app.py

import streamlit as st
import openai
import re
import csv
import base64
from io import StringIO
import threading
from queue import Queue

st.title("EinfachChatProjekt")

api_key = st.sidebar.text_input("API Key:", value="sk-")
openai.api_key = api_key

show_notes = st.sidebar.checkbox("Show Notes", value="TRUE")
data_section = st.sidebar.text_area("CSV or Text Data:")
paste_data = st.sidebar.button("Paste Data")
num_concurrent_calls = st.sidebar.number_input("Concurrent Calls:", min_value=1, max_value=2000, value=50, step=1)
generate_all = st.sidebar.button("Generate All")
reset = st.sidebar.button("Reset")
add_row = st.sidebar.button("Add row")
model = st.sidebar.selectbox("Model:", ["gpt-4", "gpt-3.5-turbo"])
temperature = st.sidebar.slider("Temperature:", 0.0, 1.0, 0.6, step=0.01)
max_tokens = st.sidebar.number_input("Max Tokens:", min_value=1, max_value=8192, value=2000, step=1)
top_p = st.sidebar.slider("Top P:", 0.0, 1.0, 1.0, step=0.01)
system_message = st.sidebar.text_area("System Message:")
row_count = st.session_state.get("row_count", 1)

if add_row:
    row_count += 1
    st.session_state.row_count = row_count

if paste_data:
    data = StringIO(data_section.strip())
    reader = csv.reader(data, delimiter='\n', quotechar='"')
    messages = [row[0] for row in reader]
    if show_notes:
        row_count = len(messages) // 2
        for i in range(row_count):
            st.session_state[f"note{i}"] = messages[i * 2]
            st.session_state[f"message{i}"] = messages[i * 2 + 1]
    else:
        row_count = len(messages)
        for i, message in enumerate(messages):
            st.session_state[f"message{i}"] = message
    st.session_state.row_count = row_count

if reset:
    row_count = 1
    st.session_state.row_count = row_count
    for i in range(100): # Assuming a maximum of 100 rows
        st.session_state[f"note{i}"] = ""
        st.session_state[f"message{i}"] = ""
        st.session_state[f"response{i}"] = ""
        st.session_state[f"prompt_tokens{i}"] = 0
        st.session_state[f"response_tokens{i}"] = 0
        st.session_state[f"word_count{i}"] = 0

def generate_response(i, message):
    try:
        completion = openai.ChatCompletion.create(
            model=model,
            messages=[
                {"role": "system", "content": system_message},
                {"role": "user", "content": message}
            ],
            temperature=temperature,
            max_tokens=max_tokens,
            top_p=top_p
        )

        response = completion.choices[0].message.content
        prompt_tokens = completion.usage['prompt_tokens']
        response_tokens = completion.usage['total_tokens'] - prompt_tokens
        word_count = len(re.findall(r'\w+', response))

        return (i, response, prompt_tokens, response_tokens, word_count)

    except Exception as e:
        return (i, str(e), 0, 0, 0)

def worker(q, results):
    for item in iter(q.get, None):
        results.put(generate_response(*item))

class WorkerThread(threading.Thread):
    def __init__(self, input_queue, output_queue):
        threading.Thread.__init__(self)
        self.input_queue = input_queue
        self.output_queue = output_queue
        self.daemon = True

    def run(self):
        while True:
            i, message = self.input_queue.get()
            try:
                result = generate_response(i, message)
                self.output_queue.put(result)
            finally:
                self.input_queue.task_done()

if generate_all:
    jobs = Queue()
    results = Queue()

    workers = [WorkerThread(jobs, results) for _ in range(num_concurrent_calls)]

    for worker in workers:
        worker.start()

    for i in range(row_count):
        message = st.session_state.get(f"message{i}", "")
        jobs.put((i, message))

    jobs.join()

    while not results.empty():
        i, response, prompt_tokens, response_tokens, word_count = results.get()
        st.session_state[f"response{i}"] = response
        st.session_state[f"prompt_tokens{i}"] = prompt_tokens
        st.session_state[f"response_tokens{i}"] = response_tokens
        st.session_state[f"word_count{i}"] = word_count

def create_download_link(text, filename):
    b64 = base64.b64encode(text.encode()).decode()
    href = f'<a href="data:file/txt;base64,{b64}" download="{filename}">Download {filename}</a>'
    return href

for i in range(row_count):
    if show_notes:
        st.text_input(f"Note {i + 1}:", key=f"note{i}", value=st.session_state.get(f"note{i}", ""))
    col1, col2 = st.columns(2)

    with col1:
        message = st.text_area(f"Message {i + 1}:", key=f"message{i}", value=st.session_state.get(f"message{i}", ""))

        if st.button(f"Generate Response {i + 1}") and not st.session_state.get(f"response{i}", ""):
            response, prompt_tokens, response_tokens, word_count = generate_response(i, message)
            st.session_state[f"response{i}"] = response
            st.session_state[f"prompt_tokens{i}"] = prompt_tokens
            st.session_state[f"response_tokens{i}"] = response_tokens
            st.session_state[f"word_count{i}"] = word_count

    with col2:
        st.text_area(f"Response {i + 1}:", value=st.session_state.get(f"response{i}", ""))
        st.write(f"Tokens: {st.session_state.get(f'prompt_tokens{i}', 0)} / {st.session_state.get(f'response_tokens{i}', 0)} + Words: {st.session_state.get(f'word_count{i}', 0)}")

responses_text = "\n\n".join([f"{st.session_state.get(f'note{i}', '')}\n{st.session_state.get(f'response{i}', '')}" for i in range(row_count) if show_notes] + [st.session_state.get(f"response{i}", "") for i in range(row_count) if not show_notes])
download_filename = "GPT-4 Responses.txt"
download_link = create_download_link(responses_text, download_filename)
st.markdown(download_link, unsafe_allow_html=True)