# main.py import spaces import os import uuid import gradio as gr import torch import torch.nn.functional as F from torch.nn import DataParallel from torch import Tensor from transformers import AutoTokenizer, AutoModel from huggingface_hub import InferenceClient from openai import OpenAI from langchain_community.document_loaders import UnstructuredFileLoader from langchain_chroma import Chroma from chromadb import Documents, EmbeddingFunction, Embeddings from chromadb.config import Settings from chromadb import HttpClient from utils import load_env_variables, parse_and_route from globalvars import API_BASE, intention_prompt, tasks, system_message, model_name from dotenv import load_dotenv load_dotenv() os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:30' os.environ['CUDA_LAUNCH_BLOCKING'] = '1' os.environ['CUDA_CACHE_DISABLE'] = '1' device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ### Utils hf_token, yi_token = load_env_variables() def clear_cuda_cache(): torch.cuda.empty_cache() client = OpenAI(api_key=yi_token, base_url=API_BASE) class EmbeddingGenerator: def __init__(self, model_name: str, token: str, intention_client): self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.tokenizer = AutoTokenizer.from_pretrained(model_name, token=token, trust_remote_code=True) self.model = AutoModel.from_pretrained(model_name, token=token, trust_remote_code=True).to(self.device) self.intention_client = intention_client def clear_cuda_cache(self): torch.cuda.empty_cache() @spaces.GPU def compute_embeddings(self, input_text: str): # Get the intention intention_completion = self.intention_client.chat.completions.create( model="yi-large", messages=[ {"role": "system", "content": intention_prompt}, {"role": "user", "content": input_text} ] ) intention_output = intention_completion.choices[0].message['content'] # Parse and route the intention parsed_task = parse_and_route(intention_output) selected_task = list(parsed_task.keys())[0] # Construct the prompt try: task_description = tasks[selected_task] except KeyError: print(f"Selected task not found: {selected_task}") return f"Error: Task '{selected_task}' not found. Please select a valid task." query_prefix = f"Instruct: {task_description}\nQuery: " queries = [input_text] # Get the embeddings with torch.no_grad(): inputs = self.tokenizer(queries, return_tensors='pt', padding=True, truncation=True, max_length=4096).to(self.device) outputs = self.model(**inputs) query_embeddings = outputs.last_hidden_state.mean(dim=1) # Normalize embeddings query_embeddings = F.normalize(query_embeddings, p=2, dim=1) embeddings_list = query_embeddings.detach().cpu().numpy().tolist() self.clear_cuda_cache() return embeddings_list class MyEmbeddingFunction(EmbeddingFunction): def __init__(self, embedding_generator: EmbeddingGenerator): self.embedding_generator = embedding_generator def __call__(self, input: Documents) -> Embeddings: embeddings = [self.embedding_generator.compute_embeddings(doc) for doc in input] embeddings = [item for sublist in embeddings for item in sublist] return embeddings def load_documents(file_path: str, mode: str = "elements"): loader = UnstructuredFileLoader(file_path, mode=mode) docs = loader.load() return [doc.page_content for doc in docs] def initialize_chroma(collection_name: str, embedding_function: MyEmbeddingFunction): client = chromadb.HttpClient(host='localhost', port=8000, settings = Settings(allow_reset=True, anonymized_telemetry=False)) client.reset() # resets the database collection = client.create_collection(collection_name) return client, collection def add_documents_to_chroma(client, collection, documents: list, embedding_function: MyEmbeddingFunction): for doc in documents: collection.add(ids=[str(uuid.uuid1())], documents=[doc], embeddings=embedding_function([doc])) def query_chroma(client, collection_name: str, query_text: str, embedding_function: MyEmbeddingFunction): db = Chroma(client=client, collection_name=collection_name, embedding_function=embedding_function) result_docs = db.similarity_search(query_text) return result_docs # Initialize clients intention_client = OpenAI(api_key=yi_token, base_url=API_BASE) embedding_generator = EmbeddingGenerator(model_name=model_name, token=hf_token, intention_client=intention_client) embedding_function = MyEmbeddingFunction(embedding_generator=embedding_generator) chroma_client, chroma_collection = initialize_chroma(collection_name="Tonic-instruct", embedding_function=embedding_function) def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, ): retrieved_text = query_documents(message) messages = [{"role": "system", "content": system_message}] for val in history: if val[0]: messages.append({"role": "user", "content": val[0]}) if val[1]: messages.append({"role": "assistant", "content": val[1]}) messages.append({"role": "user", "content": f"{retrieved_text}\n\n{message}"}) response = "" for message in intention_client.chat_completion( messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p, ): token = message.choices[0].delta.content response += token yield response def upload_documents(files): for file in files: loader = UnstructuredFileLoader(file.name) documents = loader.load_documents() add_documents_to_chroma(documents) return "Documents uploaded and processed successfully!" def query_documents(query): results = query_chroma(query) return "\n\n".join([result.content for result in results]) with gr.Blocks() as demo: with gr.Tab("Upload Documents"): with gr.Row(): document_upload = gr.File(file_count="multiple", file_types=["document"]) upload_button = gr.Button("Upload and Process") upload_button.click(upload_documents, inputs=document_upload, outputs=gr.Text()) with gr.Tab("Ask Questions"): with gr.Row(): chat_interface = gr.ChatInterface( respond, additional_inputs=[ gr.Textbox(value="You are a friendly Chatbot.", label="System message"), gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), ], ) query_input = gr.Textbox(label="Query") query_button = gr.Button("Query") query_output = gr.Textbox() query_button.click(query_documents, inputs=query_input, outputs=query_output) if __name__ == "__main__": demo.launch()