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import gradio as gr
from pipeline import preprocessing_pipeline, conversational_rag
from pipeline import system_message, user_message
from haystack.dataclasses import ChatMessage
import time
import os
def process_files_into_docs(files,progress=gr.Progress()):
if isinstance(files, dict):
files = [files]
if not files:
return 'No file uploaded!'
preprocessing_pipeline.run({'file_type_router': {'sources': files}})
return "Database created🤗🤗"
def rag(history,question):
if history is None:
history=[]
messages = [system_message, user_message]
res = conversational_rag.run(
data = {'query_rephrase_prompt_builder' : {'query': question},
'prompt_builder': {'template': messages, 'query': question},
'memory_joiner': {'values': [ChatMessage.from_user(question)]}},
include_outputs_from=['llm','query_rephrase_llm'])
bot_message = res['llm']['replies'][0].content
streamed_message = ""
for token in bot_message.split():
streamed_message += f"{token} "
yield history + [(question, streamed_message.strip())], " "
time.sleep(0.05)
history.append((question,bot_message))
yield history, " "
EXAMPLE_FILE = "RAG Survey.pdf"
with gr.Blocks(theme=gr.themes.Soft())as demo:
gr.HTML("<center><h1>TalkToFiles - Query your documents! 📂📄</h1><center>")
gr.Markdown("""##### This AI chatbot🤖 can help you chat with your documents. Can upload <b>Text(.txt), PDF(.pdf) and Markdown(.md)</b> files.\
<b>Please do not upload confidential documents.</b>""")
with gr.Row():
with gr.Column(scale=86):
gr.Markdown("""#### ***Step 1 - Upload Documents and Initialize RAG pipeline***</br>
Can upload Multiple documents""")
with gr.Row():
file_input = gr.File(label='Upload Files', file_count='multiple',file_types=['.pdf', '.txt', '.md'],interactive=True)
with gr.Row():
process_files = gr.Button('Create Document store')
with gr.Row():
result = gr.Textbox(label="Document store", value='Document store not initialized')
#Pre-processing Events
process_files.click(fn=process_files_into_docs, inputs=file_input, outputs=result ,show_progress=True)
def load_example():
return [EXAMPLE_FILE]
with gr.Row():
gr.Examples(
examples=[[EXAMPLE_FILE]],
inputs=file_input,
examples_per_page=1,
label="Click to upload an example"
).dataset.click(fn=load_example, inputs=[], outputs=file_input)
with gr.Column(scale=200):
gr.Markdown("""#### ***Step 2 - Chat with your docs*** """)
chatbot = gr.Chatbot(label='ChatBot')
user_input = gr.Textbox(label='Enter your query', placeholder='Type here...')
with gr.Row():
submit_button = gr.Button("Submit")
clear_btn = gr.ClearButton([user_input, chatbot], value='Clear')
submit_button.click(rag, inputs=[chatbot, user_input], outputs=[chatbot, user_input])
demo.launch()
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