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import gradio as gr
from langchain.document_loaders import PDFMinerLoader, PyMuPDFLoader
from langchain.text_splitter import CharacterTextSplitter
import chromadb
import chromadb.config
from chromadb.config import Settings
from transformers import T5ForConditionalGeneration, AutoTokenizer
import torch
import gradio as gr
import uuid
from sentence_transformers import SentenceTransformer
import os
global file_name
model_name = 'google/flan-t5-base'
model = T5ForConditionalGeneration.from_pretrained(model_name, device_map='auto', offload_folder="offload")
tokenizer = AutoTokenizer.from_pretrained(model_name)
print('flan read')
ST_name = 'sentence-transformers/sentence-t5-base'
st_model = SentenceTransformer(ST_name)
print('sentence read')
def get_context(query_text, collection):
query_emb = st_model.encode(query_text)
query_response = collection.query(query_embeddings=query_emb.tolist(), n_results=4)
context = query_response['documents'][0][0]
context = context.replace('\n', ' ').replace(' ', ' ')
return context
def local_query(query, context):
t5query = """Using the available context, please answer the question.
If you aren't sure please say i don't know.
Context: {}
Question: {}
""".format(context, query)
inputs = tokenizer(t5query, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
return tokenizer.batch_decode(outputs, skip_special_tokens=True)
def run_query(history, query):
# pdf file name input olarak verip, buraya upload event olarak gondermem gereki rmi
loader = PDFMinerLoader(pdf_filename)
doc = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(doc)
texts = [i.page_content for i in texts]
doc_emb = st_model.encode(texts)
doc_emb = doc_emb.tolist()
ids = [str(uuid.uuid1()) for _ in doc_emb]
client = chromadb.Client()
collection = client.create_collection("test_db")
collection.add(
embeddings=doc_emb,
documents=texts,
ids=ids
)
context = get_context(query, collection)
result = local_query(query, context)
history = history.append(query)
return history, result
# def load_document(pdf_filename):
# loader = PDFMinerLoader(pdf_filename)
# doc = loader.load()
# text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
# texts = text_splitter.split_documents(doc)
# texts = [i.page_content for i in texts]
# doc_emb = st_model.encode(texts)
# doc_emb = doc_emb.tolist()
# ids = [str(uuid.uuid1()) for _ in doc_emb]
# client = chromadb.Client()
# collection = client.create_collection("test_db")
# collection.add(
# embeddings=doc_emb,
# documents=texts,
# ids=ids
# )
# return 'Success'
def upload_pdf(file):
try:
# Check if the file is not None before accessing its attributes
if file is not None:
# Save the uploaded file
file_name = file.name
# messsage = load_document(file_name)
return 'Successfully uploaded!'
else:
return "No file uploaded."
except Exception as e:
return f"An error occurred: {e}"
with gr.Blocks() as demo:
btn = gr.UploadButton("Upload a PDF", file_types=[".pdf"])
output = gr.Textbox(label="Output Box")
chatbot = gr.Chatbot(value=[], elem_id="chatbot")
with gr.Row():
with gr.Column(scale=0.70):
txt = gr.Textbox(
show_label=False,
placeholder="Enter a question",
)
# Event handler for uploading a PDF
btn.upload(fn=upload_pdf, inputs=[btn], outputs=[output])
txt.submit(run_query, [chatbot, txt], [chatbot, txt])
#.then(
# generate_response, inputs =[chatbot,],outputs = chatbot,)
demo.launch()
# iface = gr.Interface(
# fn=upload_pdf,
# inputs="file",
# outputs="text",
# title="PDF File Uploader",
# description="Upload a PDF file and get its filename.",
# )
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