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| import os,gc,shutil | |
| import gradio as gr | |
| from util.conversation_rag import Conversation_RAG | |
| from util.index import * | |
| import torch | |
| from model_setup import ModelSetup | |
| def load_models(hf_token,embedding_model,llm): | |
| global model_setup | |
| model_setup = ModelSetup(hf_token, embedding_model, llm) | |
| success_prompt = model_setup.setup() | |
| return success_prompt | |
| def upload_and_create_vector_store(file,embedding_model): | |
| # Save the uploaded file to a permanent location | |
| file_path = file.name | |
| split_file_name = file_path.split("/") | |
| file_name = split_file_name[-1] | |
| current_folder = os.path.dirname(os.path.abspath(__file__)) | |
| root_folder = os.path.dirname(current_folder) | |
| data_folder = os.path.join(root_folder, "data") | |
| permanent_file_path = os.path.join(data_folder, file_name) | |
| shutil.copy(file.name, permanent_file_path) | |
| # Access the path of the saved file | |
| print(f"File saved to: {permanent_file_path}") | |
| if embedding_model == "all-roberta-large-v1_1024d": | |
| embedding_model_repo_id = "sentence-transformers/all-roberta-large-v1" | |
| elif embedding_model == "all-mpnet-base-v2_768d": | |
| embedding_model_repo_id = "sentence-transformers/all-mpnet-base-v2" | |
| index_success_msg = create_vector_store_index(permanent_file_path,embedding_model_repo_id) | |
| return index_success_msg | |
| def get_chat_history(inputs): | |
| res = [] | |
| for human, ai in inputs: | |
| res.append(f"Human:{human}\nAssistant:{ai}") | |
| return "\n".join(res) | |
| def add_text(history, text): | |
| history = history + [[text, None]] | |
| return history, "" | |
| conv_qa = Conversation_RAG() | |
| def bot(history, | |
| instruction="Use the following pieces of context to answer the question at the end. Generate the answer based on the given context only if you find the answer in the context. If you do not find any information related to the question in the given context, just say that you don't know, don't try to make up an answer. Keep your answer expressive.", | |
| temperature=0.1, | |
| max_new_tokens=512, | |
| repetition_penalty=1.1, | |
| top_k=10, | |
| top_p=0.95, | |
| k_context=5, | |
| num_return_sequences=1, | |
| ): | |
| qa = conv_qa.create_conversation(model_setup.model, | |
| model_setup.tokenizer, | |
| model_setup.vectordb, | |
| max_new_tokens=max_new_tokens, | |
| temperature=temperature, | |
| repetition_penalty=repetition_penalty, | |
| top_k=top_k, | |
| top_p=top_p, | |
| k_context=k_context, | |
| num_return_sequences=num_return_sequences, | |
| instruction=instruction | |
| ) | |
| chat_history_formatted = get_chat_history(history[:-1]) | |
| res = qa( | |
| { | |
| 'question': history[-1][0], | |
| 'chat_history': chat_history_formatted | |
| } | |
| ) | |
| history[-1][1] = res['answer'] | |
| return history | |
| def reset_sys_instruction(instruction): | |
| default_inst = "Use the following pieces of context to answer the question at the end. Generate the answer based on the given context only if you find the answer in the context. If you do not find any information related to the question in the given context, just say that you don't know, don't try to make up an answer. Keep your answer expressive." | |
| return default_inst | |
| def clear_cuda_cache(): | |
| torch.cuda.empty_cache() | |
| gc.collect() | |
| return None |