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from langchain_core.prompts import PromptTemplate
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
import gradio as gr
from transformers import pipeline
import numpy as np
from langchain_ollama import OllamaLLM
from langchain_huggingface import HuggingFaceEmbeddings
from load_document import load_data
from split_document import split_docs
from embed_docs import embed_docs
from retrieve import retrieve
from datetime import datetime
from js import js
from theme import theme
import os
import glob
import requests
# Initialize our speech pipeline
transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en", device="cpu")
pretext = ""
def send_to_chat(question: str, history=None):
payload = {
"question": question
}
response = requests.post("https://sadiksmart0-the-law.hf.space/query", json=payload)
if response.status_code != 200:
print(f"Error {response.status_code}:")
return f"Error {response.status_code}: Okay Unable to fetch response from server."
return response.json().get("answer", "No answer returned.")
pretext = ""
def transcribe(audio):
global pretext
if audio is None:
return "Please record again. Loud and clear audio is required."
sr, y = audio
# Convert to mono if stereo
if y.ndim > 1:
y = y.mean(axis=1)
y = y.astype(np.float32)
y /= np.max(np.abs(y))
pretext = transcriber({"sampling_rate": sr, "raw": y})["text"]
return pretext
with gr.Blocks(title="Know The Law", theme=theme, js=js) as demo:
with gr.Row():
with gr.Column():
gr.Markdown("# Know The Law")
audio_input = gr.Audio(
label="Input Audio",
sources=["microphone"],
type="numpy",
container=True,
interactive=True,
waveform_options=gr.WaveformOptions(waveform_color="#B83A4B"),
)
output_text = gr.Textbox(
interactive=True,
submit_btn=True, # Enables submit event
label="Transcription Output",
visible=False # Made it invincible
)
audio_input.change(transcribe, inputs=[audio_input], outputs=[output_text])
gr.Markdown("# What does the Law say?")
chat = gr.ChatInterface(
send_to_chat,
chatbot=gr.Chatbot(height=300, type="messages"),
textbox=gr.Textbox(
placeholder="Ask me a question related to Nigerian law",
container=True,
scale=7,
submit_btn=True,
type="text"
),
type="messages",
examples=[
"How can I file a complaint against police misconduct?",
"What is the process for obtaining a court order?",
"What are the legal requirements for starting a business in Nigeria?"
],
title="Law Assistant",
run_examples_on_click=True,
save_history=True,
cache_examples=True
)
chat_input = chat.textbox # Get chatbot's text input
# Autofill chatbot input box with transcribed text
output_text.change(lambda x: x, inputs=[output_text], outputs=[chat_input])
# Submit event: When user presses Enter on output_text, it submits to chat
output_text.submit(send_to_chat, inputs=[output_text, chat.chatbot], outputs=chat.chatbot)
if __name__ == "__main__":
demo.launch(share=True, server_port=8001) |