code changes done

.env

@@ -1 +1 @@
-cohere_api_key="CTx2vcy4vjHeGC77cGGQXrCCz4xZYxlnPyFWZqqe"
+COHERE_API_KEY="CTx2vcy4vjHeGC77cGGQXrCCz4xZYxlnPyFWZqqe"

advance_post.py

@@ -4,11 +4,12 @@ from langchain.prompts import PromptTemplate
 from langchain_community.document_loaders import WebBaseLoader
 from langchain.prompts import ChatPromptTemplate
 from langchain_core.output_parsers import StrOutputParser
-import nest_asyncio
+from scrap_post import scrappost
 
 
-def google_search(linkedin_post,model , google_api_key, search_engine_id , num_results_per_query=[3,2,1]):
 
+def google_search(url,model , google_api_key, search_engine_id , num_results_per_query=[3,2,1]):
+    post=scrappost(url)
     response_schemas = [
     ResponseSchema(name="questions", description="These are the top three relevant questions from the LinkedIn post" , type="list")]
     output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
@@ -28,10 +29,10 @@ def google_search(linkedin_post,model , google_api_key, search_engine_id , num_r
     )
 
     chain = prompt | model | output_parser
-    result=chain.invoke({"post": linkedin_post})
+    result=chain.invoke({"post": post})
     questions=result['questions']
     # print(questions)
-
+    
     all_links = []
     for query, num_results in zip(questions, num_results_per_query):
         url = f"https://www.googleapis.com/customsearch/v1?key={google_api_key}&cx={search_engine_id}&q={query}&tbm=nws&num={num_results}"
@@ -42,32 +43,12 @@ def google_search(linkedin_post,model , google_api_key, search_engine_id , num_r
         all_links.extend(links)
 
 
-    return all_links
- 
 
-# nest_asyncio.apply()
-# def advanced_post(all_links ,model ,linkedinpost):
-#     loader = WebBaseLoader(all_links,encoding="utf-8")
-#     loader.requests_per_second = 1
-#     docs = loader.load()
-#     template="""You are a  helpful linkedin post creator . You are provided with LinkedIn post and documents related to the post extracted from different articles from the internet.
-#             Your task is to create a new linkedin post but content should be taken from the documents according to the semantic similarity of the post content with document content.
-            
-#             Linkedin post:{post}
-#             Documents:  {content}"""
-                
-#     prompt = ChatPromptTemplate.from_template(template)
-#     chain= prompt | model | StrOutputParser()
-#     result=chain.invoke({'post':linkedinpost , 'content':docs})
-#     return result , docs
-
-nest_asyncio.apply()
-def advanced_post(all_links ,model ,linkedinpost):
+# def advanced_post(all_links ,model ,post):
     loader = WebBaseLoader(all_links,encoding="utf-8")
     loader.requests_per_second = 1
     docs = loader.load()
-    template1="""You are provided  with LinkedIn post and document which is related to the post . Your task is to extract the relevant information from the document which has similarity with LinkedIn post.  
-        Do not add LinkedIn Post content. It should  only from document. 
+    template1="""Extract pertinent information from the provided document that aligns with the content of the LinkedIn post. Focus solely on the document to identify and highlight relevant details that mirror the themes or topics discussed in the post. Avoid incorporating any content from the LinkedIn post itself, ensuring that the extracted information complements and enhances the post's message.
         Linkedin post:{post}
         Document:  {content}"""
             
@@ -75,13 +56,11 @@ def advanced_post(all_links ,model ,linkedinpost):
     chain= prompt | model | StrOutputParser()
     relevant_content=""
     for i in docs:
-        r=chain.invoke({'post':linkedinpost , 'content':i.page_content})
+        r=chain.invoke({'post':post , 'content':i.page_content})
         relevant_content+=r
 
-    template2="""You are provided with a document. Your task is to create a new LinkedIn post. Take content from the document only. Choose the topic of the post wisely.  Do not add anything outside of the provided document content.
-    Format should be professional .It should include topic , headings , key points , stickers and emojis.
-
-    The length of the post should be between 400 to 500 words.
+    template2="""Utilizing the content from the provided document, craft a new LinkedIn post focusing on a carefully chosen topic. Ensure a professional format incorporating headings, key points, stickers, and emojis to enhance engagement. The post's length should not surpass 3000 characters, and all content must be derived solely from the document. 
+                Strive to select a topic that resonates with the document's information and presents it in a compelling and informative manner.
             Document:  {content}"""    
     prompt2 = ChatPromptTemplate.from_template(template2)
     chain2= prompt2 | model | StrOutputParser()

app.py

@@ -1,69 +1,74 @@
 import streamlit as st 
 import re
 import openai
-from paraphrase_post import get_original_url , paraphrased_post
-from advance_post import google_search , advanced_post
+from paraphrase_post import get_original_url , paraphrased_post 
+from advance_post import google_search 
 from langchain_community.chat_models import ChatOpenAI
 from langchain_cohere import ChatCohere
+from streaming_response import StreamHandler
+from blog_post import post_from_content
 # from langchain_nvidia_ai_endpoints import ChatNVIDIA
-
 #from langchain import HuggingFaceHub
 
 
-def main():
-    st.title("LinkedIn Post Creator")
-    
-    # Initialize SessionState dictionary
-    session_state = st.session_state
-    
-    if 'paraphrase' not in session_state:
-        session_state.paraphrase = ""
-    if 'keywords' not in session_state:
-        session_state.keywords = ""
-    if 'take_aways' not in session_state:
-        session_state.take_aways = ""
-    if 'highlights' not in session_state:
-        session_state.highlights = ""
-
-    if 'advancepost' not in session_state:
-        session_state.advancepost = ""
-   
-    url = st.sidebar.text_input("Enter URL:", placeholder="Enter URL here...")
-    option = st.sidebar.selectbox('Select Model:', ('GPT-4',"Cohere"))
-    temperature= st.sidebar.select_slider(
-    'How much accurate post you want ?',
-    options=['Less accuracy', 9, 8, 7, 6, 5,4,3 ,2,1,'High accuracy'])
-    if temperature=='Less accuracy':
-        temperature=10
-    elif temperature=="High accuracy":
-        temperature=0
-    temperature=temperature/10
-
-
-    if option=="GPT-4":
-        api_key=st.sidebar.text_input("API Key:",placeholder="Enter OpenAI API Key...")
-        if api_key:
-            model=ChatOpenAI(model="gpt-4-turbo-preview" , temperature=temperature , api_key=api_key)
-    elif option=="Cohere":
-        api_key= st.sidebar.text_input("API Key:",placeholder="Enter Cohere API Key...")
-        if api_key:
-            model = ChatCohere(cohere_api_key=api_key , temperature = temperature , model = "command-r-plus")
-    # elif option=="Mixtral-8x7b":
-    #     api_key=st.sidebar.text_input("API Key:",placeholder="Enter NVIDIA NIM API Key...")
-    #     if api_key:
-    #         model = ChatNVIDIA(model="mixtral_8x7b",api_key = api_key , temperature=temperature)
-
-    
-
-  
-    # elif option=="Llama-3":
-    #     api_key=st.sidebar.text_input("API Key:",placeholder="Enter HuggingFace API Token...")
-    #     if api_key:
-    #         model=HuggingFaceHub(repo_id="mistralai/Mixtral-8x22B-Instruct-v0.1",huggingfacehub_api_token=api_key ,model_kwargs={"temperature":temperature})
-            
-
-
-            
+# def main():
+st.title("LinkedIn Post Creator")
+
+# Initialize SessionState dictionary
+session_state = st.session_state
+
+if 'paraphrase' not in session_state:
+    session_state.paraphrase = ""
+if 'keywords' not in session_state:
+    session_state.keywords = ""
+if 'take_aways' not in session_state:
+    session_state.take_aways = ""
+if 'highlights' not in session_state:
+    session_state.highlights = ""
+
+if 'advancepost' not in session_state:
+    session_state.advancepost = ""
+
+# url = st.sidebar.text_input("Enter URL:", placeholder="Enter URL here...")
+option = st.sidebar.selectbox('Select Model:', ('GPT-4',"Cohere"))
+temperature= st.sidebar.select_slider(
+'How much accurate post you want ?',
+options=['Less accuracy', 9, 8, 7, 6, 5,4,3 ,2,1,'High accuracy'])
+if temperature=='Less accuracy':
+    temperature=10
+elif temperature=="High accuracy":
+    temperature=0
+temperature=temperature/10
+
+# stream_handler = StreamHandler(st.empty())  
+
+if option=="GPT-4":
+    api_key=st.sidebar.text_input("API Key:",placeholder="Enter OpenAI API Key...")
+    if api_key:
+        model=ChatOpenAI(model="gpt-4-turbo-preview" , temperature=temperature , api_key=api_key)
+elif option=="Cohere":
+    api_key= st.sidebar.text_input("API Key:",placeholder="Enter Cohere API Key...")
+    if api_key:
+        stream_handler = StreamHandler(st.empty())    
+        model = ChatCohere(cohere_api_key=api_key,temperature=temperature ,  streaming=True, callbacks=[stream_handler])
+        #model = ChatCohere(cohere_api_key=api_key , temperature = temperature , model = "command-r-plus")
+# elif option=="Mixtral-8x7b":
+#     api_key=st.sidebar.text_input("API Key:",placeholder="Enter NVIDIA NIM API Key...")
+#     if api_key:
+#         model = ChatNVIDIA(model="mixtral_8x7b",api_key = api_key , temperature=temperature)
+
+
+
+
+# elif option=="Llama-3":
+#     api_key=st.sidebar.text_input("API Key:",placeholder="Enter HuggingFace API Token...")
+#     if api_key:
+#         model=HuggingFaceHub(repo_id="mistralai/Mixtral-8x22B-Instruct-v0.1",huggingfacehub_api_token=api_key ,model_kwargs={"temperature":temperature})
+        
+
+
+if st.sidebar.toggle("Normal LinkedIn Post"):        
+    url = st.sidebar.text_input("Enter URL:", placeholder="Enter URL here...")   
     if st.sidebar.button("Submit"):
         if url:
             if api_key:
@@ -72,7 +77,7 @@ def main():
 
                 if match:
                     try:
-                        session_state.paraphrase, session_state.keywords, session_state.take_aways, session_state.highlights = paraphrased_post(url , model)
+                        session_state.paraphrase = paraphrased_post(url , model) 
                     except (openai.AuthenticationError) as e:
                         st.sidebar.error("Enter your valid API key")
                 else:
@@ -81,55 +86,100 @@ def main():
                 st.sidebar.error("Please enter API Key")
         else:
             st.sidebar.error("Please enter url")
-
-    # def stream_data():
-    #     import time 
-    #     for word in session_state.paraphrase.split(" "):
-    #         yield word + " "
-    #         time.sleep(0.02)
-    
-            
-    #         st.write_stream(word +" ")
+    from paraphrase_post import generate_details
+    if st.sidebar.button("Show Details"):
+        session_state.keywords =generate_details(session_state.paraphrase , model) 
+
+
+
+# paraphrase_text=st.text_area("Generated LinkedIn post",value=session_state.paraphrase, height=400)
+# import pyperclip
+# if st.button('Copy'):
+#     pyperclip.copy(paraphrase_text)
+#     st.success('Text copied successfully!')
+# from paraphrase_post import extract_data
+# # session_state.keywords , session_state.take_aways , session_state.highlights =extract_data(session_state.paraphrase , model) 
+# from paraphrase_post import generate_details
+# if st.sidebar.button("Show Details"):
+#     session_state.keywords =generate_details(session_state.paraphrase , model)   
+        # session_state.take_aways =generate_takeaways(session_state.paraphrase , model)
+        # session_state.highlights =generate_highlights(session_state.paraphrase , model) 
+        
+        # st.write("Keywords:")
+        # for i, statement in enumerate(session_state.keywords, start=1):
+        #     st.write(f"{i}. {statement}")
     
-    paraphrase_text=st.text_area("Generated LinkedIn post",value=session_state.paraphrase, height=400)
-    # import pyperclip
-    # if st.button('Copy'):
-    #     pyperclip.copy(paraphrase_text)
-    #     st.success('Text copied successfully!')
-                
-    if st.sidebar.toggle("Show Details") and session_state.keywords:
-        st.write("Keywords:")
-        for i, statement in enumerate(session_state.keywords, start=1):
-            st.write(f"{i}. {statement}")
-    
-        st.write("Take Aways:")
-        for i, statement in enumerate(session_state.take_aways, start=1):
-            st.write(f"{i}. {statement}")
+        # st.write("Take Aways:")
+        # for i, statement in enumerate(session_state.take_aways, start=1):
+        #     st.write(f"{i}. {statement}")
 
-        st.write("Highlights:")
-        for i, statement in enumerate(session_state.highlights, start=1):
-            st.write(f"{i}. {statement}")
+        # st.write("Highlights:")
+        # for i, statement in enumerate(session_state.highlights, start=1):
+        #     st.write(f"{i}. {statement}")
 
 #------------------------------------------------------------Advance LinkedIn post code below-----------------------------------------------------------------
-   
-    if st.sidebar.toggle("Advance LinkedIn Post"):
-        google_api_key=st.sidebar.text_input("Google API Key:",placeholder="Enter Google Search API Key...")
-        search_engine_id=st.sidebar.text_input("Search Engine ID:",placeholder="Enter Search Engine ID...")
-        google_api_key = "AIzaSyABoOsfUbydc1AfVf2Zjto4MFWNgjLSJmY"
-        search_engine_id = "243a392ba3dff442c"
-        if st.sidebar.button("Generate Advance Post"):
-            if google_api_key:
-                if search_engine_id:
-                    all_links =google_search(session_state.paraphrase ,model , google_api_key,search_engine_id)
-                    session_state.advancepost = advanced_post(all_links ,model ,session_state.paraphrase)
-                    # if len(docs)==0:
-                    #     st.sidebar.error("Please Check your both credentials carefully")
-       
-                else:
-                    st.sidebar.error("Please enter Search Engine ID")
+
+
+if st.sidebar.toggle("Advance LinkedIn Post"):
+    url = st.sidebar.text_input("Enter URL", placeholder="Enter URL here...")   
+    google_api_key=st.sidebar.text_input("Google API Key:",placeholder="Enter Google Search API Key...")
+    search_engine_id=st.sidebar.text_input("Search Engine ID:",placeholder="Enter Search Engine ID...")
+    google_api_key = "AIzaSyABoOsfUbydc1AfVf2Zjto4MFWNgjLSJmY"
+    search_engine_id = "243a392ba3dff442c"
+    if st.sidebar.button("Generate Advance Post"):
+        if url:
+            if api_key:
+                if option=="GPT-4":
+                        model=ChatOpenAI(model="gpt-4-turbo-preview" , temperature=temperature , api_key=api_key)
+                elif option=="Cohere":
+                        model = ChatCohere(cohere_api_key=api_key,temperature=temperature)
+                        if google_api_key:
+                            if search_engine_id:
+                                original_url = get_original_url(url)
+                                match = re.match(r"https?://(?:www\.)?linkedin\.com/(posts|feed|pulse)/.*", original_url)  # checking domain and url page (means it should only be a post nothing else like login page or something else)
+
+                                if match:
+                                    try:
+                                        session_state.advancepost =google_search(url ,model , google_api_key,search_engine_id)
+                                        # session_state.advancepost = advanced_post(all_links ,model ,session_state.paraphrase)
+                                    except (openai.AuthenticationError) as e:
+                                        st.sidebar.error("Enter your valid API key")
+                                else:
+                                    st.sidebar.error("Put a valid LinkedIn post url only")
+                                    # if len(docs)==0:
+                                #     st.sidebar.error("Please Check your both credentials carefully")
+                    
+                            else:
+                                st.sidebar.error("Please enter Search Engine ID")
+                        else:
+                            st.sidebar.error("Please enter Google API Key")
+            else:
+                st.sidebar.error("Please enter API key")
+        else:
+            st.sidebar.error("Please enter url")
+
+    advance_post=st.text_area("Advance LinkedIn post",value=session_state.advancepost, height=400) 
+
+
+
+
+# from streaming_response import StreamHandler
+if st.sidebar.toggle("Message"):
+    content=st.sidebar.text_area("Paste your content" ,height=300)
+    if st.sidebar.button("Generate Post"):
+        if content:
+            if api_key:
+                # with st.container(height=500):
+                    # stream_handler = StreamHandler(st.empty())    
+                    # model = ChatCohere(cohere_api_key=api_key,temperature=temperature ,  streaming=True, callbacks=[stream_handler])
+                    post_from_content(model ,content)
+                    
+                
             else:
-                st.sidebar.error("Please enter Google API Key")
-        advance_post=st.text_area("Advance LinkedIn post",value=session_state.advancepost, height=400) 
+                st.sidebar.error("Please enter API Key")
+        else:
+            st.sidebar.error("You have not provided any content in Textbox")
+    
             
     
         # if st.button('Copy Advanced Post'):
@@ -137,8 +187,8 @@ def main():
         #     st.success('Text copied successfully!')
 #--------------------------------------------------------------------------------------------------------------------------------------------------------------
 
-if __name__ == "__main__":
-    main()
+# if __name__ == "__main__":
+#     main()
 
 
 

blog_post.py

@@ -0,0 +1,26 @@
+from langchain.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+
+
+def post_from_content(model , content):
+
+
+    template="""  As a professional LinkedIn post creator tool, your task is to craft a compelling post based on the content provided. Adhere to the following guidelines: 
+                        1. Post length should not exceed 3000 characters.
+                        2. Select a fitting title, employ professional formatting, incorporate stickers, emojis, relevant hashtags, links (if applicable in the content), and references. 
+                        3. Additionally, if the content includes code snippets, ensure to present them appropriately within the post. 
+                        
+                        Execute these steps thoughtfully to create an engaging and polished LinkedIn post.
+
+
+                
+    Content: {content}\n\n
+
+    Output should only be generated LinkedIn post so that I can quickly copy it and put on my LinkedIn page without doing any modifications.
+    
+    """
+
+    prompt = ChatPromptTemplate.from_template(template)
+    chain = prompt | model | StrOutputParser()
+    generated_post=chain.invoke({"content":content})
+    return generated_post

paraphrase_post.py

@@ -42,58 +42,71 @@ def get_original_url(url):
 # Below function extract the post only content from complete web page content and parraphrase the extracted post 
     
 def paraphrased_post(url,model):
-
     post=scrappost(url)
-    # print(post)
+    template="""  Create a paraphrased version of a given LinkedIn post while preserving the core message and tone. Ensure the paraphrased content is clear, engaging, and suitable for professional communication on LinkedIn. Focus on rephrasing the post to enhance readability and broaden its appeal to a diverse audience.
+    LinkedIn post: {data}
 
-    template=""" You are provided with LinkedIn post below . Paraphrase this post in professional way.You can change the writing style as compared to the provided post .Keep stickers , emojis , links and hastags. Final output should only be paraphrased post . 
-    LinkedIn post: {data}"""
+    The output should only the paraphrased post.
+    """
 
     prompt = ChatPromptTemplate.from_template(template)
 
     chain = prompt | model | StrOutputParser()
     phrased_post=chain.invoke({"data":post})
+    return phrased_post
+   
+
+
+def generate_details(post_data,model):
+    template=""" Extract the top three keywords , take aways and highlights from a LinkedIn post in descending order of relevance. Provide only the three most significant keywords that encapsulate the main topic or message of the post.
+    LinkedIn post: {data}
+    Keywords:\n\n
+
+    Output should only include keywords , take aways and highlights.
+
+    """
+    prompt = ChatPromptTemplate.from_template(template)
+    chain = prompt | model | StrOutputParser()
+    keywords=chain.invoke({"data":post_data})
+    return keywords
 
-    data2=extract_data(phrased_post , model)
-    keywords=data2['Keywords'][:3]
-    take_aways=data2['Take Aways'][:3]
-    highlights=data2['Highlights'][:3]
-    return phrased_post,keywords , take_aways, highlights
-    
 
 
 
 
-# Below function extract the details such as keywords  , Take aways , highlights and questions 
-def extract_data(post_data ,model):
-    keywords = ResponseSchema(name="Keywords",
-                        description="These are the keywords extracted from LinkedIn post",type="list")
+# # Below function extract the details such as keywords  , Take aways , highlights and questions 
+# def extract_data(post_data ,model):
+#     keywords = ResponseSchema(name="Keywords",
+#                         description="These are the keywords extracted from LinkedIn post",type="list")
 
-    Take_aways = ResponseSchema(name="Take Aways",
-                                description="These are the take aways extracted from LinkedIn post", type= "list")
-    Highlights=ResponseSchema(name="Highlights",
-                                description="These are the highlights extracted from LinkedIn post", type= "list")
+#     # Take_aways = ResponseSchema(name="Take Aways",
+#     #                             description="These are the take aways extracted from LinkedIn post", type= "list")
+#     # Highlights=ResponseSchema(name="Highlights",
+#     #                             description="These are the highlights extracted from LinkedIn post", type= "list")
     
     
-    response_schema = [
-        keywords,
-        Take_aways,
-        Highlights
-
-    ]
-    output_parser = StructuredOutputParser.from_response_schemas(response_schema)
-    format_instructions = output_parser.get_format_instructions()
-
-    template = """
-        You are a helpful keywords , take aways and highlights extractor from the post of LinkedIn Bot. Your task is to extract relevant keywords , take aways and highlights in descending order of their scores in a list, means high relevant should  be on the top .
-        From the following text message, extract the following information:
-
-        text message: {content}
-        {format_instructions}
-        """
+#     response_schema = [
+#         keywords,
+#         # Take_aways,
+#         # Highlights
+
+#     ]
+#     output_parser = StructuredOutputParser.from_response_schemas(response_schema)
+#     format_instructions = output_parser.get_format_instructions()
+
+#     template = """
+#         You are a helpful keywords extractor from the post of LinkedIn Bot. Your task is to extract relevant keywords in descending order of their scores in a list, means high relevant should  be on the top .
+#         From the following text message, extract the following information:
+
+#         text message: {content}
+#         {format_instructions}
+#         """
     
-    prompt_template = ChatPromptTemplate.from_template(template)
-    messages = prompt_template.format_messages(content=post_data, format_instructions=format_instructions)
-    response = model(messages)
-    output_dict=  output_parser.parse(response.content)
-    return  output_dict
+#     prompt_template = ChatPromptTemplate.from_template(template)
+#     messages = prompt_template.format_messages(content=post_data, format_instructions=format_instructions)
+#     response = model(messages)
+#     output_dict=  output_parser.parse(response.content)
+#     keywords=output_dict['Keywords'][:3]
+#     # take_aways=output_dict['Take Aways'][:3]
+#     # highlights=output_dict['Highlights'][:3]
+#     return  keywords 

streaming_response.py

@@ -0,0 +1,36 @@
+from langchain.callbacks.base import BaseCallbackHandler
+
+
+class StreamHandler(BaseCallbackHandler):
+    def __init__(self, container, initial_text=""):
+        self.container = container
+        self.text = initial_text
+
+    def on_llm_new_token(self, token: str, **kwargs) -> None:
+        self.text += token
+        self.container.markdown(self.text)
+
+
+# with st.sidebar:
+#     openai_api_key = st.text_input("OpenAI API Key", type="password")
+
+# if "messages" not in st.session_state:
+#     st.session_state["messages"] = [ChatMessage(role="assistant", content="How can I help you?")]
+
+# for msg in st.session_state.messages:
+#     st.chat_message(msg.role).write(msg.content)
+
+# if prompt := st.chat_input():
+#     st.session_state.messages.append(ChatMessage(role="user", content=prompt))
+#     st.chat_message("user").write(prompt)
+
+#     if not openai_api_key:
+#         st.info("Please add your OpenAI API key to continue.")
+#         st.stop()
+
+#     with st.chat_message("assistant"):
+#         stream_handler = StreamHandler(st.empty())
+#         # llm = ChatOpenAI(openai_api_key=openai_api_key, streaming=True, callbacks=[stream_handler])
+#         llm = ChatCohere(openai_api_key=openai_api_key, streaming=True, callbacks=[stream_handler])
+#         response = llm.invoke(st.session_state.messages)
+#         st.session_state.messages.append(ChatMessage(role="assistant", content=response.content))