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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) |