app.py

app.py

@@ -0,0 +1,151 @@
+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)