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import gradio as gr
import os
import tempfile
from langchain.document_loaders import UnstructuredPDFLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.chains import RetrievalQA
from langchain.schema import AIMessage, HumanMessage
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain import HuggingFaceHub
# Set your API keys
API_KEY = os.environ["API_KEY"]
# Create a temporary upload directory
# Define global variables for loaders and index
index = None
def chat(message,history):
global index
history_langchain_format = []
for human, ai in history:
history_langchain_format.append(HumanMessage(content=human))
history_langchain_format.append(AIMessage(content=ai))
history_langchain_format.append(HumanMessage(content=message))
history_langchain_format.append(HumanMessage(content=message))
# Create the index (update index)
llm2 = HuggingFaceHub(repo_id="declare-lab/flan-alpaca-large", model_kwargs={"temperature": 0, "max_length": 512},huggingfacehub_api_token = API_KEY )
chain = RetrievalQA.from_chain_type(llm=llm2,
chain_type="stuff",
retriever=index.vectorstore.as_retriever(),
input_key="question")
# Perform question-answering on the uploaded PDF with the user's question
gpt_response = chain.run("Based on the file you have processed, provide a related answer to this question: "+ message)
return gpt_response
# Create a Gradio interface for chat
chat_interface = gr.ChatInterface(
chat,
theme=gr.themes.Soft()
)
with gr.Blocks(theme=gr.themes.Soft()) as demo:
with gr.Row():
with gr.Column(scale=1):
with gr.Row():
upload_file = gr.File(label="Upload a PDF",file_types=["pdf"])
with gr.Row():
upload_button = gr.Button(label="Upload a PDF")
with gr.Row():
text = gr.Textbox(label="Status")
def load_file(pdf_file):
global index
pdf_loader = UnstructuredPDFLoader(pdf_file.name)
index = VectorstoreIndexCreator(
embedding=HuggingFaceEmbeddings(),
text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
).from_loaders([pdf_loader])
return "DONE ✅"
upload_button.click(load_file, [upload_file], text)
with gr.Column(scale=2):
chat_interface = gr.ChatInterface(
chat,
theme=gr.themes.Soft()
)
demo.queue().launch(inline=False)
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