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| def wait(): | |
| import streamlit as st | |
| import time | |
| progress_texts = ["Generating Code...:pencil:","Creating App...:running:","Rendering the demo page...:tv:"] | |
| num_of_texts = len(progress_texts) | |
| progress_texts_iter = iter(progress_texts) | |
| my_bar = st.progress(0, "Initializing...") | |
| with st.spinner('Processing...'): | |
| start = end = 0 | |
| for i in range(num_of_texts): | |
| text = next(progress_texts_iter) | |
| start = end | |
| end = start + 100 // num_of_texts | |
| for percent_complete in range(start, end): | |
| time.sleep(0.03*(num_of_texts-i)) | |
| my_bar.progress(percent_complete + 1, text=text) | |
| my_bar.empty() | |
| def language_translator(openai_api_key,demo_title="My Lang App"): | |
| import streamlit as st | |
| from langchain import LLMChain | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.prompts.chat import ( | |
| ChatPromptTemplate, | |
| SystemMessagePromptTemplate, | |
| HumanMessagePromptTemplate, | |
| ) | |
| def language_translator(input_language, output_language, text): | |
| chat = ChatOpenAI(openai_api_key=openai_api_key, temperature=0) | |
| template = "You are a helpful assistant that translates {input_language} to {output_language}. Please provide the text to translate." | |
| system_message_prompt = SystemMessagePromptTemplate.from_template(template) | |
| human_template = "{text}" | |
| human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) | |
| chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) | |
| chain = LLMChain(llm=chat, prompt=chat_prompt) | |
| result = chain.run(input_language=input_language, output_language=output_language, text=text) | |
| return result | |
| st.header(demo_title) | |
| input_language = st.text_input("Input Language") | |
| output_language = st.text_input("Output Language") | |
| text = st.text_area("Text") | |
| if st.button("Translate"): | |
| result = language_translator(input_language, output_language, text) | |
| st.write(result) | |
| st.balloons() | |
| def blog_post_generator(openai_api_key,demo_title="My Blogger"): | |
| import streamlit as st | |
| from langchain import LLMChain | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.prompts.chat import ( | |
| ChatPromptTemplate, | |
| SystemMessagePromptTemplate, | |
| HumanMessagePromptTemplate, | |
| ) | |
| def generate_blog_post(title): | |
| print("Generating blog post") | |
| chat = ChatOpenAI(openai_api_key=openai_api_key, temperature=0) | |
| template = "You are a helpful assistant that generates a blog post from the title: {title}. Please provide some content." | |
| system_message_prompt = SystemMessagePromptTemplate.from_template(template) | |
| human_template = "{text}" | |
| human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) | |
| chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) | |
| chain = LLMChain(llm=chat, prompt=chat_prompt) | |
| result = chain.run(title=title, text="") | |
| return result | |
| st.header(demo_title) | |
| title = st.text_input("Enter the title of your blog post") | |
| if st.button("Generate Blog Post"): | |
| print("Generate") | |
| with st.spinner("Generating the blog post..."): | |
| result = generate_blog_post(title) | |
| st.write(result) | |
| st.balloons() | |
| def grammer_corrector(openai_api_key,demo_title="My Grammerly"): | |
| import streamlit as st | |
| from langchain import LLMChain | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.prompts.chat import ( | |
| ChatPromptTemplate, | |
| SystemMessagePromptTemplate, | |
| HumanMessagePromptTemplate, | |
| ) | |
| def correct_grammar(text): | |
| chat = ChatOpenAI(openai_api_key=openai_api_key, temperature=0) | |
| template = "You are a helpful assistant that corrects grammar. Please provide the text you want to correct." | |
| system_message_prompt = SystemMessagePromptTemplate.from_template(template) | |
| human_template = "{text}" | |
| human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) | |
| chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) | |
| chain = LLMChain(llm=chat, prompt=chat_prompt) | |
| result = chain.run(text=text) | |
| return result | |
| st.header(demo_title) | |
| text = st.text_input("Enter the text you want to correct") | |
| if st.button("Correct Grammar"): | |
| result = correct_grammar(text) | |
| st.write(result) | |
| st.balloons() | |
| def lyrics_generator(openai_api_key,demo_title="Lyrics Maker"): | |
| import streamlit as st | |
| from langchain import LLMChain | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.prompts.chat import ( | |
| ChatPromptTemplate, | |
| SystemMessagePromptTemplate, | |
| HumanMessagePromptTemplate, | |
| ) | |
| def generate_song(title): | |
| chat = ChatOpenAI(openai_api_key=openai_api_key, temperature=0) | |
| template = "You are a helpful assistant that generates a song from the title: {title}. Please provide some lyrics." | |
| system_message_prompt = SystemMessagePromptTemplate.from_template(template) | |
| human_template = "{text}" | |
| human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) | |
| chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) | |
| chain = LLMChain(llm=chat, prompt=chat_prompt) | |
| result = chain.run(title=title, text="") | |
| return result | |
| st.header(demo_title) | |
| title = st.text_input("Enter the song title:") | |
| if st.button("Generate Song"): | |
| with st.spinner("Generating song..."): | |
| result = generate_song(title) | |
| st.write(result) | |
| st.balloons() | |
| def twit_generator(openai_api_key,demo_title="My AutoTwitter"): | |
| import streamlit as st | |
| from langchain import LLMChain | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.prompts.chat import ( | |
| ChatPromptTemplate, | |
| SystemMessagePromptTemplate, | |
| HumanMessagePromptTemplate, | |
| ) | |
| def twitter(hashtag): | |
| chat = ChatOpenAI(openai_api_key=openai_api_key, temperature=0.1) | |
| template = "You are a helpful assistant that generate twit from {hashtag}. Please provide the hashtag to generate a twit." | |
| system_message_prompt = SystemMessagePromptTemplate.from_template(template) | |
| human_template = "Only generate the corresponding twit for this hashtag {hashtag}" | |
| human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) | |
| chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) | |
| chain = LLMChain(llm=chat, prompt=chat_prompt) | |
| result = chain.run(hashtag=hashtag) | |
| return result | |
| st.header(demo_title) | |
| hashtag = st.text_input("Hashtag",placeholder="#") | |
| if st.button("Generate"): | |
| result = twitter(hashtag) | |
| st.write(result) | |
| st.balloons() | |
| def email_generator(openai_api_key,demo_title="My AutoTwitter"): | |
| import streamlit as st | |
| from langchain import LLMChain | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.prompts.chat import ( | |
| ChatPromptTemplate, | |
| SystemMessagePromptTemplate, | |
| HumanMessagePromptTemplate, | |
| ) | |
| def email(sender_name,receiver_name,purpose,keywords,tone): | |
| chat = ChatOpenAI(openai_api_key=openai_api_key, temperature=0.1) | |
| template = "You are a helpful assistant that generate email to a person according to the given purpose, keywords and tone." | |
| system_message_prompt = SystemMessagePromptTemplate.from_template(template) | |
| human_template = """Generate email for a person according to the given purpose, keywords and tone. | |
| Sender Name:{sender_name} | |
| Receiver Name:{receiver_name} | |
| Purpose:{purpose} | |
| Keywords:{keywords} | |
| Tone:{tone} | |
| Directly start to type an email | |
| """ | |
| human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) | |
| chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) | |
| chain = LLMChain(llm=chat, prompt=chat_prompt) | |
| result = chain.run(sender_name=sender_name, receiver_name=receiver_name, purpose=purpose, keywords=keywords, tone=tone) | |
| return result | |
| st.header(demo_title) | |
| sender_name = st.text_input("Name of the sender") | |
| receiver_name = st.text_input("Receiver of the sender") | |
| purpose = st.text_input("Purpose of email") | |
| keywords = st.text_input("Primary keywords",placeholder="comma separated list of keywords") | |
| tone = st.text_input("Tone of the email") | |
| if st.button("Generate"): | |
| with st.spinner("Generating email..."): | |
| result = email(sender_name,receiver_name,purpose,keywords,tone) | |
| st.write(result) | |
| st.balloons() | |
| examples1 = [ | |
| "Language Translator 📝", | |
| "Grammer Corrector 🛠", | |
| "Blog post generator from title 📔" | |
| ] | |
| examples2=[ | |
| "Lyrics generator from song title 🎤", | |
| "Twit generation from hashtag 🐦", | |
| 'Email generator :email:' | |
| ] | |
| examples = examples1 + examples2 | |
| pages1 = [language_translator,grammer_corrector,blog_post_generator] | |
| pages2=[lyrics_generator,twit_generator,email_generator] | |
| pages = pages1 + pages2 | |
| example2pages={ | |
| example:page | |
| for example,page in zip(examples,pages) | |
| } | |
| __all__ = ['language_translator','grammer_corrector','blog_post_generator','lyrics_generator','twit_generator', | |
| 'example2pages', 'examples', 'examples1', 'examples2', 'wait'] |