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| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| import time | |
| import json | |
| from datetime import datetime | |
| class ChatApp: | |
| def __init__(self): | |
| st.set_page_config(page_title="Inspection Methods Engineer Assistant", page_icon="🔍", layout="wide") | |
| self.initialize_session_state() | |
| self.model_handler = self.load_model() | |
| def initialize_session_state(self): | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [ | |
| {"role": "system", "content": "You are an experienced inspection methods engineer. Your task is to classify the following scope: analyze the scope provided in the input and determine the class item as an output."} | |
| ] | |
| def load_model(): | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| st.info(f"Using device: {device}") | |
| model_name = "amiguel/classItem-FT-llama-3-1-8b-instruct" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| device_map="auto", | |
| load_in_8bit=device == "cuda" | |
| ) | |
| return ModelHandler(model, tokenizer) | |
| def display_message(self, role, content): | |
| with st.chat_message(role): | |
| st.markdown(content) | |
| def get_user_input(self): | |
| return st.chat_input("Type your message here...") | |
| def stream_response(self, response): | |
| placeholder = st.empty() | |
| full_response = "" | |
| for word in response.split(): | |
| full_response += word + " " | |
| placeholder.markdown(full_response + "▌") | |
| time.sleep(0.01) | |
| placeholder.markdown(full_response) | |
| return full_response | |
| def save_chat_history(self): | |
| timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
| filename = f"chat_history_{timestamp}.json" | |
| with open(filename, "w") as f: | |
| json.dump(st.session_state.messages, f, indent=2) | |
| return filename | |
| def run(self): | |
| st.title("Inspection Methods Engineer Assistant") | |
| for message in st.session_state.messages: | |
| if message["role"] != "system": | |
| self.display_message(message["role"], message["content"]) | |
| user_input = self.get_user_input() | |
| if user_input: | |
| self.display_message("user", user_input) | |
| st.session_state.messages.append({"role": "user", "content": user_input}) | |
| conversation = "\n\n".join([msg["content"] for msg in st.session_state.messages]) | |
| with st.spinner("Analyzing and classifying scope..."): | |
| response = self.model_handler.generate_response(conversation.strip()) | |
| clean_response = self.clean_response(response) | |
| with st.chat_message("assistant"): | |
| full_response = self.stream_response(clean_response) | |
| st.session_state.messages.append({"role": "assistant", "content": full_response}) | |
| st.sidebar.title("Chat Options") | |
| if st.sidebar.button("Save Chat History"): | |
| filename = self.save_chat_history() | |
| st.sidebar.success(f"Chat history saved to {filename}") | |
| def clean_response(self, response): | |
| # Remove any system: or user: prefixes from the response | |
| lines = response.split('\n') | |
| clean_lines = [line.split(':', 1)[-1].strip() if ':' in line else line for line in lines] | |
| return '\n'.join(clean_lines) | |
| class ModelHandler: | |
| def __init__(self, model, tokenizer): | |
| self.model = model | |
| self.tokenizer = tokenizer | |
| def generate_response(self, conversation): | |
| inputs = self.tokenizer(conversation, return_tensors="pt").to(self.model.device) | |
| outputs = self.model.generate(**inputs, max_new_tokens=100) | |
| return self.tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| if __name__ == "__main__": | |
| app = ChatApp() | |
| app.run() | |