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| # import os | |
| # import gradio as gr | |
| # import numpy as np | |
| # from transformers import AutoTokenizer, AutoModel | |
| # import time | |
| # # :white_check_mark: Setup environment | |
| # os.makedirs(os.environ.get("HF_HOME", "./hf_cache"), exist_ok=True) | |
| # hf_token = os.environ.get("HF_TOKEN") | |
| # if not hf_token: | |
| # raise EnvironmentError(":x: Environment variable HF_TOKEN is not set.") | |
| # # :white_check_mark: Load model and tokenizer | |
| # text_tokenizer = AutoTokenizer.from_pretrained( | |
| # "nomic-ai/nomic-embed-text-v1.5", | |
| # trust_remote_code=True, | |
| # token=hf_token, | |
| # cache_dir=os.environ["HF_HOME"] | |
| # ) | |
| # text_model = AutoModel.from_pretrained( | |
| # "nomic-ai/nomic-embed-text-v1.5", | |
| # trust_remote_code=True, | |
| # token=hf_token, | |
| # cache_dir=os.environ["HF_HOME"] | |
| # ) | |
| # # :white_check_mark: Embedding function | |
| # def get_text_embeddings(text): | |
| # """ | |
| # Converts input text into a dense embedding using the Nomic embedding model. | |
| # These embeddings are used to query Qdrant for semantically relevant document chunks. | |
| # """ | |
| # inputs = text_tokenizer(text, return_tensors="pt", padding=True, truncation=True) | |
| # outputs = text_model(**inputs) | |
| # embeddings = outputs.last_hidden_state.mean(dim=1) | |
| # return embeddings[0].detach().numpy() | |
| # # :white_check_mark: Gradio interface function | |
| # def embed_text_interface(text): | |
| # strt_time=time.time() | |
| # embedding = get_text_embeddings(text) | |
| # print(f"Total time taken by nomic to embed: {time.time()-strt_time}") | |
| # return str(embedding) | |
| # # :white_check_mark: Gradio UI | |
| # interface = gr.Interface( | |
| # fn=embed_text_interface, | |
| # inputs=gr.Textbox(label="Enter text to embed", lines=5), | |
| # outputs=gr.Textbox(label="Embedding vector"), | |
| # title="Text Embedding with Nomic AI", | |
| # description="Enter some text, and get its embedding vector using Nomic's embedding model." | |
| # ) | |
| # # :white_check_mark: Launch the app | |
| # if __name__ == "__main__": | |
| # interface.launch() | |
| import os | |
| import gradio as gr | |
| import numpy as np | |
| from transformers import AutoTokenizer, AutoModel | |
| import time | |
| import torch | |
| # :white_check_mark: Setup environment | |
| os.makedirs(os.environ.get("HF_HOME", "./hf_cache"), exist_ok=True) | |
| hf_token = os.environ.get("HF_TOKEN") | |
| if not hf_token: | |
| raise EnvironmentError(":x: Environment variable HF_TOKEN is not set.") | |
| # Check for GPU availability | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| print(f"Using device: {device}") | |
| # :white_check_mark: Load model and tokenizer | |
| text_tokenizer = AutoTokenizer.from_pretrained( | |
| "nomic-ai/nomic-embed-text-v1.5", | |
| trust_remote_code=True, | |
| token=hf_token, | |
| cache_dir=os.environ["HF_HOME"] | |
| ) | |
| text_model = AutoModel.from_pretrained( | |
| "nomic-ai/nomic-embed-text-v1.5", | |
| trust_remote_code=True, | |
| token=hf_token, | |
| cache_dir=os.environ["HF_HOME"] | |
| ).to(device) # Move model to GPU if available | |
| # :white_check_mark: Embedding function | |
| def get_text_embeddings(text): | |
| """ | |
| Converts input text into a dense embedding using the Nomic embedding model. | |
| These embeddings are used to query Qdrant for semantically relevant document chunks. | |
| """ | |
| inputs = text_tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device) # Move inputs to same device as model | |
| with torch.no_grad(): # Disable gradient calculation for inference | |
| outputs = text_model(**inputs) | |
| embeddings = outputs.last_hidden_state.mean(dim=1) | |
| return embeddings[0].cpu().numpy() # Move back to CPU for numpy conversion | |
| # :white_check_mark: Gradio interface function | |
| def embed_text_interface(text): | |
| strt_time = time.time() | |
| embedding = get_text_embeddings(text) | |
| print(f"Total time taken by nomic to embed: {time.time()-strt_time}") | |
| return str(embedding) | |
| # :white_check_mark: Gradio UI | |
| interface = gr.Interface( | |
| fn=embed_text_interface, | |
| inputs=gr.Textbox(label="Enter text to embed", lines=5), | |
| outputs=gr.Textbox(label="Embedding vector"), | |
| title="Text Embedding with Nomic AI", | |
| description="Enter some text, and get its embedding vector using Nomic's embedding model." | |
| ) | |
| # :white_check_mark: Launch the app | |
| if __name__ == "__main__": | |
| interface.launch() |