pages/Chat_Mode.py

import openai
import streamlit as st
import json
import mysql.connector
import random
import time

st.title("LaptopGPT v1.0")

openai.api_base = "https://master-naturally-bluegill.ngrok-free.app/v1"
openai.api_key = "not needed for a local LLM"

if "openai_model" not in st.session_state:
    st.session_state["openai_model"] = "GPT4All Falcon"
# Initialize message history list
if "messages" not in st.session_state:
    st.session_state.messages = [{"role": "assistant", "content": "I'm ready for a prompt!"}]
# display message history in chat window
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])
        
token_number = st.sidebar.slider("Maximum Tokens", min_value=10, max_value=400, value=50, step=1)
temp_number = st.sidebar.slider("Temperature", min_value=0.00, max_value=1.00, value=0.28, step=0.01)
model_selector = st.sidebar.selectbox("Available Models", ("GPT4All Falcon", "Llama-2-7B Chat", "Mini Orca (Small)"))

display_stats = st.sidebar.checkbox("Display stats")

iter = random.randint(0, 999999999)
siter = str(iter)

# User input
if prompt := st.chat_input("Type some text here!"):
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)
    
    with st.chat_message("assistant"):
        message_placeholder = st.empty()
        full_response = ""
    
        response = openai.Completion.create(
            model=model_selector,
            prompt=prompt,
            max_tokens=token_number,
            temperature=temp_number,
            top_p=0.95,
            n=1,
            echo=False,
            stream=False
        )

        
        response_text = response["choices"][0]["text"]
        print(response_text)
        print(response["usage"]["prompt_tokens"])
        print(response["usage"]["completion_tokens"])
        print(response["usage"]["total_tokens"])

        ##actual text from the json
        #response_text = getSentToDatabase()
        ##entire json
        #response = getSentToDatabase()

        print(prompt)
        print(response)

        
        prompt_tokens = response["usage"]["prompt_tokens"]
        completion_tokens = response["usage"]["completion_tokens"]
        total_tokens = response["usage"]["total_tokens"]

        cost = (prompt_tokens * 0.03 + completion_tokens * 0.06) / 1000

            
        st.markdown(response_text)
        st.session_state.messages.append({"role": "assistant", "content": response_text})

        if (display_stats == True):
            st.markdown(''':red[Tokens in prompt: ]''' + " " + str(prompt_tokens))
            st.markdown(''':orange[Tokens in response: ]''' + " " + str(completion_tokens))
            st.markdown(''':green[Total Tokens: ]''' + " " + str(total_tokens))
            st.markdown(''':blue[GPT4 Cost: ]''' + " $" + str(round(cost, 5)))
            st.markdown(''':rainbow[Our Cost: ]''' + " $0.00")

    

        # Extracting fields from the response
        id = response["id"]
        model = response["model"]
        created = response["created"]
        finish_reason = response["choices"][0]["finish_reason"]
        text_content = response["choices"][0]["text"]
        
        # Splitting the 'text' field into different rows based on '\n'
        #text_line = text_content.split('\n')
        
        conn = mysql.connector.connect(
           host='34.70.75.35',
          database='chatbotdb',
          user='remote3',
         password='password'
        
        )
        
        cursor = conn.cursor()

        timestr = time.strftime("%Y%m%d-%H%M%S")
        
        # Extracting data from JSON
        id_value = "web_" + timestr
        finish_reason = response["choices"][0]["finish_reason"]
        index_value = response["choices"][0]["index"]
        #logprobs = "web"
        logprobs = json.dumps(response["choices"][0]["logprobs"]) if response["choices"][0]["logprobs"] is not None else None
        #text_lines = response["choices"][0]["text"].split('\n')
        text_line1 = prompt
        text_line2 = response["choices"][0]["text"]
        
        created = response["created"]
        model = response["model"]
        object = response["object"]
        completion_tokens = response["usage"]["completion_tokens"]
        prompt_tokens = response["usage"]["prompt_tokens"]
        total_tokens = response["usage"]["total_tokens"]

        

        # Insert data into the table
        insert_data_query = """
        INSERT INTO chatbot3 (id, finish_reason, index_value, logprobs, text_line1, text_line2, created, model, object, completion_tokens, prompt_tokens, total_tokens)
        VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);
        """
        cursor.execute(insert_data_query, (id_value, finish_reason, index_value, logprobs, text_line1, text_line2, created, model, object, completion_tokens, prompt_tokens, total_tokens))
        
        # Commit the changes and close the connection
        conn.commit()
                
        
        conn.close()

def clear_chat_history():
    st.session_state.messages = [{"role": "assistant", "content": "I'm ready for a prompt!"}]
    #model.current_chat_session = empty_chat_session(model.config["systemPrompt"])
st.sidebar.button('Clear Chat History', on_click=clear_chat_history)

#token_number = st.sidebar.slider("Maximum Tokens", min_value=10, max_value=500, value=75, step=1)
#st.sidebar.write(token_number)