app.py

created a app.py file

app.py

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+# !pip -q install git+https://github.com/huggingface/transformers # need to install from github
+!pip install -q datasets loralib sentencepiece
+!pip -q install bitsandbytes accelerate xformers
+!pip -q install langchain
+!pip -q install gradio
+
+!pip -q install peft chromadb
+!pip -q install unstructured
+!pip install -q sentence_transformers
+!pip -q install pypdf
+
+from google.colab import drive
+drive.mount('/content/drive')
+
+"""## LLaMA2 7B Chat
+
+"""
+
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
+
+bnb_config = BitsAndBytesConfig(load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=False)
+
+model_id = "meta-llama/Llama-2-7b-chat-hf"
+
+#daryl149/llama-2-7b-chat-hf
+#meta-llama/Llama-2-7b-chat-hf
+
+tokenizer = AutoTokenizer.from_pretrained(model_id,token='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')
+model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config = bnb_config,device_map={"":0},token='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')
+
+import json
+import textwrap
+
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+DEFAULT_SYSTEM_PROMPT = """\
+You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
+
+
+
+def get_prompt(instruction, new_system_prompt=DEFAULT_SYSTEM_PROMPT ):
+    SYSTEM_PROMPT = B_SYS + new_system_prompt + E_SYS
+    prompt_template =  B_INST + SYSTEM_PROMPT + instruction + E_INST
+    return prompt_template
+
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.document_loaders import PyPDFLoader
+
+loader = PyPDFLoader("/content/drive/MyDrive/Gen AI and LLM/data/data.pdf")
+
+text_splitter = RecursiveCharacterTextSplitter(
+    # Set a really small chunk size, just to show.
+    chunk_size = 800,
+    chunk_overlap  = 50,
+    length_function = len,
+)
+
+pages = loader.load_and_split(text_splitter)
+
+db = Chroma.from_documents(pages, HuggingFaceEmbeddings())
+
+instruction = "Given the context that has been provided. \n {context}, Answer the following question - \n{question}"
+
+system_prompt = """You are an expert in question and answering.
+You will be given a context to answer from. Be precise in your answers wherever possible.
+In case you are sure you don't know the answer then you say that based on the context you don't know the answer.
+In all other instances you provide an answer to the best of your capability. Cite urls when you can access them related to the context."""
+
+get_prompt(instruction, system_prompt)
+
+"""## Setting up with LangChain"""
+
+from langchain import HuggingFacePipeline
+from langchain import PromptTemplate,  LLMChain
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferWindowMemory
+
+template = get_prompt(instruction, system_prompt)
+print(template)
+
+prompt = PromptTemplate(template=template, input_variables=["context", "question"])
+
+memory = ConversationBufferWindowMemory(
+    memory_key="chat_history", k=5,
+    return_messages=True
+)
+
+retriever = db.as_retriever()
+
+def create_pipeline(max_new_tokens=512):
+    pipe = pipeline("text-generation",
+                model=model,
+                tokenizer = tokenizer,
+                max_new_tokens = max_new_tokens,
+                temperature = 0.1)
+    return pipe
+
+class GunaBot:
+  def __init__(self, memory, prompt, task:str = "text-generation", retriever = retriever):
+    self.memory = memory
+    self.prompt = prompt
+    self.retriever = retriever
+
+
+
+  def create_chat_bot(self, max_new_tokens = 512):
+    hf_pipe = create_pipeline(max_new_tokens)
+    llm = HuggingFacePipeline(pipeline =hf_pipe)
+    qa = ConversationalRetrievalChain.from_llm(
+      llm=llm,
+      retriever=self.retriever,
+      memory=self.memory,
+      combine_docs_chain_kwargs={"prompt": self.prompt}
+  )
+    return qa
+
+Guna_bot = GunaBot(memory = memory, prompt = prompt)
+
+bot = Guna_bot.create_chat_bot()
+
+import gradio as gr
+import random
+import time
+
+def clear_llm_memory():
+  bot.memory.clear()
+
+def update_prompt(sys_prompt):
+  if sys_prompt == "":
+    sys_prompt = system_prompt
+  template = get_prompt(instruction, sys_prompt)
+
+  prompt = PromptTemplate(template=template, input_variables=["context", "question"])
+
+  bot.combine_docs_chain.llm_chain.prompt = prompt
+
+with gr.Blocks() as demo:
+    update_sys_prompt = gr.Textbox(label = "Update System Prompt")
+    chatbot = gr.Chatbot(label="Guna Bot", height = 300)
+    msg = gr.Textbox(label = "Question")
+    clear = gr.ClearButton([msg, chatbot])
+    clear_memory = gr.Button(value = "Clear LLM Memory")
+
+
+    def respond(message, chat_history):
+        bot_message = bot({"question": message})['answer']
+        chat_history.append((message, bot_message))
+        return "", chat_history
+
+    msg.submit(respond, inputs=[msg, chatbot], outputs=[msg, chatbot])
+    clear_memory.click(clear_llm_memory)
+    update_sys_prompt.submit(update_prompt, inputs=update_sys_prompt)
+
+demo.launch(share=False, debug=True)
+