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| import gradio as gr | |
| import os | |
| from langchain_core.runnables import RunnablePassthrough | |
| from langchain_core.output_parsers import StrOutputParser | |
| from langchain_chroma import Chroma | |
| import re | |
| import dotenv | |
| from utils import HuggingChat | |
| from langchain_core.prompts import PromptTemplate | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| import langchain | |
| from langchain_groq.chat_models import ChatGroq | |
| # import json | |
| # import shutil | |
| # import magic | |
| # import ollama | |
| # from langchain_community.vectorstores.qdrant import Qdrant | |
| # from langchain_core.documents import Document | |
| # from langchain_core.prompts import ChatPromptTemplate | |
| # from langchain_community.chains import | |
| from langchain_community.chat_models import ChatOllama | |
| # from hugchat import hugchat | |
| # from langchain.callbacks import SystemMessage | |
| # from hugchat.login import Login | |
| langchain.debug = False | |
| dotenv.load_dotenv() | |
| class GradioApp: | |
| def __init__(self): | |
| self.history = [] | |
| self.links = [] | |
| # 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.template = """ | |
| You are a helpful AI bot that guides the customer or user through the website content and provides the user with exact details they want. | |
| You help everyone by answering questions, and improve your answers from previous answers in History. | |
| Don't try to make up an answer, if you don't know, ask for more detail otherwise say you can contact the support team at https://pragetx.com/contact-us/ | |
| Answer in the same language the question was asked. | |
| Answer in a way that is easy to understand. | |
| Try to limit the answer to 3-4 sentences. | |
| Provide source url as well at the end of the answer. | |
| Do not say "Based on the information you provided, ..." or "I think the answer is...". Just answer the question directly in detail. | |
| History: {chat_history} | |
| Context: {context} | |
| Question: {question} | |
| Answer: | |
| """ | |
| self.prompt = PromptTemplate( | |
| template=self.template, | |
| input_variables=["chat_history","context", "question"] | |
| ) | |
| self.db = Chroma(persist_directory="./pragetx_chroma", embedding_function=HuggingFaceEmbeddings(), collection_name="pragetx") | |
| # self.llm = ChatOllama(model="phi3:3.8b", base_url="http://localhost:11434", num_gpu=16) | |
| # self.llm = HuggingChat(email = os.getenv("HF_EMAIL") , psw = os.getenv("HF_PASS") ) | |
| # self.llm = HuggingChat(email = "brij1808" , psw = "Brijesh321@R" ) | |
| os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY") | |
| self.llm = ChatGroq( | |
| model="gemma2-9b-it", | |
| temperature=1, | |
| max_tokens=512, | |
| timeout=5, | |
| max_retries=2, | |
| ) | |
| self.chain = ( | |
| {"chat_history": self.chat_history, "context": self.db.as_retriever(search_kwargs={"k":3}), "question": RunnablePassthrough()} | | |
| self.prompt | | |
| self.llm | | |
| StrOutputParser()) | |
| def chat_history(self, history): | |
| print(self.history) | |
| print("\n".join(f"##Human: {x[0]}\n{'##Bot: '+x[1] if x[1] else ''}" for x in self.history)) | |
| return "\n".join(f"##Human: {x[0]}\n{'##Bot: '+x[1] if x[1] else ''}" for x in self.history) | |
| def user(self,user_message, history): | |
| self.history = history + [[user_message, None]] | |
| return "", history + [[user_message, None]] | |
| def bot(self,history): | |
| print(history) | |
| prompt = history[-1][0] or "" | |
| for chunks in self.chain.stream(prompt): | |
| print('chunks: ', chunks) | |
| history[-1][1] = history[-1][1] or "" | |
| history[-1][1] += chunks | |
| yield history | |
| history[-1][1] = history[-1][1] or "" | |
| self.history = history | |
| # extract links in the response and store them | |
| links = re.findall(r'(https?://\S+)', history[-1][1]) | |
| print(links) | |
| self.links = links | |
| print(history[-1][1]) | |
| print(history) | |
| # def list_links(self): | |
| # print( "link_buttons", self.links) | |
| # link_buttons = [] | |
| # for link in range(5): | |
| # if link < len(self.links): | |
| # btn = gr.Button(f"Open {self.links[link]}", visible=True) | |
| # else: | |
| # btn = gr.Button(visible=False) | |
| # link_buttons.append(btn) | |
| # return link_buttons | |
| ''' | |
| with gr.Blocks() as demo: | |
| gradio_app = GradioApp() | |
| # link_buttons = [] | |
| # 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() | |
| # with gr.Row(): | |
| # for link in range(5): | |
| # btn = gr.Button(visible=False) | |
| # link_buttons.append(btn) | |
| msg = gr.Textbox() | |
| clear = gr.Button("Clear") | |
| # for link in range(5): | |
| # print(gradio_app.links) | |
| # btn.click(lambda: None, None, None, js=f"window.location.assign('{gradio_app.links[link]}');" if link < len(gradio_app.links) else None) | |
| msg.submit(gradio_app.user, [msg, chatbot], [msg, chatbot], queue=False).then( | |
| gradio_app.bot, chatbot, chatbot | |
| ) | |
| # .then( | |
| # gradio_app.list_links, None, link_buttons | |
| # ) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| demo.queue() | |
| demo.launch(share=True, server_name="0.0.0.0", root_path="/bot") | |
| ''' | |
| with gr.Blocks(css=""" | |
| #chatbot {margin-bottom: -20px;} | |
| footer {display: none !important;} | |
| #colab-link-container {display: none !important;} | |
| #chatbot .wrap > .label { | |
| display: none !important; | |
| } | |
| """) as demo: | |
| gradio_app = GradioApp() | |
| output_text = gr.Markdown(label="Output", value=" ") | |
| infer_status = gr.Label("Infer Status: ", visible=False) | |
| with gr.Column(scale=1, min_width=800): | |
| chatbot = gr.Chatbot(elem_id="chatbot", height=400) | |
| with gr.Row(): | |
| msg = gr.Textbox(label="Enter your message", show_label=False, lines=1) | |
| send_button = gr.Button("Send", elem_id="send_button", scale=0) | |
| msg.submit(gradio_app.user, [msg, chatbot], [msg, chatbot], queue=False).then( | |
| gradio_app.bot, chatbot, chatbot | |
| ) | |
| send_button.click(gradio_app.user, [msg, chatbot], [msg, chatbot], queue=False).then( | |
| gradio_app.bot, chatbot, chatbot | |
| ) | |
| demo.queue() | |
| demo.launch(share=True, server_name="0.0.0.0", root_path="/bot") |