Spaces:
Sleeping
Sleeping
File size: 2,670 Bytes
5e09e0a 5e80009 d522487 5e80009 067a3d4 5e80009 fa91957 5c64c3f 5e80009 5e09e0a b93e0f2 5e09e0a 5e80009 c15373d 5e09e0a 067a3d4 5e80009 6a2dc29 5e80009 fa91957 5e80009 067a3d4 5e80009 fa91957 ee7a93d 938d0d4 25069ce ee7a93d 5e09e0a ade9294 5e09e0a fa91957 1c17feb 1bfe6b8 5e09e0a d5283e7 b93e0f2 938d0d4 5e09e0a 6690b2c 5e09e0a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
import gradio as gr
from langchain.llms import Replicate
from langchain.vectorstores import Pinecone
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
from langchain.llms import HuggingFaceHub
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import ConversationalRetrievalChain
from datasets import load_dataset
import os
key = os.environ.get('API')
os.environ["REPLICATE_API_TOKEN"] = key
import sentence_transformers
def loading_pdf():
return "Loading..."
def pdf_changes(pdf_doc):
loader = PyPDFLoader(pdf_doc.name)
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings()
db = Chroma.from_documents(texts, embeddings)
retriever = db.as_retriever(search_kwargs={'k': 2})
llm = Replicate(
model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
input={"temperature": 0.2, "max_length": 3000, "length_penalty":0.1, "num_beams":3}
)
global qa
qa = ConversationalRetrievalChain.from_llm(
llm,
retriever,
return_source_documents=True
)
return "Ready"
def query(history, text):
langchain_history = [(msg[1], history[i+1][1] if i+1 < len(history) else "") for i, msg in enumerate(history) if i % 2 == 0]
result = qa({"question": text, "chat_history": langchain_history})
new_history = history + [(text,result['answer'])]
return new_history,""
css="""
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""
title = """
<div style="text-align: center;max-width: 700px;">
<h1>Chat with PDF</h1>
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
with gr.Column():
pdf_doc = gr.File(label="Load a PDF", file_types=['.pdf'], type="file")
load_pdf = gr.Button("Load PDF")
langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
submit_btn = gr.Button("Send message")
load_pdf.click(pdf_changes, inputs=[pdf_doc], outputs=[langchain_status], queue=False)
question.submit(query, [chatbot, question], [chatbot, question])
submit_btn.click(query, [chatbot, question], [chatbot, question])
demo.launch()
|