import gradio as gr from huggingface_hub import InferenceClient from torch import cuda, bfloat16 import torch import transformers from transformers import AutoTokenizer from time import time import chromadb from chromadb.config import Settings from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings import HuggingFaceEmbeddings from langchain.chains import RetrievalQA from langchain.vectorstores import Chroma from langchain.document_loaders import PyPDFLoader import requests """ For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference """ client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") def respond(message, history, system_message, max_tokens, temperature, top_p): URL = "https://www.esmo.org/content/download/6594/114963/1/ES-Cancer-de-Mama-Guia-para-Pacientes.pdf" response = requests.get(URL) open("ES-Cancer-de-Mama-Guia-para-Pacientes.pdf", "wb").write(response.content) loader = PyPDFLoader("ES-Cancer-de-Mama-Guia-para-Pacientes.pdf") documents = loader.load() text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20) all_splits = text_splitter.split_documents(documents) model_name = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2" model_kwargs = {"device": "cuda"} embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs) vectordb = Chroma.from_documents(documents=all_splits, embedding=embeddings, persist_directory="chroma_db") query = message docs = vectordb.similarity_search_with_score(query) context = [] for doc, score in docs: if score < 7: doc_details = doc.to_json()['kwargs'] context.append(doc_details['page_content']) if len(context) != 0: messages = [ {"role": "user", "content": "Basándote en la siguiente información: " + "\n".join(context) + "\n Responde en castellano a la pregunta: " + query}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=max_tokens, do_sample=True, temperature=temperature, top_k=50, top_p=top_p) answer = outputs[0]["generated_text"] return answer[answer.rfind("[/INST]") + 8:], docs else: return "No tengo información para responder a esta pregunta", docs """ For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface """ demo = 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)", ), ], ) if __name__ == "__main__": demo.launch()