AI 故事讲述的力量：利用 OpenAI 构建一个 Streamlit 应用程序

重新开始学习Python，并对ChaatGPT Prompt Engineering着迷，下面是我与ChatGPT、OpenAI API和Streamlit的下一步计划。

一个简单的网络应用程序，利用OpenAI的文本生成能力，根据用户提供的提示生成引人入胜的故事。

• 工具：Python，Streamlit构建用户界面和OpenAI API

import streamlit as st
import json
from openai import OpenAI

# Authenticate with OpenAI API (replace with your key)
open_ai_client = OpenAI()

# Function to call OpenAI API for structured generation
def structured_generator(openai_model, prompt):
    """
    Calls the OpenAI API to generate text in a structured format.

    Args:
        openai_model (str): The name of the OpenAI model to use.
        prompt (str): The prompt to provide for text generation.

    Returns:
        dict: The JSON response from the OpenAI API, containing generated text.
    """

    result = open_ai_client.chat.completions.create(
        model=openai_model,
        messages=[{"role": "user", "content": f"{prompt}, output must be in json the object returned must a json with title and description"}]
    )
    return result

# ---- UI Layout and Components ----

st.title("StoryBoard: Unleash Your Creativity")
st.write("Craft captivating stories and poems with the power of AI.")

# Input section for topic and content type
col1, col2 = st.columns(2)
with col1:
    selected_input_topic = st.selectbox("Choose a Theme:", ["Enchanted Forests", "Magical Creatures", "Faraway Adventures"])
with col2:
    selected_input_type = st.selectbox("Choose a Format:", ["Story", "Poem"])

# Button to trigger generation with visual feedback
if st.button("Let the Magic Begin"):
    with st.spinner("Weaving Words of Wonder..."):
        prompt = f"Generate a 3 to 5 line {selected_input_type} about {selected_input_topic}"
        openai_model = "gpt-3.5-turbo"  # Replace with your preferred model
        result = structured_generator(openai_model, prompt)

        # Process and display generated content
        data = json.loads(result.choices[0].message.content)
        # Display all key-value pairs dynamically
        for key, value in data.items():
            if isinstance(value, list):
                st.subheader(key.upper())
                for item in value:
                    if isinstance(item, dict) and "image" in item:  # Check for nested images
                        st.image(item["image"])
                    else:
                        st.write(item)
            elif isinstance(value, dict):
                st.subheader(key.upper())
                for sub_key, sub_value in value.items():
                    if isinstance(sub_value, list):  # Handle lists within nested dictionaries
                        for item in sub_value:
                            st.write(item)
                    else:
                        st.write(f"**{sub_key.upper()}:** {sub_value}")
            else:
                st.write(f"**{key.upper()}:** {value}")