcb6d..78c880e 100644 --- a/src/inference/embed/modalities/audio.py +++ b/src/inference/embed/modalities/audio.py @@ -1,39 +1,39 @@ -from transformers import Wav2Vec2Processor, Wav2Vec2Model -import torch -import librosa -import numpy as np -import io +# from transformers import Wav2Vec2Processor, Wav2Vec2Model +# import torch +# import librosa +# import numpy as np +# import io -class AudioEmbeddingService: - def __init__(self, model): - self.processor = Wav2Vec2Processor.from_pretrained(model) - self.model = Wav2Vec2Model.from_pretrained(model) +# class AudioEmbeddingService: +# def __init__(self, model): +# self.processor = Wav2Vec2Processor.from_pretrained(model) +# self.model = Wav2Vec2Model.from_pretrained(model) - def encode(self, file_stream): - # Load the audio file - audio_input, sr = librosa.load(io.BytesIO(file_stream), sr=16000) +# def encode(self, file_stream): +# # Load the audio file +# audio_input, sr = librosa.load(io.BytesIO(file_stream), sr=16000) - # Process audio - inputs = self.processor(audio_input, return_tensors="pt", sampling_rate=sr) +# # Process audio +# inputs = self.processor(audio_input, return_tensors="pt", sampling_rate=sr) - # Move to CPU - inputs = {k: v.to("cpu") for k, v in inputs.items()} +# # Move to CPU +# inputs = {k: v.to("cpu") for k, v in inputs.items()} - # Get the audio embedding - with torch.no_grad(): - audio_features = self.model(**inputs).last_hidden_state - - # Mean pooling the embeddings across the time dimension - embeddings = audio_features.mean(dim=1) +# # Get the audio embedding +# with torch.no_grad(): +# audio_features = self.model(**inputs).last_hidden_state - # Normalize the embeddings - normalized_embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1) +# # Mean pooling the embeddings across the time dimension +# embeddings = audio_features.mean(dim=1) - return normalized_embeddings +# # Normalize the embeddings +# normalized_embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1) - def get_dimensions(self): - return self.model.config.hidden_size +# return normalized_embeddings - def get_token_size(self): - # Similar to images, token size isn't directly applicable to audio - return None \ No newline at end of file +# def get_dimensions(self): +# return self.model.config.hidden_size + +# def get_token_size(self): +# # Similar to images, token size isn't directly applicable to audio +# return None diff --git a/src/inference/embed/modalities/image.py b/src/inference/embed/modalities/image.py index 20fcffa..7da2575 100644 --- a/src/inference/embed/modalities/image.py +++ b/src/inference/embed/modalities/image.py @@ -1,38 +1,38 @@ -from transformers import CLIPProcessor, CLIPModel -import torch -import io -from PIL import Image +# from transformers import CLIPProcessor, CLIPModel +# import torch +# import io +# from PIL import Image -class ImageEmbeddingService: - def __init__(self, model): - self.processor = CLIPProcessor.from_pretrained(model) - self.model = CLIPModel.from_pretrained(model) +# class ImageEmbeddingService: +# def __init__(self, model): +# self.processor = CLIPProcessor.from_pretrained(model) +# self.model = CLIPModel.from_pretrained(model) - def encode(self, file_stream): - # Load the image - image = Image.open(io.BytesIO(file_stream)) +# def encode(self, file_stream): +# # Load the image +# image = Image.open(io.BytesIO(file_stream)) - # Process image - inputs = self.processor(images=image, return_tensors="pt") +# # Process image +# inputs = self.processor(images=image, return_tensors="pt") - # Move to CPU - inputs = {k: v.to("cpu") for k, v in inputs.items()} +# # Move to CPU +# inputs = {k: v.to("cpu") for k, v in inputs.items()} - # Get the image embedding - with torch.no_grad(): - image_features = self.model.get_image_features(**inputs) +# # Get the image embedding +# with torch.no_grad(): +# image_features = self.model.get_image_features(**inputs) - # Normalize the embeddings - image_embeddings = torch.nn.functional.normalize(image_features, p=2, dim=1) +# # Normalize the embeddings +# image_embeddings = torch.nn.functional.normalize(image_features, p=2, dim=1) - return image_embeddings +# return image_embeddings - def get_dimensions(self): - # CLIP's image and text embeddings are of the same size - return self.model.config.text_config.hidden_size +# def get_dimensions(self): +# # CLIP's image and text embeddings are of the same size +# return self.model.config.text_config.hidden_size - def get_token_size(self): - # This method isn't directly applicable to images as it is to text - # Returning None or a default value could be more appropriate - return None +# def get_token_size(self): +# # This method isn't directly applicable to images as it is to text +# # Returning None or a default value could be more appropriate +# return None diff --git a/src/inference/embed/modalities/video.py b/src/inference/embed/modalities/video.py index 3d4b79e..8330e0b 100644 --- a/src/inference/embed/modalities/video.py +++ b/src/inference/embed/modalities/video.py @@ -1,57 +1,57 @@ -import torch -from PIL import Image -from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize -from transformers import CLIPProcessor, CLIPModel -import cv2 -import numpy as np - - -class VideoEmbeddingService: - def __init__(self, model): - self.processor = CLIPProcessor.from_pretrained(model) - self.model = CLIPModel.from_pretrained(model) - - def frame_embeddings(self, video_path): - # Initialize a video capture object - cap = cv2.VideoCapture(video_path) - frame_embeddings = [] - - while True: - ret, frame = cap.read() - if not ret: - break - - # Convert the color space from BGR to RGB, then convert to PIL Image - frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - pil_image = Image.fromarray(frame) - - # Preprocess the image - inputs = self.processor(images=pil_image, return_tensors="pt", padding=True) - - # Move to CPU and get the image embedding - inputs = {k: v.to("cpu") for k, v in inputs.items()} - with torch.no_grad(): - frame_features = self.model.get_image_features(**inputs) - - frame_embeddings.append(frame_features) - - cap.release() - return torch.stack(frame_embeddings) - - def encode(self, file_stream): - # Assume file_stream is a path for simplicity; adapt as necessary for actual streams - embeddings = self.frame_embeddings(file_stream) - # Aggregate embeddings, e.g., by averaging - video_embedding = embeddings.mean(dim=0) - # Normalize the embeddings - normalized_embedding = torch.nn.functional.normalize( - video_embedding, p=2, dim=1 - ) - return normalized_embedding - - def get_dimensions(self): - return self.model.config.visual_projection.out_features - - def get_token_size(self): - # Not applicable for videos, similar to audio and images - return None +# import torch +# from PIL import Image +# from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize +# from transformers import CLIPProcessor, CLIPModel +# import cv2 +# import numpy as np + + +# class VideoEmbeddingService: +# def __init__(self, model): +# self.processor = CLIPProcessor.from_pretrained(model) +# self.model = CLIPModel.from_pretrained(model) + +# def frame_embeddings(self, video_path): +# # Initialize a video capture object +# cap = cv2.VideoCapture(video_path) +# frame_embeddings = [] + +# while True: +# ret, frame = cap.read() +# if not ret: +# break + +# # Convert the color space from BGR to RGB, then convert to PIL Image +# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) +# pil_image = Image.fromarray(frame) + +# # Preprocess the image +# inputs = self.processor(images=pil_image, return_tensors="pt", padding=True) + +# # Move to CPU and get the image embedding +# inputs = {k: v.to("cpu") for k, v in inputs.items()} +# with torch.no_grad(): +# frame_features = self.model.get_image_features(**inputs) + +# frame_embeddings.append(frame_features) + +# cap.release() +# return torch.stack(frame_embeddings) + +# def encode(self, file_stream): +# # Assume file_stream is a path for simplicity; adapt as necessary for actual streams +# embeddings = self.frame_embeddings(file_stream) +# # Aggregate embeddings, e.g., by averaging +# video_embedding = embeddings.mean(dim=0) +# # Normalize the embeddings +# normalized_embedding = torch.nn.functional.normalize( +# video_embedding, p=2, dim=1 +# ) +# return normalized_embedding + +# def get_dimensions(self): +# return self.model.config.visual_projection.out_features + +# def get_token_size(self): +# # Not applicable for videos, similar to audio and images +# return None diff --git a/src/inference/embed/service.py b/src/inference/embed/service.py index d505762..1942652 100644 --- a/src/inference/embed/service.py +++ b/src/inference/embed/service.py @@ -5,10 +5,11 @@ from _utils import create_success_response -from .modalities.image import ImageEmbeddingService from .modalities.text import TextEmbeddingService -from .modalities.audio import AudioEmbeddingService -from .modalities.video import VideoEmbeddingService + +# from .modalities.image import ImageEmbeddingService +# from .modalities.audio import AudioEmbeddingService +# from .modalities.video import VideoEmbeddingService class EmbeddingHandler: @@ -16,15 +17,15 @@ def __init__(self, modality, model): if modality == "text": # sentence-transformers/all-MiniLM-L6-v2 self.service = TextEmbeddingService(model) - elif modality == "image": - # openai/clip-vit-base-patch32 - self.service = ImageEmbeddingService(model) - elif modality == "audio": - # facebook/wav2vec2-base-960h - self.service = AudioEmbeddingService(model) - elif modality == "video": - # openai/clip-vit-base-patch32 - self.service = VideoEmbeddingService(model) + # elif modality == "image": + # # openai/clip-vit-base-patch32 + # self.service = ImageEmbeddingService(model) + # elif modality == "audio": + # # facebook/wav2vec2-base-960h + # self.service = AudioEmbeddingService(model) + # elif modality == "video": + # # openai/clip-vit-base-patch32 + # self.service = VideoEmbeddingService(model) else: raise ValueError(f"Unknown modality: {modality}") diff --git a/src/inference/poetry.lock b/src/inference/poetry.lock index 76e891e..f275acf 100644 --- a/src/inference/poetry.lock +++ b/src/inference/poetry.lock @@ -187,49 +187,35 @@ tests = ["attrs[tests-no-zope]", "zope-interface"] tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"] tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"] -[[package]] -name = "audioread" -version = "3.0.1" -description = "Multi-library, cross-platform audio decoding." -optional = false -python-versions = ">=3.6" -files = [ - 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