import streamlit as st
import pandas as pd
import re
import time
import os
from io import StringIO
import pyperclip
import json
import requests
st.set_page_config(page_title="Prompt Output Separator", page_icon="✂️", layout="wide", initial_sidebar_state="expanded")
if 'api_key' not in st.session_state:
st.session_state.api_key = None
if 'history' not in st.session_state:
st.session_state.history = []
if 'prompt' not in st.session_state:
st.session_state.prompt = ""
if 'output' not in st.session_state:
st.session_state.output = ""
if 'title' not in st.session_state:
st.session_state.title = ""
if 'mode' not in st.session_state:
st.session_state.mode = 'light'
def count_text_stats(text):
words = len(text.split())
chars = len(text)
return words, chars
def analyze_with_llm(text):
if not st.session_state.api_key:
st.error("Please provide an OpenAI API key in the sidebar")
return None, None, None
try:
headers = {
"Authorization": f"Bearer {st.session_state.api_key}",
"Content-Type": "application/json"
}
data = {
"model": "gpt-4",
"messages": [
{
"role": "system",
"content": """You are a text separator. Your ONLY job is to split the input text into its original prompt and response components.
CRITICAL RULES:
- DO NOT summarize or modify ANY text
- Return the EXACT original text split into two parts
- Make NO changes to the content
- Preserve ALL formatting and whitespace
Return ONLY a JSON object with these fields:
- title: brief descriptive title (max 6 words)
- prompt: the EXACT, COMPLETE first part of the conversation
- output: the EXACT, COMPLETE response/answer part"""
},
{
"role": "user",
"content": f"Split this text into its original parts with NO modifications: {text}"
}
],
"temperature": 0
}
response = requests.post(
"https://api.openai.com/v1/chat/completions",
headers=headers,
json=data
)
if response.status_code == 200:
result = response.json()['choices'][0]['message']['content']
try:
parsed = json.loads(result)
# Verify no content was lost
original_words = len(text.split())
result_words = len((parsed.get("prompt", "") + parsed.get("output", "")).split())
if result_words < original_words * 0.9: # Allow for 10% difference due to splitting
st.error("Content was modified during processing. Using basic split instead.")
parts = text.split('\n\n', 1)
if len(parts) == 2:
return "Untitled Conversation", parts[0].strip(), parts[1].strip()
return "Untitled Conversation", text.strip(), ""
return parsed.get("title"), parsed.get("prompt"), parsed.get("output")
except json.JSONDecodeError:
st.error("Failed to parse LLM response as JSON")
return None, None, None
else:
st.error(f"API request failed with status code: {response.status_code}")
st.error(f"Response: {response.text}")
return None, None, None
except Exception as e:
st.error(f"Error analyzing text: {str(e)}")
return None, None, None
def separate_prompt_output(text):
if not text:
return "", "", ""
if st.session_state.api_key:
title, prompt, output = analyze_with_llm(text)
if all(v is not None for v in [title, prompt, output]):
return title, prompt, output
parts = text.split('\n\n', 1)
if len(parts) == 2:
return "Untitled Conversation", parts[0].strip(), parts[1].strip()
return "Untitled Conversation", text.strip(), ""
def process_column(column):
processed_data = []
for item in column:
title, prompt, output = separate_prompt_output(str(item))
processed_data.append({"Title": title, "Prompt": prompt, "Output": output})
return pd.DataFrame(processed_data)
with st.sidebar:
st.image("https://img.icons8.com/color/96/000000/chat.png", width=50)
st.markdown("## 🛠️ Configuration")
api_key = st.text_input("Enter OpenAI API Key", type="password", help="Get your API key from platform.openai.com")
if api_key:
st.session_state.api_key = api_key
st.markdown("---")
st.markdown("## 🎨 Appearance")
dark_mode = st.checkbox("Dark Mode", value=st.session_state.mode == 'dark')
st.session_state.mode = 'dark' if dark_mode else 'light'
st.title("✂️ Prompt Output Separator")
st.markdown("Utility to assist with separating prompts and outputs when they are recorded in a unified block of text.")
tabs = st.tabs(["📝 Paste Text", "📁 File Processing", "📊 History"])
with tabs[0]:
st.subheader("Paste Prompt and Output")
input_container = st.container()
with input_container:
input_text = st.text_area("Paste your conversation here...", height=200, placeholder="Paste your conversation here. The tool will automatically separate the prompt from the output.", help="Enter the text you want to separate into prompt and output.")
if st.button("🔄 Process", use_container_width=True) and input_text:
with st.spinner("Processing..."):
title, prompt, output = separate_prompt_output(input_text)
st.session_state.title = title
st.session_state.prompt = prompt
st.session_state.output = output
st.session_state.history.append(input_text)
st.markdown("### 📌 Suggested Title")
title_area = st.text_area("", value=st.session_state.get('title', ""), height=70, key="title_area", help="AI-generated title based on the conversation content")
st.markdown("### 📝 Prompt")
prompt_area = st.text_area("", value=st.session_state.get('prompt', ""), height=200, key="prompt_area", help="The extracted prompt will appear here")
prompt_words, prompt_chars = count_text_stats(st.session_state.get('prompt', ""))
st.markdown(f"Words: {prompt_words} | Characters: {prompt_chars}
", unsafe_allow_html=True)
if st.button("📋 Copy Prompt", use_container_width=True):
pyperclip.copy(st.session_state.get('prompt', ""))
st.success("Copied prompt to clipboard!")
st.markdown("### 🤖 Output")
output_area = st.text_area("", value=st.session_state.get('output', ""), height=200, key="output_area", help="The extracted output will appear here")
output_words, output_chars = count_text_stats(st.session_state.get('output', ""))
st.markdown(f"Words: {output_words} | Characters: {output_chars}
", unsafe_allow_html=True)
if st.button("📋 Copy Output", use_container_width=True):
pyperclip.copy(st.session_state.get('output', ""))
st.success("Copied output to clipboard!")
with tabs[1]:
st.subheader("Process File")
uploaded_file = st.file_uploader("Choose a file", type=['txt', 'csv'])
if uploaded_file is not None:
try:
if uploaded_file.type == "text/csv":
df = pd.read_csv(uploaded_file)
st.write("Select the column containing the conversations:")
column = st.selectbox("Column", df.columns.tolist())
if st.button("Process CSV"):
with st.spinner("Processing..."):
result_df = process_column(df[column])
st.write(result_df)
st.download_button(
"Download Processed CSV",
result_df.to_csv(index=False).encode('utf-8'),
"processed_conversations.csv",
"text/csv",
key='download-csv'
)
else:
content = StringIO(uploaded_file.getvalue().decode("utf-8")).read()
if st.button("Process Text File"):
with st.spinner("Processing..."):
title, prompt, output = separate_prompt_output(content)
st.session_state.title = title
st.session_state.prompt = prompt
st.session_state.output = output
st.session_state.history.append(content)
st.experimental_rerun()
except Exception as e:
st.error(f"Error processing file: {str(e)}")
with tabs[2]:
st.subheader("Processing History")
if st.session_state.history:
if st.button("🗑️ Clear History", type="secondary"):
st.session_state.history = []
st.experimental_rerun()
for idx, item in enumerate(reversed(st.session_state.history)):
with st.expander(f"Entry {len(st.session_state.history) - idx}", expanded=False):
st.text_area("Content", value=item, height=150, key=f"history_{idx}", disabled=True)
else:
st.info("💡 No processing history available yet. Process some text to see it here.")
st.markdown("---")
st.markdown("", unsafe_allow_html=True)
if st.session_state.mode == 'dark':
st.markdown("""
""", unsafe_allow_html=True)