File size: 4,079 Bytes
301614f 9035153 604b59c 6c36800 604b59c 9a664f0 725d485 2cd4e0a 058d9a5 2cd4e0a 604b59c 058d9a5 725d485 9035153 dffeb2d 725d485 dffeb2d 725d485 9035153 dffeb2d 9035153 5573a68 301614f 725d485 301614f 544ea93 301614f 9035153 42d5877 9035153 5573a68 2024184 dffeb2d 911a8be 5573a68 911a8be 725d485 23d4171 725d485 911a8be 5573a68 911a8be 23d4171 2024184 dffeb2d a610295 24464a6 dffeb2d 2024184 fc4d061 7471e3e 23d4171 7f9bf9b 2347d67 a610295 dffeb2d 7c1d20d fc4d061 2024184 fc4d061 058d9a5 |
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 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
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):
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], queue=False)
#.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.",
# )
|