Spaces:
Running
Running
| 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 sunni moslem bot that always give answer based on quran, hadith, and the companions of prophet Muhammad!"}] | |
| #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(1) | |
| 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(f'{row["user"]}') | |
| user = translator.translate(f'{row["user"]}', src='ar', dest='en').text | |
| print(user) | |
| #print(row['assistant']) | |
| assistant = translator.translate(row['assistant']).text | |
| #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 | |
| from googletrans import Translator | |
| translator = Translator() | |
| message_language = translator.detect(message).lang | |
| print(message_language) | |
| en_message = translator.translate(message).text | |
| messages.append({"role": "user", "content": en_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 | |
| translated_response = translator.translate(response, src='en', dest=message_language).text | |
| yield translated_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() |