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import os |
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import streamlit as st |
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from langchain.llms import HuggingFaceHub |
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from langchain.chains import LLMChain |
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from llm import similarity |
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from file_manipulation import make_directory_if_not_exists |
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from models import return_models, return_text2text_generation_models, return_task_name, return_text_generation_models |
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class LLM_Langchain(): |
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def __init__(self): |
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dummy_parent = "google" |
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self.models_count = return_text2text_generation_models(dummy_parent, True) + return_text_generation_models(dummy_parent, True) |
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st.warning("Warning: Some models may not work and some models may require GPU to run") |
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st.text(f"As of now there are {self.models_count} model available") |
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st.text("Made with Langchain, StreamLit, Hugging Face and 💖") |
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st.header('🦜🔗 One stop for Open Source Models') |
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self.task_name = st.sidebar.selectbox( |
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label = "Choose the task you want to perform", |
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options = return_task_name(), |
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help="Choose your open source LLM to get started" |
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) |
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if self.task_name is None: |
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model_parent_visibility = True |
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else: |
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model_parent_visibility = False |
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model_parent_options = return_models(self.task_name) |
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model_parent = st.sidebar.selectbox( |
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label = "Choose your Source", |
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options = model_parent_options, |
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help="Choose your source of models", |
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disabled=model_parent_visibility |
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) |
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if model_parent is None: |
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model_name_visibility = True |
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else: |
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model_name_visibility = False |
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if self.task_name == "text2text-generation": |
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options = return_text2text_generation_models(model_parent) |
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else: |
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options = return_text_generation_models(model_parent) |
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self.model_name = st.sidebar.selectbox( |
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label = "Choose your Models", |
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options = options, |
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help="Choose your open source LLM to get started", |
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disabled=model_name_visibility |
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) |
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self.temperature = st.sidebar.slider( |
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label="Temperature", |
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min_value=0.1, |
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max_value=1.0, |
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step=0.1, |
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value=0.9, |
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help="Set the temperature to get accurate results" |
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) |
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self.max_token_length = st.sidebar.slider( |
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label="Token Length", |
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min_value=32, |
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max_value=1024, |
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step=32, |
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value=1024, |
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help="Set the max tokens to get accurate results" |
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) |
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self.model_kwargs = { |
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"temperature": self.temperature, |
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"max_new_tokens": self.max_token_length |
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} |
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os.environ['HUGGINGFACEHUB_API_TOKEN'] = os.getenv("HF_KEY") |
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def generate_response(self, input_text, context): |
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template = f"<|system|>\nYou are a intelligent chatbot and expertise in {context}.</s>\n<|user|>\n{input_text}.\n<|assistant|>" |
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llm = HuggingFaceHub( |
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repo_id = self.model_name, |
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model_kwargs = self.model_kwargs |
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) |
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result = llm(template) |
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return result |
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def radio_button(self): |
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options = ['FineTune', 'Inference'] |
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selected_option = st.radio( |
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label="Choose your options", |
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options=options |
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) |
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return selected_option |
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def pdf_uploader(self): |
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if self.selected_option == "Inference": |
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self.uploader_visibility = True |
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else: |
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self.uploader_visibility = False |
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self.file_upload_status = st.file_uploader( |
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label="Upload PDF file", |
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disabled=self.uploader_visibility |
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) |
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make_directory_if_not_exists('assets/') |
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if self.file_upload_status is not None: |
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self.pdf_file_path = f"assets/{self.file_upload_status.name}" |
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with open(self.pdf_file_path, "wb") as f: |
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f.write(self.file_upload_status.getbuffer()) |
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st.write("File Uploaded Successfully") |
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def form_data(self): |
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try: |
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self.selected_option = self.radio_button() |
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self.pdf_uploader() |
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if self.selected_option == "FineTune": |
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if self.file_upload_status is None: |
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text_input_visibility = True |
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else: |
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text_input_visibility = False |
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else: |
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text_input_visibility = False |
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if "messages" not in st.session_state: |
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st.session_state.messages = [] |
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st.write(f"You are using {self.model_name} model") |
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for message in st.session_state.messages: |
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with st.chat_message(message.get('role')): |
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st.write(message.get("content")) |
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context = st.sidebar.text_input( |
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label="Context", |
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help="Context lets you know on what the answer should be generated" |
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) |
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text = st.chat_input(disabled=text_input_visibility) |
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if text: |
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st.session_state.messages.append( |
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{ |
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"role":"user", |
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"content": text |
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} |
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) |
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with st.chat_message("user"): |
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st.write(text) |
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if text.lower() == "clear": |
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del st.session_state.messages |
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return |
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if self.selected_option == 'FineTune': |
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result = similarity(self.pdf_file_path, self.model_name, self.model_kwargs, text) |
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else: |
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result = self.generate_response(text, context) |
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st.session_state.messages.append( |
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{ |
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"role": "assistant", |
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"content": result |
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} |
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) |
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with st.chat_message('assistant'): |
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st.markdown(result) |
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except Exception as e: |
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st.error(e, icon="🚨") |
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model = LLM_Langchain() |
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model.form_data() |
