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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'] |