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| # -*- coding: utf-8 -*- | |
| """SimpleChatBot_OpenSourceModel_WithUI.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1q7EXhcR6gncrcwySFbN7u9fOIwTc4LtD | |
| ##*** Note : *** You will be NOT be charged for this exercise. Everything is OpenSource! | |
| ### This notebook presents how to make a simple conversational chatbot using Open Source language model that we will download from hugging-face hub | |
| ### Fix the UTF-8 encoding | |
| """ | |
| """### Install the python packages. They are need to execute necessary to make the program work""" | |
| """### Import the necessary libraries""" | |
| from langchain.llms.huggingface_pipeline import HuggingFacePipeline | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from transformers import BitsAndBytesConfig | |
| from langchain.chains import LLMChain | |
| from langchain.prompts import PromptTemplate | |
| from langchain.embeddings import (OpenAIEmbeddings, HuggingFaceEmbeddings) | |
| from langchain.schema import StrOutputParser | |
| from langchain.schema.runnable import RunnablePassthrough | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import Chroma | |
| from langchain.document_loaders import PyPDFLoader | |
| from langchain.callbacks.manager import CallbackManager | |
| from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
| """### Select and download the model from Hugging face | |
| #### Hugging face hub contains a lot of pre-trained AI models related to computer vision, NLP, etc. For our task, we would need to use a text-generation model. Follow the steps below to choose and download a model | |
| 1. Go to this link -> https://huggingface.co/models?pipeline_tag=text-generation&sort=trending | |
| 2. For this example, we will be using the Mitsral 7B Instruct v0.2 [https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2] | |
| """ | |
| torch.set_default_device("cuda") | |
| model_id = "mistralai/Mistral-7B-Instruct-v0.2" | |
| model = AutoModelForCausalLM.from_pretrained(model_id, | |
| device_map='auto', | |
| torch_dtype="auto", | |
| load_in_4bit=True, | |
| trust_remote_code=True, | |
| low_cpu_mem_usage=True) | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) | |
| """### Setup a text generation pipeline | |
| """ | |
| from transformers import pipeline | |
| pipe = pipeline( | |
| task = "text-generation", | |
| model = model, | |
| tokenizer = tokenizer, | |
| pad_token_id = tokenizer.eos_token_id, | |
| temperature = 0.3, | |
| top_k = 50, | |
| top_p = 0.95, | |
| max_new_tokens=3072, | |
| repetition_penalty = 1.2 | |
| ) | |
| """### Create an llm object""" | |
| llm = HuggingFacePipeline(pipeline = pipe) | |
| """### Create a simple prompt tempelate using Langchain framework""" | |
| template = """ | |
| "<s>[INST] You are a question and answering bot | |
| You always respond with a funny twist, and keep your | |
| answers short. Now answer this Question : {question}. | |
| To keep you more stateful, you also get help with previous | |
| chat history : {chat_history}[/INST] | |
| """ | |
| prompt = PromptTemplate(template=template, input_variables=["question", "chat_history"]) | |
| """### Create an llm chain""" | |
| llm_chain = LLMChain(prompt=prompt, llm=llm) | |
| """### Try invoking the LLM, with a simple chain""" | |
| def ask_me_chat_completions(query, chat_history, llm_chain): | |
| response = llm_chain.run({"question":query,"chat_history":chat_history}) | |
| return response | |
| """### Create a UI for more interactive conversation! | |
| #### **Excercise ** | |
| 1. Can you make the UI look better ? | |
| 2. Can you include more customizations (for example, setting up something like OpenAI playground ? Check out this link for how it looks like -> https://platform.openai.com/playground?mode=chat) | |
| 3. Host your application by creating space on HuggingFaceHub | |
| """ | |
| import gradio as gr | |
| # Initialize chat history | |
| chat_history = [] | |
| def chat_interface(query): | |
| global chat_history | |
| if query: # Ensure the query is not empty | |
| response = ask_me_chat_completions(query, chat_history, llm_chain) | |
| formatted_response = f"Q: {query}\nA: {response}" # Format the response | |
| chat_history.append(formatted_response) # Append the formatted response to the history | |
| return formatted_response # Return only the latest Q&A for display | |
| return "" # Return an empty string if the query is empty | |
| # Create the Gradio interface | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Chat"): | |
| query_input = gr.Textbox(label="Ask your questions here", placeholder="Type your question and press submit...") | |
| submit_button = gr.Button("Submit") | |
| response_output = gr.Textbox(label="Response", interactive=False, lines=6, value="", placeholder="Your answer will appear here...") | |
| submit_button.click(chat_interface, inputs=query_input, outputs=response_output) | |
| # Launch the Gradio app | |
| demo.launch() | |