removed extra cards

pages.py

@@ -1,35 +0,0 @@
-# sidebar.py
-
-import streamlit as st
-import streamlit_router as sr
-
-def common_sidebar(router):
-    st.sidebar.markdown("# Sidebar Navigation")
-    st.sidebar.markdown("---")
-    st.sidebar.markdown("### Pages")
-    if st.sidebar.button("Home"):
-        router.redirect(*router.build('/'))
-    if st.sidebar.button("TIL Feedback"):
-        router.redirect(*router.build('/feedback'))
-    if st.sidebar.button("Course Suggester"):
-        router.redirect(*router.build('/course_suggester'))
-    if st.sidebar.button("Course Lesson Extractor"):
-        router.redirect(*router.build('/lessons_extractor'))
-    if st.sidebar.button("Article Recommender"):
-        router.redirect(*router.build('/article_recommendor'))
-    if st.sidebar.button("Recent Article Suggester"):
-        router.redirect(*router.build('/research_article_suggester'))
-
-def with_sidebar(func):
-    def wrapper(*args, **kwargs):
-        from ui_main import router  # Import router from the main file
-        current_route = st.query_params.get("route", [""])[0]
-        
-        if current_route in ['feedback', 'course_suggester', 'lessons_extractor', 'article_recommendor', 'research_article_suggester']:
-            common_sidebar(router)
-        
-        return func(*args, **kwargs)
-    return wrapper
-
-
-

ui/article_recommendation.py

@@ -1,108 +0,0 @@
-from dotenv import load_dotenv
-import json
-import streamlit as st
-import utils.settings as settings
-from workflows.article_suggestion import article_recommendation_crew
-from utils.write_to_json import write_dict_to_json as write_dict_to_json
-load_dotenv()
-settings.init()
-
-
-def icon(emoji: str):
-    """Shows an emoji as a Notion-style page icon."""
-    st.write(
-        f'<span style="font-size: 78px; line-height: 1">{emoji}</span>',
-        unsafe_allow_html=True,
-    )
-
-
-def article_recommendor_main():
-    page_bg_img = '''
-        <style>
-        [data-testid="stAppViewContainer"]{
-            background-image:url("https://www.shutterstock.com/image-vector/abstract-technology-communication-concept-vector-600nw-1914443275.jpg");
-            background-size:cover;
-        }
-        [data-testid="stHeader"]{
-            background: rgba(0,0,0,0);
-        }
-        [data-testid="stToolbar"]{
-            right: 2rem;
-        }
-       
-
-        </style>
-        '''
-    st.markdown(page_bg_img, unsafe_allow_html=True)
-    icon("📖 Articles RecommendAIgent")
-    st.subheader("Let AI agents recommend articles based on your interest!")
-
-    with st.sidebar:
-        st.header("👇 Provide Your Interests Below!")
-        with st.form("user_input_form", border=True):
-            interests = st.text_input(
-                "Enter your interests (comma-separated):",
-                "GenAI, Architecture, Agentic Programming",
-            )
-            previous_article_insights = st.text_area(
-                "Enter previous article insights:",
-                "Agentic Design Patterns (https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/)\n"
-                "Reflection: The LLM examines its own work to come up with ways to improve it. "
-                "Tool Use: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data. "
-                "Planning: The LLM comes up with, and executes, a multistep plan to achieve a goal "
-                "Multi-agent collaboration: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would.\n\n"
-                "GenAI Multi-Agent Systems (https://thenewstack.io/genai-multi-agent-systems-a-secret-weapon-for-tech-teams/)\n"
-                "Multi-agent systems go beyond the task-oriented roles to truly super-charge development and strategy teams. "
-                "Successful multi-agent systems act as a “digital twin” for your development team. "
-                "Different Approaches: 1. Centralized, with one agent in the center that collects and assimilates all the other outputs. "
-                "2. Distributed, where there is no central controller and the agents coordinate directly with one another in an “agent swarm. "
-                "3. Hierarchical, where agents are organized in teams or hierarchical layers.\n\n"
-                "LLM Model Quantisation\n"
-                "Different Methods for Compression: Pruning, Knowledge Distiallation and Quantization."
-                "Quantization process represents the model weights in lower precession which is also known as downcasting."
-                "Quanitzatoin Error is the difference in the weights of the quantized model and the original model."
-                "Advantages of Quanitzation: Reduced memory footprint, increased compute and speed of inferrence."
-                "Disadvantages of Quantization: Less precise.\n\n",
-                height=400,
-            )
-            st.markdown("")
-            submitted = st.form_submit_button("Submit")
-
-    if submitted:
-        with st.status(
-            "🤖 **Agents at work...**", state="running", expanded=True
-        ) as status:
-            with st.container(height=500, border=False):
-                result = article_recommendation_crew.kickoff(
-                    inputs={
-                        "interests": interests,
-                        "previous_article_insights": previous_article_insights,
-                    }
-                )
-            status.update(
-                label="✅ Articles are Ready for Reading!",
-                state="complete",
-                expanded=False,
-            )
-
-        st.subheader("", anchor=False, divider="rainbow")
-
-        articles_list = settings.articles.values()
-
-        if articles_list is None:
-            st.markdown("No articles found.")
-            return
-        else:
-            for article in articles_list:
-                st.markdown(f"# {article['title']}")
-                st.markdown(f"**URL:** [{article['url']}]({article['url']})")
-                st.markdown(f"**Pitch:** {article.get('pitch', '')}")
-                st.markdown(f"**Evaluation Score:** {article.get('evaluation_score', '')}")
-                st.markdown(f"**Evaluation Reason:** {article.get('evaluation_reason', '')}")
-                st.markdown(f"**Reason For Recommendation:** {article.get('reason_for_recommendation', '')}")
-                st.markdown("---")
-
-    if st.sidebar.button("← Back to Main Page"):
-        st.session_state.page = "main"
-if __name__ == "__main__":
-    article_recommendor_main()

ui/course_lessons_extractor.py

@@ -1,74 +0,0 @@
-import streamlit as st
-import asyncio
-from contextlib import contextmanager
-from dotenv import load_dotenv
-from workflows.courses.lessons_extractor import LessonsExtractor
-from streamlit_extras.capture import stdout
-
-load_dotenv()
-
-
-@contextmanager
-def setup_event_loop():
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    try:
-        yield loop
-    finally:
-        loop.close()
-        asyncio.set_event_loop(None)
-def lessons_extractor_main():
-    page_bg_img = '''
-        <style>
-        [data-testid="stAppViewContainer"]{
-            background-image:url("https://www.shutterstock.com/image-vector/abstract-technology-communication-concept-vector-600nw-1914443275.jpg");
-            background-size:cover;
-        }
-        [data-testid="stHeader"]{
-            background: rgba(0,0,0,0);
-        }
-        [data-testid="stToolbar"]{
-            right: 2rem;
-        }
-       
-
-        </style>
-        '''
-    st.markdown(page_bg_img, unsafe_allow_html=True)
-    st.markdown("<div class='container'>", unsafe_allow_html=True)
-
-    st.markdown(
-        """
-        <div class="centered">
-            <p class="title">Course Lesson Extractor</p>
-        </div>
-        """,
-        unsafe_allow_html=True
-    )
-    course_url = st.text_area('Enter the URL for the course:',
-                              "https://www.coursera.org/learn/google-data-analytics-capstone?specialization=google-data-analytics",
-                               key='course_url', help='Enter course you want to learn')
-
-    if st.button("Get Lessons"):
-        with st.status(
-            "🤖 **Extracting Lessons...**", state="running", expanded=True
-        ) as status:
-            with st.container(height=500, border=False):
-                log_container = st.empty()
-                with stdout(log_container.code, terminator=""):
-                    with setup_event_loop() as loop:
-                        extractor = LessonsExtractor()
-                        inputs = {"course_url": course_url}
-                        results = extractor.kickoff(inputs=inputs)["lessons"]
-            status.update(
-                label="✅ Extracted Lessons!",
-                state="complete",
-                expanded=False,
-            )
-
-        for idx, lesson in enumerate(results):
-            st.markdown(f"#### Lessons {idx}: {lesson['name']}")
-            st.markdown(f"Concpets: {', '.join(lesson['concepts'])}")
-
-if __name__ == "__main__":
-    lessons_extractor_main()

ui/research_paper.py

@@ -1,144 +0,0 @@
-import streamlit as st
-from workflows.research_article_suggester import RecentArticleSuggester
-from streamlit_extras.capture import stdout
-
-
-
-
-
-def research_article_suggester_main():
-
-    st.markdown(
-        """
-        <style>
-        # .main {
-        #     background-color: #f5f5f5;
-        #     padding: 20px;
-        #     border-radius: 10px;
-        # }
-        .centered {
-            display: flex;
-            flex-direction: column;
-            align-items: center;
-            justify-content: center;
-            text-align: center;
-        }
-        .title {
-            font-family: 'Helvetica', sans-serif;
-            font-weight: bold;
-            font-size: 36px;
-            color: #1f77b4;
-        }
-        .description {
-            font-family: 'Helvetica', sans-serif;
-            font-size: 18px;
-            color: #333333;
-            margin-top: 10px;
-        }
-        .subheader {
-            font-family: 'Helvetica', sans-serif;
-            font-weight: bold;
-            font-size: 24px;
-            color: #ff7f0e;
-            margin-top: 20px;
-        }
-        .element {
-            background-color: #ffffff;
-            padding: 1rem;
-            border-radius: 8px;
-            box-shadow: 0 0 5px rgba(0, 0, 0, 0.1);
-            margin-bottom: 1rem;
-        }
-        .element h3 {
-            font-size: 24px;
-            color: #1f77b4;
-            margin-bottom: 0.5rem;
-            text-transform: uppercase;
-            padding :10px;
-
-        }
-        .element ul {
-            list-style-type: none;
-            padding: 0;
-            margin: 0;
-        }
-        .element li {
-            font-size: 16px;
-            color: #333333;
-            margin-bottom: 0.5rem;
-        }
-        .element li b {
-            font-size: 22px;
-        }
-        [data-testid="stAppViewContainer"]{
-            background-image:url("https://www.shutterstock.com/image-vector/abstract-technology-communication-concept-vector-600nw-1914443275.jpg");
-            background-size:cover;
-        }
-        [data-testid="stHeader"]{
-            background: rgba(0,0,0,0);
-        }
-        [data-testid="stToolbar"]{
-            right: 2rem;
-        }
-
-        </style>
-        """,
-        unsafe_allow_html=True
-    )
-
-    st.markdown("<div class='container'>", unsafe_allow_html=True)
-
-    st.markdown(
-        """
-        <div class="centered">
-            <p class="title">Recent Article Suggester</p>
-            <p class="description">Discover recent articles based on your topic of interest using advanced AI.</p>
-        </div>
-        """,
-        unsafe_allow_html=True
-    )
-
-    st.sidebar.markdown("<p class='sidebar-header'>Search for the papers you are interested in:</p>", unsafe_allow_html=True)
-    topic = st.sidebar.text_input('Enter a topic:', 'GenAI', key='topic_input', help='Enter a topic of interest')
-
-    if st.sidebar.button('Get Suggestions'):
-
-        with st.status(
-            "🤖 **Agents at work...**", state="running", expanded=True
-        ) as status:
-            with st.container(height=500, border=False):
-                log_container = st.empty()
-                with stdout(log_container.code, terminator=""):
-                    suggester = RecentArticleSuggester()
-                    inputs = {"topic": topic}
-                    results = suggester.kickoff(inputs=inputs)
-            status.update(
-                label="✅ Articles are Ready for Reading!",
-                state="complete",
-                expanded=False,
-            )
-
-        st.subheader("", anchor=False, divider="rainbow")
-
-        if results is None:
-            st.markdown('No articles found.', unsafe_allow_html=True)
-        else:
-
-            st.markdown('<p class="subheader">Results:</p>', unsafe_allow_html=True)
-
-            for element in results:
-                st.markdown(
-                    f"""
-                    <div class="element">
-                        <h3><a href="{element['url']}" target="_blank">{element['title']}</a></h3>
-                        <ul>
-                            {"".join(f"<li style='font-size: 20px;'><b>{key.capitalize()}:</b> {value}</li>" for key, value in element.items() if key != "title")}
-                        </ul>
-                    </div>
-                    """,
-                    unsafe_allow_html=True
-                )
-    if st.sidebar.button("← Back to Main Page"):
-        st.session_state.page = "main"
-if __name__ == "__main__":
-    research_aricle_suggester_main()

ui/til_feedback.py

@@ -5,11 +5,8 @@ from streamlit_extras.capture import stdout
 from langsmith import Client
 from workflows.utils.feedback import Feedback
 
-
-
 load_dotenv()
 
-
 def feedback_main():
 
     page_bg_img = '''

ui_main.py

@@ -1,11 +1,7 @@
 from dotenv import load_dotenv
 from streamlit_extras.stylable_container import stylable_container
-from ui.article_recommendation import article_recommendor_main
-from ui.course_lessons_extractor import lessons_extractor_main
-from ui.research_paper import research_article_suggester_main
 from ui.til_feedback import  feedback_main
 from ui.course_learn_suggest_expectations import course_suggester_main
-# from pages import card_main
 import math
 import streamlit as st
 import subprocess
@@ -28,9 +24,7 @@ def common_sidebar():
     st.sidebar.button("Home", on_click=lambda: router.redirect(*router.build("show_main_page")))
     st.sidebar.button("TIL Feedback", on_click=lambda: router.redirect(*router.build("feedback_main_wrapper")))
     st.sidebar.button("Course Suggester", on_click=lambda: router.redirect(*router.build("course_suggester_main_wrapper")))
-    st.sidebar.button("Course Lesson Extractor", on_click=lambda: router.redirect(*router.build("lessons_extractor_main_wrapper")))
-    st.sidebar.button("Article Recommender", on_click=lambda: router.redirect(*router.build("article_recommendor_main_wrapper")))
-    st.sidebar.button("Recent Article Suggester", on_click=lambda: router.redirect(*router.build("research_article_suggester_main_wrapper")))
+
 
 
 def show_main_page():
@@ -61,20 +55,10 @@ def show_main_page():
     st.markdown('<div class="main-title">Welcome to Growthy AI Workflows!</div>', unsafe_allow_html=True)
     st.markdown("---")
 
-    # card_info = [
-    #     {"title": "TIL Feedback", "description": "Provide your valuable feedback.", "key": "feedback_main_wrapper","image":"https://agent.ai/icons/search.svg"},
-    #     {"title": "Course Suggester", "description": "Get suggestions for courses", "key": "course_suggester_main_wrapper","image":"https://agent.ai/icons/prospecting.svg"},
-    #     {"title": "Course Lesson Extractor", "description": "Extract lessons for a given course", "key": "lessons_extractor_main_wrapper","image":"https://agent.ai/icons/business-analyst.svg"},
-    #     {"title": "Article Recommender", "description": "Discover articles tailored to your interests.", "key": "article_recommendor_main_wrapper","image":"https://agent.ai/icons/robot.svg"},
-    #     {"title": "Recent Article Suggester", "description": "Get suggestions for recent research articles.", "key": "research_article_suggester_main_wrapper","image":"https://agent.ai/icons/data.svg"},
-       
-    # ]
     card_info = [
     {"title": "TIL Feedback", "description": "Provide your valuable feedback.", "key": "feedback_main_wrapper", "image": "📝"},
     {"title": "Course Suggester", "description": "Get suggestions for courses", "key": "course_suggester_main_wrapper", "image": "📚"},
-    {"title": "Course Lesson Extractor", "description": "Extract lessons for a given course", "key": "lessons_extractor_main_wrapper", "image": "📑"},
-    {"title": "Article Recommender", "description": "Discover articles tailored to your interests.", "key": "article_recommendor_main_wrapper", "image": "📰"},
-    {"title": "Recent Article Suggester", "description": "Get suggestions for recent research articles.", "key": "research_article_suggester_main_wrapper", "image": "🔍"},
+
 ]
 
     num_cols = 3
@@ -110,7 +94,6 @@ def show_main_page():
                                             margin-bottom: 10px;
                                         }  """,
                                 ):
-                                    # with st.container():
                                     st.markdown(
                                         f"""
                                         <div style="
@@ -131,8 +114,6 @@ def show_main_page():
                                     )
                                     if st.button("Explore", key=card["key"]):
                                             router.redirect(*router.build(f"{card['key']}"))
-                                            # st.experimental_rerun()
-                                            # st.session_state.page = card["key"]
 
                                     st.markdown(
                                     """
@@ -153,7 +134,7 @@ def show_main_page():
                                     """, unsafe_allow_html=True
                                             )      
 
-# Wrapping each main page function to include the common sidebar
+
 def feedback_main_wrapper():
     common_sidebar()
     feedback_main()
@@ -162,25 +143,13 @@ def course_suggester_main_wrapper():
     common_sidebar()
     course_suggester_main()
 
-def lessons_extractor_main_wrapper():
-    common_sidebar()
-    lessons_extractor_main()
-
-def article_recommendor_main_wrapper():
-    common_sidebar()
-    article_recommendor_main()
 
-def research_article_suggester_main_wrapper():
-    common_sidebar()
-    research_article_suggester_main()
                    
 router = sr.StreamlitRouter()
 
 router.register(show_main_page, '/')
 router.register(feedback_main_wrapper, '/feedback', methods=['GET'])
 router.register(course_suggester_main_wrapper, '/course_suggester')
-router.register(lessons_extractor_main_wrapper, '/lessons_extractor')
-router.register(article_recommendor_main_wrapper, '/article_recommendor')
-router.register(research_article_suggester_main_wrapper, '/research_article_suggester')
+
 
 router.serve()