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idea_generation.py

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+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+import re
+
+def ideas_generator(topic, keywords, llm, tone_of_voice='Professional', num_of_ideas=3):
+    
+    '''
+    Description:
+    
+        The ideas_generator() function designed to generate catchy short or long form article titles for a given topic, 
+        utilizing a set of specified keywords. 
+        This function employs a language model to create these titles, 
+        The function can produce either a single title or multiple titles based on the value of the num argument and supports both concise and informative title generation.
+    '''
+    
+    '''
+    Parameters:
+    
+        topic (str, required): The topic or subject matter of the article for which you want to generate titles.
+        keywords (str, required): A list of keywords that should be used to help generate catchy titles relevant to the topic. These keywords can provide context and improve the quality of the titles.
+        llm (langchain.llms object, required): An instance of a pre-trained language model (e.g., GPT-3 or GPT-4) that will be used to generate the titles. This model should be provided by the user.
+        num_of_ideas (int, optional): The number of long-form titles to generate. If num is set to 1, the function will produce a single title. If num is greater than 1, it will generate multiple titles. Default is 3.
+        tone_of_voice (str, optional): A String to determine the tone of voice of the title. Default Value Professional
+    '''
+    
+    '''
+    Returns:
+    
+        ideas (str): Functions returns a text with number of ideas numbered with roman numerals   
+    '''
+    
+    if num_of_ideas == 1:
+        
+            ideas_prompt = f"Generate only 1 {tone_of_voice} and catchy Innovation title for my article about {topic} topic.\n\nuse this keywords to help you generate {tone_of_voice} catchy title: {keywords}."
+
+    else:
+            ideas_prompt = f"Generate only {num_of_ideas} {tone_of_voice} and catchy Innovation titles for my article about {topic} topic.\n\nuse this keywords to help you generate {tone_of_voice} catchy titles: {keywords}."
+
+    ideas_promptTemp = PromptTemplate(
+    input_variables=["text_input"],
+    template="You are a professional content creator and Title Generator:\n\n{text_input}\n\n:Titles (number them with roman numerals):")
+
+    ideas_extraction_chain = LLMChain(llm=llm, prompt=ideas_promptTemp)
+    ideas = ideas_extraction_chain.run(ideas_prompt)
+
+    return ideas
+
+
+def filter_ideas(ideas):
+    
+    '''
+    Description:
+    
+        The filter_ideas() function extracts and filters article titles numbered with roman numerals from a given block of text. 
+        This function uses a regular expression to identify and extract these titles and returns them as a list of strings.
+    '''
+    '''
+    Parameters:
+    
+    ideas (str): A block of text that contain article titles formatted with Roman numerals and their corresponding content.
+    '''
+    
+    '''
+    Returns
+       filtered_ideas (list of str): A list of long-form article titles extracted from the input text.
+   '''
+    pattern =  r'\b[IVXLCDM]+\.\s*(.*?)(?:\n|$)'
+    filtered_ideas = re.findall(pattern, ideas)
+    
+    return filtered_ideas
+
+
+def pick_idea(list_ideas):
+    
+    """
+    Description:
+    
+        The pick_idea() function allows a user to choose one idea from a list of ideas. 
+        It presents the user with a numbered list of ideas and prompts them to select an idea by typing the corresponding number. 
+        The selected idea is then returned as the output of the function.
+    """
+    
+    """
+    Parameters:
+    
+        list_ideas (list of str): A list of ideas from which the user will choose one.
+    """
+    
+    """
+    Return:
+        
+        idea (str): The idea selected by the user from the list of ideas.
+    """
+    
+    print("Choose One Idea:\n")
+    
+    for counter, idea in enumerate(list_ideas):
+        c = counter+1
+        print(f"{c}. {idea}")
+        
+    x = int(input("Type the number of the idea: "))
+    idea = list_ideas[x-1]
+    
+    return idea

insta_image_caption_generation.py

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+from transformers import pipeline
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+
+def img2text(url):
+
+    image_to_text = pipeline("image-to-text", model='Salesforce/blip-image-captioning-base')
+
+    text = image_to_text(url)
+
+    out = text[0]['generated_text']
+    
+    return out
+
+
+def generate_InstaCap(scenario, tone_of_voice, form, llm):
+    
+    instaCap_prompt = f"Craft a {form} Caption on my Instagram Image  Here is the description of my Instagram Image: {scenario}.\nThe tone should be {tone_of_voice}"
+        
+    instaCap_promptTemp = PromptTemplate(
+    input_variables=["text_input"],
+    template="You are infulencer:\n{text_input}\nInstagram Caption:")
+   
+    instaCap_extraction_chain = LLMChain(llm=llm, prompt=instaCap_promptTemp)
+    instaCap = instaCap_extraction_chain.run(instaCap_prompt)
+    
+    return instaCap

keywords_generation.py

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+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+import re
+
+def keywords_generator(topic, llm, num_keywords=10):
+    
+    '''
+    Description: 
+    
+        Generates a list of keywords for a given topic using a large language model (LLM).
+    '''
+    
+    '''
+    Parameters:
+    
+        topic (str): Required Parameter -> The topic for which to generate keywords.
+        llm (langchain.llms object): Required Parameter -> The LLM to use for generating keywords.
+        num_keywords (int): Optional Parameter -> The number of keywords to generate. Default Value is 10
+        long_tail_keywords (bool): Optional Parameter -> A boolean flag indicating whether to generate long-tail keywords. Default Value is False
+    '''
+    
+    '''
+    Returns:
+        Keywords (str): A Text of list of keywords numbered with roman numerals for the given topic -> str datatype.
+    '''
+    
+    keywords_prompt = f"Identify {num_keywords} SEO keywords related to '{topic}'."
+
+    keywords_promptTemp = PromptTemplate(
+    input_variables=["text_input"],
+    template="You are a professional content creator and SEO Keywords Generator:\n\n{text_input}\n\nKeywords (number them with roman numerals):")
+
+    keywords_extraction_chain = LLMChain(llm=llm, prompt=keywords_promptTemp)
+    keywords = keywords_extraction_chain.run(keywords_prompt)
+
+    return keywords
+
+
+def filter_keywords(keywords):
+    '''
+    Description: 
+    
+        Filters keywords to extract the keywords that numbered with roman numerals using regx
+    '''
+    
+    '''
+    Parameters:
+
+        keywords (str): Required Parameter -> A Text of keywords Numbered with roman numerals to filter.
+    '''
+    
+    '''
+    Returns:
+    
+        filtered_keywords(list): A filtered list of keywords.
+    '''
+    
+    pattern = r'\b[IVXLCDM]+\.\s*(.*?)(?:\n|$)'
+    filtered_keywords = re.findall(pattern, keywords)
+    
+    return filtered_keywords
+
+
+def process_keywords(list_keywords):
+    
+    '''
+    Description: 
+        Formats a list of keywords into a comma-separated string.
+    '''
+    
+    '''
+    Parameters:
+
+        list_keywords (list):Required Parameters -> A list of keywords to format.    
+    '''
+    
+    '''
+    Returns:
+    
+        formatted_keywords (str): A comma-separated string of keywords.
+    '''
+    formatted_keywords = ", ".join(list_keywords)
+    return formatted_keywords

landing_page_generation.py

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+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+
+def landing_page_gen(product_name, product_desc, target_audience, goal, llm):
+    
+    landing_page_prompt = f"Generate a landing page content for {product_name}. {product_name} is {product_desc} that is targeted at {target_audience} and has the goal of {goal}. The landing page should be clear, concise, and persuasive. It should also include a call to action."
+    landing_page_promptTemp = PromptTemplate(
+    input_variables=["text_input"],
+    template="{text_input}")
+    landing_page_extraction_chain = LLMChain(llm=llm, prompt=landing_page_promptTemp)
+    landing_page = landing_page_extraction_chain.run(landing_page_prompt)
+    
+    return landing_page

linkedIn_ads_generation.py

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+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+
+def linkedIn_ads_gen(product_name, product_desc, target_audience, target_keywords, llm):
+    
+    
+    linkedIn_ads_prompt = f"Generate a LinkedIn ad for {product_name}. {product_name} is {product_desc} that is targeted at {target_audience} and uses the keywords {target_keywords}. The ad should be persuasive and engaging, and it should include a call to action."
+
+    linkedIn_ads_promptTemp = PromptTemplate(
+    input_variables=["text_input"],
+    template="You are a Professional LinkedIn Ad Copywriter:\n{text_input}\nFacebook Ad:")
+    linkedIn_ad_extraction_chain = LLMChain(llm=llm, prompt=linkedIn_ads_promptTemp)
+    linkedIn_ad = linkedIn_ad_extraction_chain.run(linkedIn_ads_prompt)
+    
+    return linkedIn_ad