| import streamlit as st | |
| import random | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| tokenizer = AutoTokenizer.from_pretrained("Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum", from_tf=True) | |
| def generate(prompt, max_new_tokens): | |
| batch = tokenizer(prompt, return_tensors="pt") | |
| generated_ids = model.generate(batch["input_ids"], max_new_tokens=max_new_tokens) | |
| output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) | |
| return output[0] | |
| st.title("ChatGPT-BingChat Prompt Generator") | |
| st.write("This app generates ChatGPT/BingChat & GPT-3 prompts using [this](https://huggingface.co/Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum) model trained by [Kaludi](https://huggingface.co/Kaludi/). Enter a role and a prompt will be generated based on it.") | |
| prompt = st.text_input("Enter a Role, Example: Virtual Assistant", placeholder="Text here", value="") | |
| max_new_tokens = st.slider("Select Max Tokens in Response", min_value=100, max_value=500, value=150, step=10) | |
| if st.button("Generate"): | |
| output = generate(prompt, max_new_tokens) | |
| st.write("Generated Prompt:", box=True) | |
| st.write("<div style='background-color: #2E2E2E; padding: 10px;'>{}</div>".format(output), unsafe_allow_html=True) | |
| st.write("") | |
| st.write("<div style='text-align: center; font-weight: bold;'>Examples:</div>",unsafe_allow_html=True, box=True) | |
| st.write("<style> .stBox span { background-color: #2E2E2E; } </style>", unsafe_allow_html=True) | |
| with open("examples.txt", "r") as f: | |
| examples = f.readlines() | |
| random_examples = random.sample(examples, 5) | |
| for example in random_examples: | |
| example = example.strip() | |
| st.write("<div style='background-color: #2E2E2E; padding: 10px; text-align: center;'>• {}</div>".format(example), unsafe_allow_html=True) | |