WIP - Streamlit UI, Porting CLI

README.md

@@ -1,5 +1,10 @@
-# How to Use AI Content Generation Toolkit - Alwrity
+# How to Alwrity - Getting Started
 
+Alwrity assists content creators and digital marketers in generating, formatting, and uploading blog content with unprecedented efficiency.
+
+Our toolkit integrates advanced AI models for text generation, image creation, and data analysis, streamlining your content creation pipeline and ensuring high-quality output with minimal effort.
+
+---
 1). [Visit alwrity.com](https://www.alwrity.com/ai-writing-tools), You will find AI content writing tools, which are Free & No-Signup.
 **Note:** Although, this is limited, as is our wallet & Resources.
 

alwrity_streamlit.py

@@ -6,11 +6,13 @@ import streamlit as st
 # Load .env file
 load_dotenv()
 
+from lib.chatbot_custom.chatbot_local_docqa import alwrity_chat_docqa
 from lib.utils.alwrity_streamlit_utils import (
         blog_from_keyword, ai_agents_team, 
         blog_from_audio, write_story,
         essay_writer, ai_news_writer,
-        ai_finance_ta_writer, ai_social_writer
+        ai_finance_ta_writer, ai_social_writer,
+        do_web_research, competitor_analysis,
         )
 
 # Custom CSS for styling
@@ -291,8 +293,10 @@ def main():
         alwrity_brain()
 
     with tab5:
-        st.title("🙎 Ask Alwrity 🤦")
+        st.info("Chatbot")
-        st.write("Oh, you decided to talk to a chatbot? I guess even Netflix can't... Shall we get this over with?")
+        st.markdown("Create a collection by uploading files (PDF, MD, CSV, etc), or crawl a data source (Websites, more sources coming soon.")
+        st.markdown("One can ask/chat, summarize and do semantic search over the uploaded data")
+        #alwrity_chat_docqa()
     
     # Sidebar for prompt modification
     st.sidebar.title("📝 Modify Prompts")
@@ -350,19 +354,17 @@ def content_planning_tools():
              Provide few keywords to get Google, Neural, pytrends analysis. Know keywords, blog titles to target.
              Generate months long content calender around given keywords.""")
     options = [
-        "Keywords web research🤓",
+        "Keywords Researcher",
-        "Competitor Analysis🧐",
+        "Competitor Analysis"
-        "Give me content calendar 🥹🥹"
     ]
     choice = st.selectbox("Select a content planning tool:", options, index=0, format_func=lambda x: f"🔍 {x}")
 
-    if st.button("Plan Content"):
+    if choice == "Keywords Researcher":
-        if choice == "Keywords web research🤓":
+        do_web_research()
-            do_web_research()
+    elif choice == "Competitor Analysis":
-        elif choice == "Competitor Analysis🧐":
+        competitor_analysis()
-            competitor_analysis()
+    #elif choice == "Get Content Calender":
-        elif choice == "Give me content calendar 🥹🥹":
+    #    planning_agents()
-            content_planning_agents()
 
 
 def alwrity_brain():

lib/ai_web_researcher/google_trends_researcher.py

@@ -187,7 +187,8 @@ def get_related_topics_and_save_csv(search_keywords):
             data = pytrends.related_topics()
         except Exception as err:
             logger.error(f"Failed to get pytrends realted topics: {err}")
-            return
+            return None
+
         # Extract data from the result
         top_topics = list(data.values())[0]['top']
         rising_topics = list(data.values())[0]['rising']

lib/ai_web_researcher/metaphor_basic_neural_web_search.py

@@ -7,6 +7,7 @@ from pathlib import Path
 from metaphor_python import Metaphor
 from datetime import datetime, timedelta
 
+import streamlit as st 
 from loguru import logger
 from tqdm import tqdm
 from tabulate import tabulate
@@ -71,28 +72,45 @@ def metaphor_find_similar(similar_url):
         raise
 
     competitors = search_response.results
-    urls = {}
+    # Initialize lists to store titles and URLs
+    titles = []
+    urls = []
+
+    # Initialize lists to store titles, URLs, and contents
+    titles = []
+    urls = []
+    contents = []
+    
+    # Extract titles, URLs, and contents from the competitors
     for c in competitors:
-        print(c.title + ':' + c.url)
+        titles.append(c.title)
-    for acompetitor in tqdm(competitors, desc="Processing URL content", unit="competitor"):
+        urls.append(c.url)
+        # Simulate web content fetching and summarization (replace with actual logic)
         all_contents = ""
         try:
             search_response = metaphor.search_and_contents(
-                acompetitor.url,
+                c.url,
                 type="keyword",
-                num_results=3
+                num_results=1
             )
+            research_response = search_response.results
+            for r in research_response:
+                all_contents += r.text
+            c.text = summarize_competitor_content(all_contents)  # Replace with actual summarization function
         except Exception as err:
-            logger.error(f"Failed to do metaphor keyword/url research: {err}")
+            c.text = f"Failed to summarize content: {err}"
+        contents.append(c.text)
     
-        research_response = search_response.results
+    # Create a DataFrame from the titles, URLs, and contents
-        # Add a progress bar for the inner loop
+    df = pd.DataFrame({
-        for r in tqdm(research_response, desc=f"{acompetitor.url}", unit="research"):
+        "Title": titles,
-            all_contents += r.text
+        "URL": urls,
-            try:
+        "Content Summary": contents
-                acompetitor.text = summarize_competitor_content(all_contents)
+    })
-            except Exception as err:
+    # Display the DataFrame as a table
-                logger.error(f"Failed to summarize_web_content: {err}")
+    if not df.empty:
+        st.write("### Competitor Analysis Results")
+        st.table(df)
  
     print_search_result(competitors)
     return search_response
@@ -179,6 +197,12 @@ def print_search_result(contents_response):
         tablefmt="fancy_grid",
         colalign=["left", "left", "left"],
         maxcolwidths=[20, 20, 70])
+
+    # Convert table_data to DataFrame
+    import pandas as pd
+    df = pd.DataFrame(table_data, columns=["URL", "Title", "Summary"])
+    import streamlit as st
+    st.table(df)
     print(table)
     # Save the combined table to a file
     try:

lib/chatbot_custom/chat_history_chatbot.py

@@ -0,0 +1,104 @@
+import time
+import os
+import joblib
+import streamlit as st
+import google.generativeai as genai
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+GOOGLE_API_KEY = os.environ.get('GOOGLE_API_KEY')
+genai.configure(api_key=os.environ.get('GEMINI_API_KEY'))
+
+# Constants
+MODEL_ROLE = 'ai'
+AI_AVATAR_ICON = '👄'
+DATA_DIR = 'data/'
+
+
+def history_chatbot():
+	# Ensure the data/ directory exists
+	os.makedirs(DATA_DIR, exist_ok=True)
+	
+	# Generate a new chat ID
+	new_chat_id = f'{time.time()}'
+	
+	# Load past chats if available
+	try:
+	    past_chats = joblib.load(os.path.join(DATA_DIR, 'past_chats_list'))
+	except FileNotFoundError:
+	    past_chats = {}
+	
+	# Sidebar for past chats
+	with st.sidebar:
+	    st.write('# Past Chats')
+	    if 'chat_id' not in st.session_state:
+	        st.session_state.chat_id = st.selectbox(
+	            label='Pick a past chat',
+	            options=[new_chat_id] + list(past_chats.keys()),
+	            format_func=lambda x: past_chats.get(x, 'New Chat'),
+	            placeholder='_'
+	        )
+	    else:
+	        st.session_state.chat_id = st.selectbox(
+	            label='Pick a past chat',
+	            options=[new_chat_id, st.session_state.chat_id] + list(past_chats.keys()),
+	            index=1,
+	            format_func=lambda x: past_chats.get(x, 'New Chat' if x != st.session_state.chat_id else st.session_state.chat_title),
+	            placeholder='_'
+	        )
+	    st.session_state.chat_title = f'ChatSession-{st.session_state.chat_id}'
+	
+	# Load chat history if available
+	try:
+	    st.session_state.messages = joblib.load(os.path.join(DATA_DIR, f'{st.session_state.chat_id}-st_messages'))
+	    st.session_state.gemini_history = joblib.load(os.path.join(DATA_DIR, f'{st.session_state.chat_id}-gemini_messages'))
+	    print('Loaded existing chat history')
+	except FileNotFoundError:
+	    st.session_state.messages = []
+	    st.session_state.gemini_history = []
+	    print('Initialized new chat history')
+	
+	# Configure the AI model
+	st.session_state.model = genai.GenerativeModel('gemini-pro')
+	st.session_state.chat = st.session_state.model.start_chat(history=st.session_state.gemini_history)
+	
+	# Display past messages
+	for message in st.session_state.messages:
+	    with st.chat_message(name=message['role'], avatar=message.get('avatar')):
+	        st.markdown(message['content'])
+	
+	# Handle user input
+	if prompt := st.chat_input('Ask Alwrity...'):
+	    if st.session_state.chat_id not in past_chats:
+	        past_chats[st.session_state.chat_id] = st.session_state.chat_title
+	        joblib.dump(past_chats, os.path.join(DATA_DIR, 'past_chats_list'))
+	
+	    # Display and save user message
+	    with st.chat_message('user'):
+	        st.markdown(prompt)
+	    st.session_state.messages.append({'role': 'user', 'content': prompt})
+	
+	    # Send message to AI and stream the response
+	    response = st.session_state.chat.send_message(prompt, stream=True)
+	    full_response = ''
+	    with st.chat_message(name=MODEL_ROLE, avatar=AI_AVATAR_ICON):
+	        message_placeholder = st.empty()
+	        for chunk in response:
+	            for ch in chunk.text.split(' '):
+	                full_response += ch + ' '
+	                time.sleep(0.05)
+	                message_placeholder.write(full_response + '▌')
+	        message_placeholder.write(full_response)
+	
+	    # Save the AI response
+	    st.session_state.messages.append({
+	        'role': MODEL_ROLE,
+	        'content': full_response,
+	        'avatar': AI_AVATAR_ICON
+	    })
+	    st.session_state.gemini_history = st.session_state.chat.history
+	
+	    # Persist chat history to disk
+	    joblib.dump(st.session_state.messages, os.path.join(DATA_DIR, f'{st.session_state.chat_id}-st_messages'))
+	    joblib.dump(st.session_state.gemini_history, os.path.join(DATA_DIR, f'{st.session_state.chat_id}-gemini_messages'))

lib/chatbot_custom/chatbot_local_docqa.py

@@ -0,0 +1,101 @@
+import os
+import streamlit as st
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext, Document
+from llama_index.llms.openai import OpenAI
+import openai
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv(Path("../../.env"))
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+
+def initialize_session_state():
+    """Initialize the chat message history in session state."""
+    if "messages" not in st.session_state:
+        st.session_state.messages = [
+            {"role": "assistant", "content": f"Ask me a question about documents from {LOCAL_BRAIN_DATA} or from the Web."}
+        ]
+
+
+@st.cache_resource(show_spinner=False)
+def load_data(input_dir):
+    """Load and index documents from the specified directory."""
+    with st.spinner("Loading and indexing your docs – hang tight! This should take 1-2 minutes."):
+        reader = SimpleDirectoryReader(input_dir=input_dir, recursive=True)
+        docs = reader.load_data()
+        service_context = ServiceContext.from_defaults(
+            llm=OpenAI(
+                model="gpt-3.5-turbo",
+                temperature=0.5,
+                system_prompt=(
+                    "You are an expert on content & digital marketing and your job is to answer technical questions."
+                    "Assume that all questions are related to provided documents, as context."
+                    "Keep your answers technical and based on facts – do not hallucinate features."
+                )
+            )
+        )
+        index = VectorStoreIndex.from_documents(docs, service_context=service_context)
+        return index
+
+
+def display_chat_history():
+    """Display the chat message history."""
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.write(message["content"])
+
+
+def generate_response(prompt, chat_engine):
+    """Generate a response from the chat engine and update the chat history."""
+    if prompt:
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                response = chat_engine.chat(prompt)
+                st.write(response.response)
+                st.session_state.messages.append({"role": "assistant", "content": response.response})
+
+
+def alwrity_chat_docqa():
+    """Main function to run the Streamlit app."""
+    st.header("Ask Alwrity 💬 📚")
+    initialize_session_state()
+    option = st.radio(
+        "Choose Data Source To Ask From:",
+        ("Ask Your Local Docs", "Ask Your PDFs", "Ask Your Videos", "Ask Your Audio Files")
+    )
+
+    if option == "Ask Your Local Docs":
+        input_dir = st.text_input("Enter the path to the folder:")
+        if input_dir:
+            st.session_state.input_dir = input_dir
+
+    elif option == "Ask Your PDFs":
+        pdf_file = st.file_uploader("Upload a PDF file or enter a URL:", type=["pdf"])
+        if pdf_file:
+            st.session_state.input_file = pdf_file
+
+    elif option == "Ask Your Videos":
+        video_dir = st.text_input("Enter the path to the video folder:")
+        if video_dir:
+            st.session_state.input_dir = video_dir
+
+    elif option == "Ask Your Audio Files":
+        audio_dir = st.text_input("Enter the path to the audio folder:")
+        if audio_dir:
+            st.session_state.input_dir = audio_dir
+
+    if 'input_dir' in st.session_state:
+        index = load_data(st.session_state.input_dir)
+        chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)
+        display_chat_history()
+        prompt = st.chat_input("Your question")
+        if st.session_state.messages[-1]["role"] != "assistant":
+            generate_response(prompt, chat_engine)
+    
+    elif 'input_file' in st.session_state:
+        # Handle PDF file or URL input here
+        st.write("Handling PDF file or URL input is not implemented yet.")

lib/content_planning_calender/content_planning_agents_alwrity_crew.py

@@ -1,4 +1,6 @@
 import os
+import streamlit as st
+
 from crewai import Agent, Task, Crew
 from crewai_tools import SerperDevTool
 from langchain_google_genai import ChatGoogleGenerativeAI
@@ -162,7 +164,7 @@ def create_agents(search_keywords, already_written_on):
 def create_tasks(agents, search_keywords, already_written_on):
     research_task = Task(
         description=f"""Conduct web analysis on "{search_keywords}",for content calender.
-            Set the input parameter as : search_query""",
+            Set the input parameter 'search_query' to query""",
         expected_output=f"""Provide comprehensive content calender ideas to Senior Content Strategist & planner - Ted XingPi""",
         agent=agents[0]  # Assign to the researcher agent
     )
@@ -170,7 +172,7 @@ def create_tasks(agents, search_keywords, already_written_on):
     google_trends_task = Task(
             description=f"""Conduct Google Trends analysis, on keywords: {search_keywords}, from the file({os.getenv('SEARCH_SAVE_FILE')}).
             Suggest blog titles for content calender. Recommend high-volume, low-competition keywords with strong user intent.
-            Set the input parameter as : file_path""",
+            Set the input parameter 'file_path' to {os.getenv('SEARCH_SAVE_FILE')}""",
         expected_output=f"Provide comprehensive content calender ideas to Senior Content Strategist & planner - Ted XingPi",
         agent=agents[1]  # Assign to the researcher agent
     )
@@ -189,7 +191,7 @@ def create_tasks(agents, search_keywords, already_written_on):
         description=f"""Make sure the content calender is optimised for keywords: '{search_keywords}'.
             Make sure the titles are unique, semantically unique and mitigate keyword cannabilization.
             Use context & insights from Aisha Sharma, Ted XingPi & Sarah Qureshi.
-            Set the input parameter file_path to {already_written_on}
+            Set the input parameter 'file_path' to {already_written_on}
         """,
         expected_output=f"""Final content calender for the next 2 months. Targeting 5 articles per week.
             Make sure to present the content calender in tabular format. Include details of how to use the content calender.
@@ -201,6 +203,8 @@ def create_tasks(agents, search_keywords, already_written_on):
 
 
 def execute_tasks(agents, tasks):
+    """ WIP """
+    result = None
     crew = Crew(
         agents=agents,
         tasks=tasks,
@@ -209,17 +213,23 @@ def execute_tasks(agents, tasks):
         #memory=True,
         language="en"
     )
-    result = crew.kickoff()
+    try:
-    return result
+        result = crew.kickoff()
+        return result
+    except Exception as err:
+        print(err)
 
 
 def ai_agents_planner(search_keywords):
     already_written_on = os.path.join(os.getcwd(), "lib", "content_planning_calender", "content_already_planned.txt")
     do_google_trends_analysis(search_keywords)
-
+    result = None
     #setup_environment()
-    agents = create_agents(search_keywords, already_written_on)
+    try:
-    tasks = create_tasks(agents, search_keywords, already_written_on)
+        agents = create_agents(search_keywords, already_written_on)
-    result = execute_tasks(agents, tasks)
+        tasks = create_tasks(agents, search_keywords, already_written_on)
-    print("########## Final Output Result ############")
+        result = execute_tasks(agents, tasks)
-    print(result)
+    except Exception as err:
+        print(err)
+    st.markdown("### Final Content Calender:")
+    st.markdown(result)

lib/utils/alwrity_streamlit_utils.py

@@ -3,6 +3,7 @@ import streamlit as st
 from pathlib import Path
 import configparser
 from datetime import datetime
+import uuid
 
 from rich import print
 from lib.ai_web_researcher.gpt_online_researcher import gpt_web_researcher
@@ -29,43 +30,42 @@ def blog_from_keyword():
     """ Input blog keywords, research and write a factual blog."""
     st.markdown("<div class='sub-header'>Blog from Keywords</div>", unsafe_allow_html=True)
 
-    content_keywords = st.text_input('Enter Keywords/Blog Title',
+    blog_keywords = st.text_input('Enter Keywords/Blog Title',
             help='Check your keywords against Google search, if you get good search results, you will get good content with Alwrity.',
             placeholder='Shit in, Shit Out; Better keywords, better research, hence better content.\n👋 :')
 
-    if content_keywords and len(content_keywords.split()) < 2:
+    if blog_keywords and len(blog_keywords.split()) < 2:
         st.error('🚫 Blog keywords should be at least two words long. Please try again.')
 
     content_type = st.selectbox("Select content type:", ["Normal-length content", "Long-form content", "Experimental - AI Agents team"])
     if st.button("Write Blog"):
         # Clear the previous results from the screen
         st.empty()
-        if content_keywords and len(content_keywords.split()) >= 2:
+        if blog_keywords and len(blog_keywords.split()) >= 2:
             if content_type == "Normal-length content":
                 try:
-                    short_blog = write_blog_from_keywords(content_keywords)
+                    short_blog = write_blog_from_keywords(blog_keywords)
                     st.markdown(short_blog)
-                    st.success(f"Successfully wrote blog on: {content_keywords}")
                 except Exception as err:
-                    st.error(f"🚫 Failed to write blog on {content_keywords}, Error: {err}")
+                    st.error(f"🚫 Failed to write blog on {blog_keywords}, Error: {err}")
             elif content_type == "Long-form content":
                 try:
                     st.empty()
-                    long_form_generator(content_keywords)
+                    long_form_generator(blog_keywords)
-                    st.success(f"Successfully wrote long-form blog on: {content_keywords}")
+                    st.success(f"Successfully wrote long-form blog on: {blog_keywords}")
                 except Exception as err:
-                    st.error(f"🚫 Failed to write blog on {content_keywords}, Error: {err}")
+                    st.error(f"🚫 Failed to write blog on {blog_keywords}, Error: {err}")
             elif content_type == "Experimental - AI Agents team":
                 try:
-                    ai_agents_writers(content_keywords)
+                    ai_agents_writers(blog_keywords)
-                    st.success(f"Successfully wrote content with AI agents on: {content_keywords}")
+                    st.success(f"Successfully wrote content with AI agents on: {blog_keywords}")
                 except Exception as err:
                     st.error(f"🚫 Failed to Write content with AI agents: {err}")
 
 
 def ai_agents_team():
     # Define options for AI Content Teams
-    st.title("🧚🐲 AI Agents Content Teams")
+    st.title("🧚🐲 Your AI Agents Teams")
     st.markdown("""Alwrity offers AI agents team for content creators to easily modify them for their needs.
                 Abstracting tech & plumbing, easily define role, goal, task. Use different AI agents framework.""")
     options = [
@@ -77,16 +77,33 @@ def ai_agents_team():
     selected_team = st.selectbox("**Choose AI Agents Team:**", options)
 
     if selected_team == "AI Content Ideation & Planning Team":
-        content_planning_agents()
+        st.title("AI Agents for Content Ideation")
+        plan_keywords = st.text_input(
+            "Enter Keywords to get 2 months content calender:",
+            placeholder="Enter keywords to generate AI content calendar:",
+            help="Enter at least two words for better results."
+        )
+        if st.button("Get calender"):
+            if plan_keywords and len(plan_keywords.split()) >= 2:
+                with st.spinner("Get Content Plan..."):
+                    try:
+                        plan_content = ai_agents_planner(plan_keywords)
+                        st.success(f"Successfully generated content plan for: {plan_keywords}")
+                        st.markdown(plan_content)
+                    except Exception as err:
+                        st.error(f"Failed to generate content plan: {err}")
+            else:
+                st.error("🚫 Single keywords are just too vague. Try again.")
+
     elif selected_team == "AI Content Creation Team":
-        content_creation_agents()
+        content_agents()
 
 
-def content_creation_agents():
+def content_agents():
-    st.markdown("<div class='sub-header'>AI Agents Team for Content Creation</div>", unsafe_allow_html=True)
+    st.markdown("AI Agents Team for Content Writing")
     content_keywords = st.text_input(
         "Enter Main Domain Keywords of your business:",
-        placeholder="Better keywords, Better content calendar:",
+        placeholder="Better keywords, Better content. Get keywords from Google search",
         help="These keywords define your main business sector, blogging niche, Industry, domain etc"
     )
 
@@ -103,27 +120,6 @@ def content_creation_agents():
             st.error("🚫 Single keywords are just too vague. Try again.")
 
 
-def content_planning_agents():
-    st.markdown("<div class='sub-header'>AI Agents Team for Content Ideation</div>", unsafe_allow_html=True)
-    content_keywords = st.text_input(
-        "Enter Main Domain Keywords of your business:",
-        placeholder="Better keywords will generate better content calendar:",
-        help="Enter at least two words for better results."
-    )
-
-    if st.button("Generate Content Plan"):
-        if content_keywords and len(content_keywords.split()) >= 2:
-            with st.spinner("Generating Content Plan..."):
-                try:
-                    plan_content = ai_agents_planner(content_keywords)
-                    st.success(f"Successfully generated content plan for: {content_keywords}")
-                    st.markdown(plan_content)
-                except Exception as err:
-                    st.error(f"Failed to generate content plan: {err}")
-        else:
-            st.error("🚫 Single keywords are just too vague. Try again.")
-
-
 
 def blog_from_audio():
     """
@@ -372,6 +368,52 @@ def ai_news_writer():
                 st.error(f"Failed to generate news report: {err}")
 
 
+
+def competitor_analysis():
+    st.title("Competitor Analysis")
+    st.markdown("""**Use Cases:**
+        - Know similar companies and alternatives for the given URL.
+        - Write listicles, similar companies, Top tools, alternative-to, similar products, similar websites, etc.
+        [Read More Here](https://docs.exa.ai/reference/company-analyst)
+    """)
+
+    similar_url = st.text_input("👋 Enter a single valid URL for web analysis:",
+                placeholder="Provide a competitor's URL and get details of similar/alternative companies.")
+
+    if st.button("Analyze"):
+        if similar_url:
+            try:
+                st.info(f"Starting analysis for the URL: {similar_url}")
+                with st.spinner("Performing competitor analysis..."):
+                    result = metaphor_find_similar(similar_url)
+                st.success("Analysis completed successfully!")
+                st.write(result)
+            except Exception as err:
+                st.error(f"✖ 🚫 Failed to do similar search.\nError: {err}")
+        else:
+            st.error("Please enter a valid URL.")
+
+
+def do_web_research():
+    """ Input keywords and do web research and present a report."""
+    st.title("Web Research Assistant")
+    st.write("Enter keywords for web research. The keywords should be at least three words long.")
+    
+    search_keywords = st.text_input("Search Keywords", placeholder="Enter keywords for web research...")
+    if st.button("Start Web Research"):
+        if search_keywords and len(search_keywords.split()) >= 3:
+            try:
+                st.info(f"Starting web research on given keywords: {search_keywords}")
+                with st.spinner("Performing web research..."):
+                    web_research_result = gpt_web_researcher(search_keywords)
+                st.success("Web research completed successfully!")
+                st.write(web_research_result)
+            except Exception as err:
+                st.error(f"ERROR: Failed to do web research: {err}")
+        else:
+            st.warning("Search keywords should be at least three words long. Please try again.")
+
+
 def ai_finance_ta_writer():
     st.markdown("<div class='sub-header'>AI Financial Technical Analysis Writer</div>", unsafe_allow_html=True)