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| import utils | |
| import streamlit as st | |
| from streaming import StreamHandler | |
| import torch | |
| # from LLM import OpenAI | |
| from langchain.chains import ConversationChain | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.llms import HuggingFaceHub | |
| from transformers import AutoConfig | |
| from langchain.llms import HuggingFaceHub | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| from langchain.llms import HuggingFacePipeline | |
| from langchain.chat_models import ChatOllama | |
| import pandas as pd | |
| import requests | |
| import tempfile | |
| import os | |
| st.set_page_config(page_title="Chatbot", page_icon="💬") | |
| st.header('Ebiz Chatbot') | |
| st.write('Allows users to interact with the LLM') | |
| # st.write('[](https://github.com/shashankdeshpande/langchain-chatbot/blob/master/pages/1_%F0%9F%92%AC_basic_chatbot.py)') | |
| # Using "with" notation | |
| with st.sidebar: | |
| ktopVal= st.slider( | |
| 'K-top', | |
| 0.0, 1.0,step=0.1) | |
| ptopVal=st.slider( | |
| 'P-top', | |
| 0.0, 1.0,step=0.1) | |
| tempVal=st.slider( | |
| 'Temperature', | |
| 0.0, 1.0, step=0.1) | |
| uploaded_file = st.file_uploader("Choose a file") | |
| submit_button = st.button("Submit") | |
| if submit_button: | |
| # Display the file details | |
| st.write("File Details:") | |
| st.write(f"Filename: {uploaded_file.name}") | |
| st.write(f"File Type: {uploaded_file.type}") | |
| st.write(f"File Size: {uploaded_file.size} bytes") | |
| # Send file to Flask server | |
| url = "http://127.0.0.1:5000/upload" | |
| files = {'file': (uploaded_file.name, uploaded_file, uploaded_file.type)} | |
| payload = {'ktopVal':ktopVal,"ptopVal":ptopVal,"tempVal":tempVal} | |
| response = requests.post(url, files=files,data=payload) | |
| # Display the response from the server | |
| st.write("Server Response:") | |
| st.write(response.text) | |
| class Basic: | |
| def __init__(self): | |
| utils.configure_openai_api_key() | |
| self.openai_model = "gpt-3.5-turbo" | |
| def setup_chain(self): | |
| # config = AutoConfig.from_pretrained('stabilityai/stablelm-3b-4e1t', trust_remote_code=True) | |
| model_name = 'stabilityai/stablelm-3b-4e1t' | |
| torch_dtype = "float16" #@param ["float16", "bfloat16", "float"] | |
| load_in_8bit = False #@param {type:"boolean"} | |
| device_map = "auto" | |
| # tokenizer = AutoTokenizer.from_pretrained(model_name,trust_remote_code=True) | |
| # model = AutoModelForCausalLM.from_pretrained( | |
| # model_name, | |
| # torch_dtype=getattr(torch, torch_dtype), | |
| # load_in_8bit=load_in_8bit, | |
| # device_map=device_map, | |
| # trust_remote_code=True, | |
| # offload_folder="./offload", | |
| # ) | |
| # pipe = pipeline( | |
| # "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=256 | |
| # ) | |
| llm = ChatOllama(model="llama2") | |
| memory = ConversationBufferMemory() | |
| chain = ConversationChain(llm=llm, memory=memory, verbose=True) | |
| return chain | |
| def main(self): | |
| chain = self.setup_chain() | |
| user_query = st.chat_input(placeholder="Ask me anything!") | |
| if user_query: | |
| utils.display_msg(user_query, 'user') | |
| with st.chat_message("assistant"): | |
| st_cb = StreamHandler(st.empty()) | |
| response = chain(user_query, callbacks=[st_cb]) | |
| st.session_state.messages.append({"role": "assistant", "content": response['response']}) | |
| if __name__ == "__main__": | |
| obj = Basic() | |
| obj.main() |