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from langchain.llms import OpenAI | |
from langchain.prompts import PromptTemplate | |
from langchain.chains import LLMChain | |
from langchain.tools import DuckDuckGoSearchRun | |
import langchain | |
langchain.verbose = False | |
# Function to generate video script | |
def generate_script(prompt,video_length,creativity,tasktype,api_key): | |
# Template for generating 'Title' | |
title_template = PromptTemplate( | |
input_variables = ['subject','tasktype'], | |
template='Please come up with a title for a {tasktype} on the subject: {subject}.' | |
) | |
# Template for generating 'Video Script' using search engine | |
script_template = PromptTemplate( | |
input_variables = ['title', 'DuckDuckGo_Search','duration', 'tasktype'], | |
template='Create a script for a {tasktype} based on this title for me. TITLE: {title} of duration: {duration} minutes using this search data {DuckDuckGo_Search} ' | |
) | |
#Setting up OpenAI LLM | |
llm = OpenAI(temperature=creativity,openai_api_key=api_key, | |
model_name='gpt-3.5-turbo') | |
#Creating chain for 'Title' & 'Video Script' | |
title_chain = LLMChain(llm=llm, prompt=title_template, verbose=True) | |
script_chain = LLMChain(llm=llm, prompt=script_template, verbose=True) | |
# https://python.langchain.com/docs/modules/agents/tools/integrations/ddg | |
search = DuckDuckGoSearchRun() | |
# Executing the chains we created for 'Title' | |
title = title_chain.run(subject=prompt,tasktype=tasktype) | |
# Executing the chains we created for 'Video Script' by taking help of search engine 'DuckDuckGo' | |
search_result = search.run(prompt) | |
script = script_chain.run(title=title, DuckDuckGo_Search=search_result,duration=video_length,tasktype=tasktype) | |
# Returning the output | |
return search_result,title,script |