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| import gradio as gr | |
| import json | |
| import os | |
| import shutil | |
| # import magic | |
| # import ollama | |
| from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings | |
| from langchain_community.vectorstores.qdrant import Qdrant | |
| from langchain_core.runnables import RunnablePassthrough | |
| from langchain_core.documents import Document | |
| from langchain_core.prompts import ChatPromptTemplate | |
| from langchain_core.output_parsers import StrOutputParser | |
| # from langchain_community.chains import | |
| from langchain_community.chat_models import ChatOllama | |
| from langchain_chroma import Chroma | |
| from hugchat import hugchat | |
| from hugchat.login import Login | |
| import dotenv | |
| from utils import HuggingChat | |
| from langchain import PromptTemplate | |
| dotenv.load_dotenv() | |
| class GradioApp: | |
| def __init__(self): | |
| # self.llm = ChatOllama(model="phi3:3.8b", base_url="http://localhost:11434", num_gpu=32) | |
| template = """ | |
| You are a helpful health assistant. These Human will ask you a questions about their pregnancy health. | |
| Use following piece of context to answer the question. | |
| If you don't know the answer, just say you don't know. | |
| Keep the answer within 2 sentences and concise. | |
| Context: {context} | |
| Question: {question} | |
| Answer: | |
| """ | |
| self.llm = HuggingChat(email = os.getenv("HF_EMAIL") , psw = os.getenv("HF_PASS") ) | |
| self.chain = (self.llm | StrOutputParser()) | |
| def user(self,user_message, history): | |
| return "", history + [[user_message, None]] | |
| def bot(self,history): | |
| print(history) | |
| prompt = history[-1][0] or "" | |
| for chunks in self.chain.stream(prompt): | |
| history[-1][1] = history[-1][1] or "" | |
| history[-1][1] += chunks | |
| yield history | |
| history[-1][1] = history[-1][1] or "" | |
| history[-1][1] += self.chain.invoke(prompt) | |
| print(history[-1][1]) | |
| print(history) | |
| return history | |
| with gr.Blocks() as demo: | |
| gradio_app = GradioApp() | |
| # files = gr.Files(label="Upload Documents and Medical Reports", type="filepath", file_types=["pdf", "docx", "jpg", "jpeg", "png"]) | |
| # upload_button = gr.UploadButton(label="Upload Documents and Medical Reports", type="filepath", file_count='multiple', file_types=["pdf", "docx", "jpg", "jpeg", "png"], ) | |
| output_text = gr.Markdown(label="Output", value=" ") | |
| infer_status = gr.Label("Infer Status: ", visible=False) | |
| # upload_button.upload(gradio_app.upload_files, upload_button, [files, output_text]) | |
| chatbot = gr.Chatbot() | |
| msg = gr.Textbox() | |
| clear = gr.Button("Clear") | |
| msg.submit(gradio_app.user, [msg, chatbot], [msg, chatbot], queue=False).then( | |
| gradio_app.bot, chatbot, chatbot | |
| ) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| # demo.queue() | |
| demo.launch(share=True) | |