Delete app.py

app.py

@@ -1,96 +0,0 @@
-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."}
-            ]
-
-    @staticmethod
-    @st.cache_resource
-    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.write(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[1:]:  # Skip the system message
-            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([f"{msg['role']}: {msg['content']}" for msg in st.session_state.messages])
-
-            with st.spinner("Analyzing and classifying scope..."):
-                response = self.model_handler.generate_response(conversation.strip())
-
-            with st.chat_message("assistant"):
-                full_response = self.stream_response(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}")
-
-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()