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import numpy as np
import gradio as gr
from z_generate import ServerlessInference
from z_embedding import load_vector_store
# STATIC TEXT DISPLAY
TXT_APP_DESCRIPTION = '''
# Just another RAG with images ✍️ 🖼️
## What its not
- Advance RAG; idk why [HF's tutorial](https://huggingface.co/learn/cookbook/en/advanced_rag) says so.
## How this works
1. First user query is passed to LLM
2. LLM generated Response is matched against images's descriptions
3. You see wonders.
'''
TXT_SOURCE_DOC_DESCRIPTION = '''
Manually Downloaded as HTML files:
1. https://en.wikipedia.org/wiki/MS_Dhoni
2. https://en.wikipedia.org/wiki/Jharkhand
2. https://en.wikipedia.org/wiki/Cricket_World_Cup
## Details
1. Vector Store is built using FAISS prior to starting this app. Although the vector store size in KBs but
- the creation and loading of the store takes processing takes ~10GB RAM and lasts 5 mins. Hence **NOT BUILDING IT DURING RUNTIME OF APP**.
- `multi_process=True` was not working with HF Space `free-tier`.
2. Currently only html of wikipedia is supported as I am not wasting resources embedding tags of HTML, instead just parsing specific locations
3. Instead of Image tag, I have parsed figure/figcaption tags; much easy and better to find. Long run you can improve that part
'''
# UI Interface
demo = gr.Blocks()
vector_text, vector_image = load_vector_store()
llm = ServerlessInference(vector_store_text=vector_text, vector_store_images=vector_image)
# Processing Functions
def update_response(query:str = "something"):
response_text, response_images = llm.perform_rag(query)
return response_text, response_images
def update_gallery(text:str = "hell"):
imgs = [
("http://www.marketingtool.online/en/face-generator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg", "Some Description"),
("http://www.marketingtool.online/en/face-generator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg", "Another Description")
]
return imgs
def ask_bot(text):
return update_response(text)
# UI Layout
with demo:
gr.Markdown(TXT_APP_DESCRIPTION)
with gr.Tabs():
with gr.TabItem("Ask Bot"):
with gr.Row(equal_height=True):
with gr.Column(scale=3):
text_input = gr.Textbox(
label="You query here",
placeholder="What positions apart from crickter did Dhoni held?"
)
with gr.Column(scale=1):
btn_generate = gr.Button("Generate Answer")
with gr.Row():
with gr.Column(scale=3):
text_output = gr.Textbox(label="Bot Response:", placeholder="Type in Query before I could answer")
with gr.Column(scale=2):
gallery = gr.Gallery(
label="Generated images", show_label=False, elem_id="gallery"
, columns=[3], rows=[1], object_fit="contain", height="auto"
)
btn_generate.click(ask_bot, text_input, outputs=[text_output, gallery])
examples = gr.Examples(
examples=[
["What are the achievements of Dhoni"],
["In which state of India Dhoni was born ?"],
["When did Dhoni won World Cup ?"],
["Tell me something about Cricket world Cup of 2011"]
],
inputs=[text_input],
)
####
with gr.TabItem("Source Documents"):
gr.Markdown(TXT_SOURCE_DOC_DESCRIPTION)
demo.launch() |