update

app.py

@@ -1,19 +1,36 @@
+# This is a simple script generator that uses OpenAI to generate scripts based on a document summary and content outline.
+# It's a Streamlit app that allows the user to input the document summary, content outline, and system prompts for context and script generation.
+# The app generates a context that captures the essential information from the summary, and then generates a detailed script for each heading in the content outline.
+# The scripts are then displayed in the app.
+
 import os
 import asyncio
 import streamlit as st
 import aiohttp
 
+# This is the OpenAI API key and URL.
 API_KEY = os.environ["OPENAI_API_KEY"]
 API_URL = "https://api.openai.com/v1/chat/completions"
 
+
+# 1st Step: Generate Context
+# まず初めに、コンテンツの前提条件を作るためにcontextを生成します。
 async def generate_context(summaries, system_prompt_prefix, system_prompt_suffix):
     prompt = f"{system_prompt_prefix}\n\nYou are an AI assistant. Based on the document summary below, please provide a concise context that captures the essential information:\n\n{summaries}\n\n{system_prompt_suffix}"
 
     async with aiohttp.ClientSession() as session:
         payload = {
             "model": "gpt-4-turbo-preview",
-            "messages": [{"role": "user", "content": prompt}],
-            "temperature": 0,
+            "messages": [
+                        {"role": "system", "content": """
+System prompt:システムプロンプトは、コンテンツの前提条件を作るために使用されます。
+You are an AI assistant. Based on the document summary below, please provide a concise context that captures the essential information:
+ご自由にお代えください。
+                         """ 
+                         },
+                        {"role": "user", "content": prompt}
+                        ],
+            "temperature": 0.7,
             "max_tokens": 2048,
         }
         headers = {"Authorization": f"Bearer {API_KEY}"}
@@ -21,15 +38,24 @@ async def generate_context(summaries, system_prompt_prefix, system_prompt_suffix
             result = await response.json()
             return result["choices"][0]["message"]["content"].strip()
 
+# 2nd Step: Generate Scripts
 async def generate_script(context, heading, system_prompt_prefix, system_prompt_suffix):
     prompt = f"{system_prompt_prefix}\n\n{context}\n\nYou are an AI scriptwriter. Based on the context above and the heading provided below, please generate a detailed script of more than 600 characters.\n\nHeading: {heading}\n\n{system_prompt_suffix}"
 
     async with aiohttp.ClientSession() as session:
         payload = {
             "model": "gpt-4-turbo-preview",
-            "messages": [{"role": "user", "content": prompt}],
-            "temperature": 0,
-            "max_tokens": 2048,
+            "messages": [
+                        {"role": "system", "content": """
+
+
+
+System prompt:システムプロンプトは、コンテンツの前提条件を作るために使用されます。"""
+                        },
+                        {"role": "user", "content": prompt}
+                        ],
+            "temperature": 0.7,
+            "max_tokens": 4000,
         }
         headers = {"Authorization": f"Bearer {API_KEY}"}
         async with session.post(API_URL, json=payload, headers=headers) as response:
@@ -39,9 +65,9 @@ async def generate_script(context, heading, system_prompt_prefix, system_prompt_
 async def main():
     st.title("Script Generator")
 
+    context_system_prompt_prefix = st.text_input("Enter the prompt prefix for context generation:", value="")
     summaries = st.text_area("Enter the document summary:", height=200)
-    context_system_prompt_prefix = st.text_input("Enter the system prompt prefix for context generation:", value="")
-    context_system_prompt_suffix = st.text_input("Enter the system prompt suffix for context generation:", value="")
+    context_system_prompt_suffix = st.text_input("Enter the prompt suffix for context generation:", value="")
 
     outline = st.text_area("Enter the content outline (one heading per line):", height=200)
     script_system_prompt_prefix = st.text_input("Enter the system prompt prefix for script generation:", value="")