File size: 2,449 Bytes
055a3f7 136ff58 055a3f7 | 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 | import os
import os.path
import pymongo
from pymongo.mongo_client import MongoClient
import gradio as gr
import certifi
MONGODB_ATLAS_DB_PASSWORD = os.environ['MONGODB_ATLAS_DB_PASSWORD']
from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
from llama_index import (
VectorStoreIndex,
SimpleDirectoryReader,
StorageContext,
load_index_from_storage,
)
mongo_uri = (
f"mongodb+srv://arkiitkgp:{MONGODB_ATLAS_DB_PASSWORD}@genaicluster0.fgmvvsx.mongodb.net/?retryWrites=true&w=majority"
)
mongodb_client = pymongo.MongoClient(mongo_uri, tlsCAFile=certifi.where())
# Send a ping to confirm a successful connection
try:
mongodb_client.admin.command('ping')
print("Pinged your deployment. You successfully connected to MongoDB!")
except Exception as e:
print(e)
store = MongoDBAtlasVectorSearch(mongodb_client, db_name='testdb1', collection_name='dummyIndex', index_name='vector_index')
# storage_context = StorageContext.from_defaults(vector_store=store)
# documents = SimpleDirectoryReader(
# "temp",
# file_metadata=get_metadata_from_filename,
# required_exts=['.pdf', '.docx'],
# recursive=True,
# ).load_data()
# construct index
# index = VectorStoreIndex.from_documents(
# documents, storage_context=storage_context
# )
index = VectorStoreIndex.from_vector_store(store)
def get_answer(query):
response = index.as_query_engine(streaming=True).query(query)
# response.print_response_stream()
return response
###### GRADIO #################################
classes = ['Class 10', 'Class 9']
subjects = ['Science']
def get_streaming_answer(input_query):
r = get_answer(input_query)
ans = ""
for new_tokens in r.response_gen:
ans += new_tokens
yield ans
with gr.Blocks() as demo:
gr.Markdown("""# NCERT Tutor \n ### Type your question...""")
with gr.Tab("For Students"):
choose_class = gr.Dropdown(label= "Class", choices=classes)
choose_subject = gr.Dropdown(label="Subject", choices=subjects)
question_input = gr.Textbox(label="Enter question...")
submit_button = gr.Button("Ask")
response_output = gr.Textbox(label="Answer...", lines=5)
with gr.Tab("For Teachers"):
gr.Markdown("Coming soon...")
submit_button.click(fn=get_streaming_answer, inputs=question_input, outputs=response_output)
demo.launch(share=True, debug=True) |