app.py

from datasets import load_dataset
from datasets import Dataset
#from langchain.docstore.document import Document as LangchainDocument
# from langchain.memory import ConversationBufferMemory
from sentence_transformers import SentenceTransformer
import faiss
import time
#import torch
import pandas as pd

from transformers import AutoTokenizer, AutoModelForCausalLM
#from transformers import AutoModelForCausalLM, AutoModel
from transformers import TextIteratorStreamer
from threading import Thread
#from transformers import LlamaForCausalLM, LlamaTokenizer
#git lfs install
#from ctransformers import AutoModelForCausalLM, AutoConfig, Config, AutoTokenizer

#from huggingface_hub import InferenceClient
from huggingface_hub import Repository, upload_file
import os


HF_TOKEN = os.getenv('HF_Token')
#Log_Path="./Logfolder"
logfile = 'DiabetesChatLog.txt'
historylog = [{
        "Prompt": '',
        "Output": ''
}]

llm_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
# TheBloke/Llama-2-7B-Chat-GGML , TinyLlama/TinyLlama-1.1B-Chat-v1.0 , microsoft/Phi-3-mini-4k-instruct, health360/Healix-1.1B-V1-Chat-dDPO
# TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF and tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf not working

model = AutoModelForCausalLM.from_pretrained(llm_model)
tokenizer = AutoTokenizer.from_pretrained(llm_model)
#initiate model and tokenizer

data = load_dataset("Namitg02/Test", split='train', streaming=False)
#Returns a list of dictionaries, each representing a row in the dataset.
length = len(data)

embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
embedding_dim = embedding_model.get_sentence_embedding_dimension()
# Returns dimensions of embedidng


index =  faiss.IndexFlatL2(embedding_dim)
data.add_faiss_index("embeddings", custom_index=index) 
# adds an index column for the embeddings

print("check1d")
#question = "How can I reverse Diabetes?"

SYS_PROMPT = """You are an assistant for answering questions.
You are given the extracted parts of documents and a question. Provide a conversational answer.
If you don't know the answer, just say "I do not know." Don't make up an answer."""
# Provides context of how to answer the question


print("check2")

# memory = ConversationBufferMemory(return_messages=True)


terminators = [
    tokenizer.eos_token_id, # End-of-Sequence Token that indicates where the model should consider the text sequence to be complete
    tokenizer.convert_tokens_to_ids("<|eot_id|>") # Converts a token strings in a single/ sequence of integer id using the vocabulary
]
# indicates the end of a sequence


def search(query: str, k: int = 2 ):
    """a function that embeds a new query and returns the most probable results"""
    embedded_query = embedding_model.encode(query) # create embedding of a new query
    scores, retrieved_examples = data.get_nearest_examples( # retrieve results
        "embeddings", embedded_query, # compare our new embedded query with the dataset embeddings
        k=k # get only top k results
    )
    return scores, retrieved_examples
# returns scores (List[float]): the retrieval scores from either FAISS (IndexFlatL2 by default) and examples (dict) format
# called by talk function that passes prompt

#print(scores, retrieved_examples)
print("check2A")


def format_prompt(prompt,retrieved_documents,k):
    """using the retrieved documents we will prompt the model to generate our responses"""   
    PROMPT = f"Question:{prompt}\nContext:"
    for idx in range(k) :
        PROMPT+= f"{retrieved_documents['0'][idx]}\n"
    return PROMPT

# Called by talk function to add retrieved documents to the prompt. Keeps adding text of retrieved documents to string taht are retreived

print("check3")

def talk(prompt, history):
    k = 2 # number of retrieved documents
    scores , retrieved_documents = search(prompt, k) # get retrival scores and examples in dictionary format based on the prompt passed
    print(retrieved_documents.keys())
    formatted_prompt = format_prompt(prompt,retrieved_documents,k) # create a new prompt using the retrieved documents
    print(retrieved_documents['0'])
    print(formatted_prompt)
    formatted_prompt = formatted_prompt[:600] # to avoid memory issue
 #   print(retrieved_documents['0'][1]
 #   print(retrieved_documents['0'][2] 
    print(formatted_prompt)
    messages = [{"role":"system","content":SYS_PROMPT},{"role":"user","content":formatted_prompt}]  
    # binding the system context and new prompt for LLM
    # the chat template structure should be based on text generation model format
    print("check3B")    
    input_ids = tokenizer.apply_chat_template(
      messages,
      add_generation_prompt=True,
      return_tensors="pt"
    ).to(model.device)
    # tell the model to generate
    # add_generation_prompt argument tells the template to add tokens that indicate the start of a bot response
    print("check3C")
    outputs = model.generate(
      input_ids,
      max_new_tokens=300,
      eos_token_id=terminators,
      do_sample=True,
      temperature=0.4,
      top_p=0.95,
    )
    # calling the model to generate response based on message/ input
    # do_sample if set to True uses strategies to select the next token from the probability distribution over the entire vocabulary
    # temperature controls randomness. more renadomness with higher temperature
    # only the tokens comprising the top_p probability mass are considered for responses
    # This output is a data structure containing all the information returned by generate(), but that can also be used as tuple or dictionary.
    print("check3D")
    streamer = TextIteratorStreamer(
            tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
            )
    # stores print-ready text in a queue, to be used by a downstream application as an iterator. removes specail tokens in generated text. 
    # timeout for text queue. tokenizer for decoding tokens
    # called by generate_kwargs
    print("check3E")
    generate_kwargs = dict(
        input_ids= input_ids,
        streamer=streamer,
        max_new_tokens= 200,
        do_sample=True,
        top_p=0.95,
        temperature=0.4,
        eos_token_id=terminators,
    )
    # send additional parameters to model for generation
    print("check3F")
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    # to process multiple instances
    t.start()
    # start a thread
    print("check3G")
    outputs = []
    for text in streamer:
        outputs.append(text)
        print(outputs)
        yield "".join(outputs)
        print("check3H")

    pd.options.display.max_colwidth = 800

    outputstring = ''.join(outputs)

    global historylog
    historynew = {
       "Prompt": prompt,
       "Output": outputstring
    }
    historylog.append(historynew)
    return historylog
    print(historylog)
    
#    history.update({prompt: outputstring})
#    print(history)
    #print(memory_string2)
    #with open(logfile, 'a', encoding='utf-8') as f:
      #  f.write(memory_string2)
     #   f.write('\n')
    #f.close()
    #print(logfile)
    #logfile.push_to_hub("Namitg02/",token = HF_TOKEN)
    #memory_panda = pd.DataFrame()
    #if len(memory_panda) == 0:
    #    memory_panda = pd.DataFrame(memory_string)
    #else:
    #    memory_panda = memory_panda.append(memory_string, ignore_index=True)
    #print(memory_panda.iloc[[0]])
    
    #memory_panda.loc[len(memory_panda.index)] = ['prompt', outputstring] 
    #print(memory_panda.iloc[[1]])
    #Logfile = Dataset.from_pandas(memory_panda)
    #Logfile.push_to_hub("Namitg02/Logfile",token = HF_TOKEN)

    
TITLE = "AI Copilot for Diabetes Patients"

DESCRIPTION = "I provide answers to concerns related to Diabetes"

import gradio as gr
# Design chatbot
demo = gr.ChatInterface(
    fn=talk,
    chatbot=gr.Chatbot(
        show_label=True,
        show_share_button=True,
        show_copy_button=True,
        likeable=True,
        layout="bubble",
        bubble_full_width=False,
    ),
    theme="Soft",
    examples=[["what is Diabetes? "]],
    title=TITLE,
    description=DESCRIPTION,
    
)
# launch chatbot and calls the talk function which in turn calls other functions
print("check3I")
print(historylog)
memory_panda = pd.DataFrame(historylog)
Logfile = Dataset.from_pandas(memory_panda)
Logfile.push_to_hub("Namitg02/Logfile",token = HF_TOKEN)
demo.launch()