app.py

import gradio as gr
from sentence_transformers import SentenceTransformer
from huggingface_hub import InferenceClient
import pandas as pd
import torch
import math
import httpcore
setattr(httpcore, 'SyncHTTPTransport', 'AsyncHTTPProxy')

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def get_detailed_instruct(task_description: str, query: str) -> str:
        return f'Instruct: {task_description}\nQuery: {query}'

def respond(
    message,
    history: list[tuple[str, str]],
    max_tokens = 2048,
    temperature = 0.7,
    top_p = 0.95,
):
    #system role
    messages = [{"role": "system", "content": "You are a moslem bot that always give answer based on quran and hadith!"}]

    #make a moslem bot
    messages.append({"role": "user", "content": "I want you to answer strictly based on quran and hadith"})
    messages.append({"role": "assistant", "content": "I'd be happy to help! Please go ahead and provide the sentence you'd like me to analyze. Please specify whether you're referencing a particular verse or hadith (Prophetic tradition) from the Quran or Hadith, or if you're asking me to analyze a general statement."})

    #adding references
    df = pd.read_csv("moslem-bot-reference.csv")
    for index, row in df.iterrows():
        messages.append({"role": "user", "content": row['user']})
        messages.append({"role": "assistant", "content": row['assistant']})

    #adding more references
    selected_references = torch.load('selected_references.sav', map_location=torch.device('cpu'))
    encoded_questions = torch.load('encoded_questions.sav', map_location=torch.device('cpu'))
    
    task = 'Given a web search query, retrieve relevant passages that answer the query'
    queries = [
        get_detailed_instruct(task, message)
    ]

    model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')
    query_embeddings = model.encode(queries, convert_to_tensor=True, normalize_embeddings=True)
    scores = (query_embeddings @ encoded_questions.T) * 100
    selected_references['similarity'] = scores.tolist()[0]
    sorted_references = selected_references.sort_values(by='similarity', ascending=False)
    sorted_references = sorted_references.head(3)
    sorted_references = selected_references.sort_values(by='similarity', ascending=True)

    from googletrans import Translator
    translator = Translator()
    
    for index, row in sorted_references.iterrows():
        print(index)
        print(row['user'])
        user = translator.translate(row['user'])
        print(user)
        print(row['assistant'])
        assistant = translator.translate(row['assistant'])
        print(assistant)
        messages.append({"role": "user", "content":user })
        messages.append({"role": "assistant", "content": assistant})

    #history from chat session
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    #latest user question
    messages.append({"role": "user", "content": message})
    print(messages)

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
    examples=[
                ["Why is men created?"],
                ["How is life after death?"],
                ["Please tell me about superstition!"],
                ["How moses defeat pharaoh?"],
                ["Please tell me about inheritance law in Islam!"],
                ["A woman not wear hijab"],
                ["Worshipping God beside Allah"],
                ["Blindly obey a person"],
                ["Make profit from lending money to a friend"],
            ],
)

if __name__ == "__main__":
    demo.launch()