Update pages/study_roadmap.py

pages/study_roadmap.py

@@ -1,481 +1,482 @@
-import streamlit as st
-import networkx as nx
-import plotly.graph_objects as go
-from dotenv import load_dotenv
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts import ChatPromptTemplate
-from langchain.output_parsers import PydanticOutputParser
-from pydantic import BaseModel, Field
-from typing import List, Dict, Optional
-import json
-import pandas as pd
-import time
-from datetime import datetime
-import random
-import re
-from PIL import Image
-import logging
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Load environment variables
-load_dotenv()
-
-AI71_BASE_URL = "https://api.ai71.ai/v1/"
-AI71_API_KEY = "api71-api-92fc2ef9-9f3c-47e5-a019-18e257b04af2"
-
-# Initialize the Falcon model
-chat = ChatOpenAI(
-    model="tiiuae/falcon-180B-chat",
-    api_key=AI71_API_KEY,
-    base_url=AI71_BASE_URL,
-    temperature=0.7,
-)
-
-class RoadmapStep(BaseModel):
-    title: str
-    description: str
-    resources: List[Dict[str, str]] = Field(default_factory=list)
-    estimated_time: str
-    how_to_use: Optional[str] = None
-
-class Roadmap(BaseModel):
-    steps: Dict[str, RoadmapStep] = Field(default_factory=dict)
-
-def clean_json(content):
-    # Remove any leading or trailing whitespace
-    content = content.strip()
-    
-    # Ensure the content starts and ends with curly braces
-    if not content.startswith('{'):
-        content = '{' + content
-    if not content.endswith('}'):
-        content = content + '}'
-    
-    # Remove any newline characters and extra spaces
-    content = ' '.join(content.split())
-    
-    # Escape any unescaped double quotes within string values
-    content = re.sub(r'(?<!\\)"(?=(?:(?:[^"]*"){2})*[^"]*$)', r'\"', content)
-    
-    return content
-
-def ensure_valid_json(content):
-    # First, apply our existing cleaning function
-    content = clean_json(content)
-    
-    # Use regex to find and fix unquoted property names
-    pattern = r'(\{|\,)\s*([a-zA-Z_][a-zA-Z0-9_]*)\s*:'
-    content = re.sub(pattern, r'\1 "\2":', content)
-    
-    # Replace single quotes with double quotes
-    content = content.replace("'", '"')
-    
-    # Attempt to parse the JSON to catch any remaining issues
-    try:
-        json_obj = json.loads(content)
-        return json.dumps(json_obj)  # Return a properly formatted JSON string
-    except json.JSONDecodeError as e:
-        # If we still can't parse it, log the error and return None
-        logger.error(f"Failed to parse JSON after cleaning: {str(e)}")
-        logger.debug(f"Problematic JSON: {content}")
-        return None
-
-def generate_roadmap(topic):
-    levels = [
-        "knowledge",
-        "comprehension",
-        "application",
-        "analysis",
-        "synthesis",
-        "evaluation"
-    ]
-
-    roadmap = Roadmap()
-
-    for level in levels:
-        try:
-            logger.info(f"Generating roadmap step for topic: {topic} at {level} level")
-            step = generate_simplified_step(topic, level, chat)
-            roadmap.steps[level] = step
-            logger.info(f"Added step for {level} level")
-            
-        except Exception as e:
-            logger.error(f"Error in generate_roadmap for {level}: {str(e)}")
-            step = create_fallback_step(topic, level, chat)
-            roadmap.steps[level] = step
-
-    logger.info("Roadmap generation complete")
-    return roadmap
-
-def generate_diverse_resources(topic, level):
-    encoded_topic = topic.replace(' ', '+')
-    encoded_level = level.replace(' ', '+')
-    
-    resource_templates = [
-        {"title": "Wikipedia", "url": f"https://en.wikipedia.org/wiki/{topic.replace(' ', '_')}"},
-        {"title": "YouTube Overview", "url": f"https://www.youtube.com/results?search_query={encoded_topic}+{encoded_level}"},
-        {"title": "Coursera Courses", "url": f"https://www.coursera.org/search?query={encoded_topic}"},
-        {"title": "edX Courses", "url": f"https://www.edx.org/search?q={encoded_topic}"},
-        {"title": "Brilliant", "url": f"https://brilliant.org/search/?q={encoded_topic}"},
-        {"title": "Google Scholar", "url": f"https://scholar.google.com/scholar?q={encoded_topic}"},
-        {"title": "MIT OpenCourseWare", "url": f"https://ocw.mit.edu/search/?q={encoded_topic}"},
-        {"title": "Khan Academy", "url": f"https://www.khanacademy.org/search?query={encoded_topic}"},
-        {"title": "TED Talks", "url": f"https://www.ted.com/search?q={encoded_topic}"},
-        {"title": "arXiv Papers", "url": f"https://arxiv.org/search/?query={encoded_topic}&searchtype=all"},
-        {"title": "ResearchGate", "url": f"https://www.researchgate.net/search/publication?q={encoded_topic}"},
-        {"title": "Academic Earth", "url": f"https://academicearth.org/search/?q={encoded_topic}"},
-    ]
-    
-    # Randomly select 5-7 resources
-    num_resources = random.randint(5, 7)
-    selected_resources = random.sample(resource_templates, num_resources)
-    
-    return selected_resources
-
-def create_fallback_step(topic, level, chat):
-    def generate_component(prompt, default_value):
-        try:
-            response = chat.invoke([{"role": "system", "content": prompt}])
-            return response.content.strip() or default_value
-        except Exception as e:
-            logger.error(f"Error generating component: {str(e)}")
-            return default_value
-
-    # Generate title
-    title_prompt = f"Create a concise title (max 10 words) for a study step about {topic} at the {level} level of Bloom's Taxonomy."
-    default_title = f"{level.capitalize()} Step for {topic}"
-    title = generate_component(title_prompt, default_title)
-
-    # Generate description
-    description_prompt = f"""Write a detailed description (500-700 words) for a study step about {topic} at the {level} level of Bloom's Taxonomy. 
-    Explain what this step entails, how the user should approach it, and why it's important for mastering the topic at this level. 
-    The description should be specific to {topic} and not a generic explanation of the Bloom's Taxonomy level."""
-    default_description = f"In this step, you will focus on {topic} at the {level} level. This involves understanding key concepts and theories related to {topic}. Engage with the provided resources to build a strong foundation."
-    description = generate_component(description_prompt, default_description)
-
-    # Generate estimated time
-    time_prompt = f"Estimate the time needed to complete a study step about {topic} at the {level} level of Bloom's Taxonomy. Provide the answer in a format like '3-4 days' or '1-2 weeks'."
-    default_time = "3-4 days"
-    estimated_time = generate_component(time_prompt, default_time)
-
-    # Generate how to use
-    how_to_use_prompt = f"""Write a paragraph (100-150 words) on how to effectively use the {level} level of Bloom's Taxonomy when studying {topic}. 
-    Include tips and strategies specific to {topic} at this {level} level."""
-    default_how_to_use = f"Explore the provided resources and take notes on key concepts related to {topic}. Practice explaining these concepts in your own words to reinforce your understanding at the {level} level."
-    how_to_use = generate_component(how_to_use_prompt, default_how_to_use)
-
-    return RoadmapStep(
-        title=title,
-        description=description,
-        resources=generate_diverse_resources(topic, level),
-        estimated_time=estimated_time,
-        how_to_use=how_to_use
-    )
-
-def create_interactive_graph(roadmap):
-    G = nx.DiGraph()
-    color_map = {
-        'Knowledge': '#FF6B6B',
-        'Comprehension': '#4ECDC4',
-        'Application': '#45B7D1',
-        'Analysis': '#FFA07A',
-        'Synthesis': '#98D8C8',
-        'Evaluation': '#F9D56E'
-    }
-    
-    for i, (level, step) in enumerate(roadmap.steps.items()):
-        G.add_node(step.title, level=level.capitalize(), pos=(i, -i))
-    
-    pos = nx.get_node_attributes(G, 'pos')
-    
-    edge_trace = go.Scatter(
-        x=[], y=[],
-        line=dict(width=2, color='#888'),
-        hoverinfo='none',
-        mode='lines')
-
-    node_trace = go.Scatter(
-        x=[], y=[],
-        mode='markers+text',
-        hoverinfo='text',
-        marker=dict(
-            showscale=False,
-            color=[],
-            size=30,
-            line_width=2
-        ),
-        text=[],
-        textposition="top center"
-    )
-
-    for node in G.nodes():
-        x, y = pos[node]
-        node_trace['x'] += (x,)
-        node_trace['y'] += (y,)
-        node_info = f"{node}<br>Level: {G.nodes[node]['level']}"
-        node_trace['text'] += (node_info,)
-        node_trace['marker']['color'] += (color_map.get(G.nodes[node]['level'], '#CCCCCC'),)
-
-    fig = go.Figure(data=[edge_trace, node_trace],
-                    layout=go.Layout(
-                        title='Interactive Study Roadmap',
-                        titlefont_size=16,
-                        showlegend=False,
-                        hovermode='closest',
-                        margin=dict(b=20,l=5,r=5,t=40),
-                        annotations=[dict(
-                            text="",
-                            showarrow=False,
-                            xref="paper", yref="paper",
-                            x=0.005, y=-0.002
-                        )],
-                        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
-                        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
-                        plot_bgcolor='rgba(0,0,0,0)',
-                        paper_bgcolor='rgba(0,0,0,0)'
-                    ))
-    
-    # Add a color legend
-    for level, color in color_map.items():
-        fig.add_trace(go.Scatter(
-            x=[None], y=[None],
-            mode='markers',
-            marker=dict(size=10, color=color),
-            showlegend=True,
-            name=level
-        ))
-    
-    fig.update_layout(legend=dict(
-        orientation="h",
-        yanchor="bottom",
-        y=1.02,
-        xanchor="right",
-        x=1
-    ))
-    
-    return fig
-
-def get_user_progress(roadmap):
-    if 'user_progress' not in st.session_state:
-        st.session_state.user_progress = {}
-    
-    for level, step in roadmap.steps.items():
-        if step.title not in st.session_state.user_progress:
-            st.session_state.user_progress[step.title] = 0
-    
-    return st.session_state.user_progress
-
-def update_user_progress(step_title, progress):
-    st.session_state.user_progress[step_title] = progress
-
-def calculate_overall_progress(progress_dict):
-    if not progress_dict:
-        return 0
-    total_steps = len(progress_dict)
-    completed_steps = sum(1 for progress in progress_dict.values() if progress == 100)
-    return (completed_steps / total_steps) * 100
-
-def generate_simplified_step(topic, level, chat):
-    prompt = f"""Create a detailed study step for the topic: {topic} at the {level} level of Bloom's Taxonomy.
-    
-    Provide:
-    1. A descriptive title (max 10 words)
-    2. A detailed description (500-700 words) explaining what this step entails, how the user should approach it, and why it's important for mastering the topic at this level. The description should be specific to {topic} and not a generic explanation of the Bloom's Taxonomy level.
-    3. Estimated time for completion (e.g., 3-4 days, 1-2 weeks, etc.)
-    4. A paragraph (100-150 words) on how to use this level effectively, including tips and strategies specific to {topic} at this {level} level
-
-    Format your response as a valid JSON object with the following structure:
-    {{
-        "title": "Step title",
-        "description": "Step description",
-        "estimated_time": "Estimated time",
-        "how_to_use": "Paragraph on how to use this level effectively"
-    }}
-    """
-    
-    try:
-        response = chat.invoke([{"role": "system", "content": prompt}])
-        valid_json = ensure_valid_json(response.content)
-        if valid_json is None:
-            raise ValueError("Failed to create valid JSON")
-        
-        step_dict = json.loads(valid_json)
-        
-        # Generate diverse resources
-        resources = generate_diverse_resources(topic, level)
-        
-        return RoadmapStep(
-            title=step_dict["title"],
-            description=step_dict["description"],
-            resources=resources,
-            estimated_time=step_dict["estimated_time"],
-            how_to_use=step_dict["how_to_use"]
-        )
-    except Exception as e:
-        logger.error(f"Error in generate_simplified_step for {level}: {str(e)}")
-        return create_fallback_step(topic, level, chat)
-
-
-
-def display_step(step, level, user_progress):
-    with st.expander(f"{level.capitalize()}: {step.title}"):
-        st.write(f"**Description:** {step.description}")
-        st.write(f"**Estimated Time:** {step.estimated_time}")
-        st.write("**Resources:**")
-        for resource in step.resources:
-            st.markdown(f"- [{resource['title']}]({resource['url']})")
-            if 'contribution' in resource:
-                st.write(f"  *{resource['contribution']}*")
-        
-        # Check if how_to_use exists before displaying it
-        if step.how_to_use:
-            st.write("**How to use this level effectively:**")
-            st.write(step.how_to_use)
-        
-        progress = st.slider(f"Progress for {step.title}", 0, 100, user_progress.get(step.title, 0), key=f"progress_{level}")
-        update_user_progress(step.title, progress)
-
-def main():
-    st.set_page_config(page_title="S.H.E.R.L.O.C.K. Study Roadmap Generator", layout="wide")
-
-    # Custom CSS for dark theme
-    st.markdown("""
-        <style>
-        .stApp {
-            background-color: #1E1E1E;
-            color: #FFFFFF;
-        }
-        .stButton>button {
-            background-color: #4CAF50;
-            color: white;
-            border-radius: 5px;
-        }
-        .stProgress > div > div > div > div {
-            background-color: #4CAF50;
-        }
-        .streamlit-expanderHeader {
-            background-color: #2E2E2E;
-            color: #FFFFFF;
-        }
-        .streamlit-expanderContent {
-            background-color: #2E2E2E;
-            color: #FFFFFF;
-        }
-        </style>
-    """, unsafe_allow_html=True)
-
-    st.title("🧠 S.H.E.R.L.O.C.K. Study Roadmap Generator")
-    st.write("Generate a comprehensive study roadmap based on first principles for any topic.")
-
-    # Sidebar
-    with st.sidebar:
-        st.image("https://placekitten.com/300/200", caption="S.H.E.R.L.O.C.K.", use_column_width=True)
-        st.markdown("""
-        ## About S.H.E.R.L.O.C.K.
-        **S**tudy **H**elper for **E**fficient **R**oadmaps and **L**earning **O**ptimization using **C**omprehensive **K**nowledge
-
-        S.H.E.R.L.O.C.K. is your AI-powered study companion, designed to create personalized learning roadmaps for any topic. It breaks down complex subjects into manageable steps, ensuring a comprehensive understanding from fundamentals to advanced concepts.
-        """)
-
-        st.subheader("📋 Todo List")
-        if 'todos' not in st.session_state:
-            st.session_state.todos = []
-        
-        new_todo = st.text_input("Add a new todo:")
-        if st.button("Add Todo", key="add_todo"):
-            if new_todo:
-                st.session_state.todos.append({"task": new_todo, "completed": False})
-                st.success("Todo added successfully!")
-            else:
-                st.warning("Please enter a todo item.")
-        
-        for i, todo in enumerate(st.session_state.todos):
-            col1, col2, col3 = st.columns([0.05, 0.8, 0.15])
-            with col1:
-                todo['completed'] = st.checkbox("", todo['completed'], key=f"todo_{i}")
-            with col2:
-                st.write(todo['task'], key=f"todo_text_{i}")
-            with col3:
-                if st.button("🗑️", key=f"delete_{i}", help="Delete todo"):
-                    st.session_state.todos.pop(i)
-                    st.experimental_rerun()
-
-        st.subheader("⏱️ Pomodoro Timer")
-        pomodoro_duration = st.slider("Pomodoro Duration (minutes)", 1, 60, 25)
-        if st.button("Start Pomodoro"):
-            progress_bar = st.progress(0)
-            for i in range(pomodoro_duration * 60):
-                time.sleep(1)
-                progress_bar.progress((i + 1) / (pomodoro_duration * 60))
-            st.success("Pomodoro completed!")
-            if 'achievements' not in st.session_state:
-                st.session_state.achievements = set()
-            st.session_state.achievements.add("Consistent Learner")
-
-    topic = st.text_input("📚 Enter the topic you want to master:")
-    
-    if st.button("🚀 Generate Roadmap"):
-        if topic:
-            with st.spinner("🧠 Generating your personalized study roadmap..."):
-                try:
-                    logger.info(f"Starting roadmap generation for topic: {topic}")
-                    roadmap = generate_roadmap(topic)
-                    if roadmap and roadmap.steps:
-                        logger.info("Roadmap generated successfully")
-                        st.session_state.current_roadmap = roadmap
-                        st.session_state.current_topic = topic
-                        st.success("Roadmap generated successfully!")
-                    else:
-                        logger.warning("Generated roadmap is empty or invalid")
-                        st.error("Failed to generate a valid roadmap. Please try again with a different topic.")
-                except Exception as e:
-                    logger.error(f"Error during roadmap generation: {str(e)}", exc_info=True)
-                    st.error(f"An error occurred while generating the roadmap: {str(e)}")
-    
-    if 'current_roadmap' in st.session_state:
-        st.subheader(f"📊 Study Roadmap for: {st.session_state.current_topic}")
-        
-        roadmap = st.session_state.current_roadmap
-        fig = create_interactive_graph(roadmap)
-        fig.update_layout(
-            plot_bgcolor='rgba(0,0,0,0)',
-            paper_bgcolor='rgba(0,0,0,0)',
-            font_color='#FFFFFF'
-        )
-        st.plotly_chart(fig, use_container_width=True)
-        
-        user_progress = get_user_progress(roadmap)
-        
-        levels_description = {
-            "knowledge": "Understanding and remembering basic facts and concepts",
-            "comprehension": "Grasping the meaning and interpreting information",
-            "application": "Using knowledge in new situations",
-            "analysis": "Breaking information into parts and examining relationships",
-            "synthesis": "Combining elements to form a new whole",
-            "evaluation": "Making judgments based on criteria and standards"
-        }
-        
-        for level, step in roadmap.steps.items():
-            st.header(f"{level.capitalize()} Level")
-            st.write(f"**Description:** {levels_description[level]}")
-            st.write("**How to master this level:**")
-            st.write(f"To master the {level} level, focus on {levels_description[level].lower()}. Engage with the resources provided, practice applying the concepts, and gradually build your understanding. Remember that mastery at this level is crucial before moving to the next.")
-            display_step(step, level, user_progress)
-        
-        overall_progress = calculate_overall_progress(user_progress)
-        st.progress(overall_progress / 100)
-        st.write(f"Overall progress: {overall_progress:.2f}%")
-        
-        roadmap_json = json.dumps(roadmap.dict(), indent=2)
-        st.download_button(
-            label="📥 Download Roadmap as JSON",
-            data=roadmap_json,
-            file_name="study_roadmap.json",
-            mime="application/json"
-        )
-
-if __name__ == "__main__":
+import streamlit as st
+import networkx as nx
+import plotly.graph_objects as go
+from dotenv import load_dotenv
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from langchain.output_parsers import PydanticOutputParser
+from pydantic import BaseModel, Field
+from typing import List, Dict, Optional
+import json
+import os
+import pandas as pd
+import time
+from datetime import datetime
+import random
+import re
+from PIL import Image
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Load environment variables
+load_dotenv()
+
+AI71_BASE_URL = "https://api.ai71.ai/v1/"
+AI71_API_KEY = "api71-api-92fc2ef9-9f3c-47e5-a019-18e257b04af2"
+
+# Initialize the Falcon model
+chat = ChatOpenAI(
+    model="tiiuae/falcon-180B-chat",
+    api_key=AI71_API_KEY,
+    base_url=AI71_BASE_URL,
+    temperature=0.7,
+)
+
+class RoadmapStep(BaseModel):
+    title: str
+    description: str
+    resources: List[Dict[str, str]] = Field(default_factory=list)
+    estimated_time: str
+    how_to_use: Optional[str] = None
+
+class Roadmap(BaseModel):
+    steps: Dict[str, RoadmapStep] = Field(default_factory=dict)
+
+def clean_json(content):
+    # Remove any leading or trailing whitespace
+    content = content.strip()
+    
+    # Ensure the content starts and ends with curly braces
+    if not content.startswith('{'):
+        content = '{' + content
+    if not content.endswith('}'):
+        content = content + '}'
+    
+    # Remove any newline characters and extra spaces
+    content = ' '.join(content.split())
+    
+    # Escape any unescaped double quotes within string values
+    content = re.sub(r'(?<!\\)"(?=(?:(?:[^"]*"){2})*[^"]*$)', r'\"', content)
+    
+    return content
+
+def ensure_valid_json(content):
+    # First, apply our existing cleaning function
+    content = clean_json(content)
+    
+    # Use regex to find and fix unquoted property names
+    pattern = r'(\{|\,)\s*([a-zA-Z_][a-zA-Z0-9_]*)\s*:'
+    content = re.sub(pattern, r'\1 "\2":', content)
+    
+    # Replace single quotes with double quotes
+    content = content.replace("'", '"')
+    
+    # Attempt to parse the JSON to catch any remaining issues
+    try:
+        json_obj = json.loads(content)
+        return json.dumps(json_obj)  # Return a properly formatted JSON string
+    except json.JSONDecodeError as e:
+        # If we still can't parse it, log the error and return None
+        logger.error(f"Failed to parse JSON after cleaning: {str(e)}")
+        logger.debug(f"Problematic JSON: {content}")
+        return None
+
+def generate_roadmap(topic):
+    levels = [
+        "knowledge",
+        "comprehension",
+        "application",
+        "analysis",
+        "synthesis",
+        "evaluation"
+    ]
+
+    roadmap = Roadmap()
+
+    for level in levels:
+        try:
+            logger.info(f"Generating roadmap step for topic: {topic} at {level} level")
+            step = generate_simplified_step(topic, level, chat)
+            roadmap.steps[level] = step
+            logger.info(f"Added step for {level} level")
+            
+        except Exception as e:
+            logger.error(f"Error in generate_roadmap for {level}: {str(e)}")
+            step = create_fallback_step(topic, level, chat)
+            roadmap.steps[level] = step
+
+    logger.info("Roadmap generation complete")
+    return roadmap
+
+def generate_diverse_resources(topic, level):
+    encoded_topic = topic.replace(' ', '+')
+    encoded_level = level.replace(' ', '+')
+    
+    resource_templates = [
+        {"title": "Wikipedia", "url": f"https://en.wikipedia.org/wiki/{topic.replace(' ', '_')}"},
+        {"title": "YouTube Overview", "url": f"https://www.youtube.com/results?search_query={encoded_topic}+{encoded_level}"},
+        {"title": "Coursera Courses", "url": f"https://www.coursera.org/search?query={encoded_topic}"},
+        {"title": "edX Courses", "url": f"https://www.edx.org/search?q={encoded_topic}"},
+        {"title": "Brilliant", "url": f"https://brilliant.org/search/?q={encoded_topic}"},
+        {"title": "Google Scholar", "url": f"https://scholar.google.com/scholar?q={encoded_topic}"},
+        {"title": "MIT OpenCourseWare", "url": f"https://ocw.mit.edu/search/?q={encoded_topic}"},
+        {"title": "Khan Academy", "url": f"https://www.khanacademy.org/search?query={encoded_topic}"},
+        {"title": "TED Talks", "url": f"https://www.ted.com/search?q={encoded_topic}"},
+        {"title": "arXiv Papers", "url": f"https://arxiv.org/search/?query={encoded_topic}&searchtype=all"},
+        {"title": "ResearchGate", "url": f"https://www.researchgate.net/search/publication?q={encoded_topic}"},
+        {"title": "Academic Earth", "url": f"https://academicearth.org/search/?q={encoded_topic}"},
+    ]
+    
+    # Randomly select 5-7 resources
+    num_resources = random.randint(5, 7)
+    selected_resources = random.sample(resource_templates, num_resources)
+    
+    return selected_resources
+
+def create_fallback_step(topic, level, chat):
+    def generate_component(prompt, default_value):
+        try:
+            response = chat.invoke([{"role": "system", "content": prompt}])
+            return response.content.strip() or default_value
+        except Exception as e:
+            logger.error(f"Error generating component: {str(e)}")
+            return default_value
+
+    # Generate title
+    title_prompt = f"Create a concise title (max 10 words) for a study step about {topic} at the {level} level of Bloom's Taxonomy."
+    default_title = f"{level.capitalize()} Step for {topic}"
+    title = generate_component(title_prompt, default_title)
+
+    # Generate description
+    description_prompt = f"""Write a detailed description (500-700 words) for a study step about {topic} at the {level} level of Bloom's Taxonomy. 
+    Explain what this step entails, how the user should approach it, and why it's important for mastering the topic at this level. 
+    The description should be specific to {topic} and not a generic explanation of the Bloom's Taxonomy level."""
+    default_description = f"In this step, you will focus on {topic} at the {level} level. This involves understanding key concepts and theories related to {topic}. Engage with the provided resources to build a strong foundation."
+    description = generate_component(description_prompt, default_description)
+
+    # Generate estimated time
+    time_prompt = f"Estimate the time needed to complete a study step about {topic} at the {level} level of Bloom's Taxonomy. Provide the answer in a format like '3-4 days' or '1-2 weeks'."
+    default_time = "3-4 days"
+    estimated_time = generate_component(time_prompt, default_time)
+
+    # Generate how to use
+    how_to_use_prompt = f"""Write a paragraph (100-150 words) on how to effectively use the {level} level of Bloom's Taxonomy when studying {topic}. 
+    Include tips and strategies specific to {topic} at this {level} level."""
+    default_how_to_use = f"Explore the provided resources and take notes on key concepts related to {topic}. Practice explaining these concepts in your own words to reinforce your understanding at the {level} level."
+    how_to_use = generate_component(how_to_use_prompt, default_how_to_use)
+
+    return RoadmapStep(
+        title=title,
+        description=description,
+        resources=generate_diverse_resources(topic, level),
+        estimated_time=estimated_time,
+        how_to_use=how_to_use
+    )
+
+def create_interactive_graph(roadmap):
+    G = nx.DiGraph()
+    color_map = {
+        'Knowledge': '#FF6B6B',
+        'Comprehension': '#4ECDC4',
+        'Application': '#45B7D1',
+        'Analysis': '#FFA07A',
+        'Synthesis': '#98D8C8',
+        'Evaluation': '#F9D56E'
+    }
+    
+    for i, (level, step) in enumerate(roadmap.steps.items()):
+        G.add_node(step.title, level=level.capitalize(), pos=(i, -i))
+    
+    pos = nx.get_node_attributes(G, 'pos')
+    
+    edge_trace = go.Scatter(
+        x=[], y=[],
+        line=dict(width=2, color='#888'),
+        hoverinfo='none',
+        mode='lines')
+
+    node_trace = go.Scatter(
+        x=[], y=[],
+        mode='markers+text',
+        hoverinfo='text',
+        marker=dict(
+            showscale=False,
+            color=[],
+            size=30,
+            line_width=2
+        ),
+        text=[],
+        textposition="top center"
+    )
+
+    for node in G.nodes():
+        x, y = pos[node]
+        node_trace['x'] += (x,)
+        node_trace['y'] += (y,)
+        node_info = f"{node}<br>Level: {G.nodes[node]['level']}"
+        node_trace['text'] += (node_info,)
+        node_trace['marker']['color'] += (color_map.get(G.nodes[node]['level'], '#CCCCCC'),)
+
+    fig = go.Figure(data=[edge_trace, node_trace],
+                    layout=go.Layout(
+                        title='Interactive Study Roadmap',
+                        titlefont_size=16,
+                        showlegend=False,
+                        hovermode='closest',
+                        margin=dict(b=20,l=5,r=5,t=40),
+                        annotations=[dict(
+                            text="",
+                            showarrow=False,
+                            xref="paper", yref="paper",
+                            x=0.005, y=-0.002
+                        )],
+                        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+                        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+                        plot_bgcolor='rgba(0,0,0,0)',
+                        paper_bgcolor='rgba(0,0,0,0)'
+                    ))
+    
+    # Add a color legend
+    for level, color in color_map.items():
+        fig.add_trace(go.Scatter(
+            x=[None], y=[None],
+            mode='markers',
+            marker=dict(size=10, color=color),
+            showlegend=True,
+            name=level
+        ))
+    
+    fig.update_layout(legend=dict(
+        orientation="h",
+        yanchor="bottom",
+        y=1.02,
+        xanchor="right",
+        x=1
+    ))
+    
+    return fig
+
+def get_user_progress(roadmap):
+    if 'user_progress' not in st.session_state:
+        st.session_state.user_progress = {}
+    
+    for level, step in roadmap.steps.items():
+        if step.title not in st.session_state.user_progress:
+            st.session_state.user_progress[step.title] = 0
+    
+    return st.session_state.user_progress
+
+def update_user_progress(step_title, progress):
+    st.session_state.user_progress[step_title] = progress
+
+def calculate_overall_progress(progress_dict):
+    if not progress_dict:
+        return 0
+    total_steps = len(progress_dict)
+    completed_steps = sum(1 for progress in progress_dict.values() if progress == 100)
+    return (completed_steps / total_steps) * 100
+
+def generate_simplified_step(topic, level, chat):
+    prompt = f"""Create a detailed study step for the topic: {topic} at the {level} level of Bloom's Taxonomy.
+    
+    Provide:
+    1. A descriptive title (max 10 words)
+    2. A detailed description (500-700 words) explaining what this step entails, how the user should approach it, and why it's important for mastering the topic at this level. The description should be specific to {topic} and not a generic explanation of the Bloom's Taxonomy level.
+    3. Estimated time for completion (e.g., 3-4 days, 1-2 weeks, etc.)
+    4. A paragraph (100-150 words) on how to use this level effectively, including tips and strategies specific to {topic} at this {level} level
+
+    Format your response as a valid JSON object with the following structure:
+    {{
+        "title": "Step title",
+        "description": "Step description",
+        "estimated_time": "Estimated time",
+        "how_to_use": "Paragraph on how to use this level effectively"
+    }}
+    """
+    
+    try:
+        response = chat.invoke([{"role": "system", "content": prompt}])
+        valid_json = ensure_valid_json(response.content)
+        if valid_json is None:
+            raise ValueError("Failed to create valid JSON")
+        
+        step_dict = json.loads(valid_json)
+        
+        # Generate diverse resources
+        resources = generate_diverse_resources(topic, level)
+        
+        return RoadmapStep(
+            title=step_dict["title"],
+            description=step_dict["description"],
+            resources=resources,
+            estimated_time=step_dict["estimated_time"],
+            how_to_use=step_dict["how_to_use"]
+        )
+    except Exception as e:
+        logger.error(f"Error in generate_simplified_step for {level}: {str(e)}")
+        return create_fallback_step(topic, level, chat)
+
+
+
+def display_step(step, level, user_progress):
+    with st.expander(f"{level.capitalize()}: {step.title}"):
+        st.write(f"**Description:** {step.description}")
+        st.write(f"**Estimated Time:** {step.estimated_time}")
+        st.write("**Resources:**")
+        for resource in step.resources:
+            st.markdown(f"- [{resource['title']}]({resource['url']})")
+            if 'contribution' in resource:
+                st.write(f"  *{resource['contribution']}*")
+        
+        # Check if how_to_use exists before displaying it
+        if step.how_to_use:
+            st.write("**How to use this level effectively:**")
+            st.write(step.how_to_use)
+        
+        progress = st.slider(f"Progress for {step.title}", 0, 100, user_progress.get(step.title, 0), key=f"progress_{level}")
+        update_user_progress(step.title, progress)
+
+def main():
+    st.set_page_config(page_title="S.H.E.R.L.O.C.K. Study Roadmap Generator", layout="wide")
+
+    # Custom CSS for dark theme
+    st.markdown("""
+        <style>
+        .stApp {
+            background-color: #1E1E1E;
+            color: #FFFFFF;
+        }
+        .stButton>button {
+            background-color: #4CAF50;
+            color: white;
+            border-radius: 5px;
+        }
+        .stProgress > div > div > div > div {
+            background-color: #4CAF50;
+        }
+        .streamlit-expanderHeader {
+            background-color: #2E2E2E;
+            color: #FFFFFF;
+        }
+        .streamlit-expanderContent {
+            background-color: #2E2E2E;
+            color: #FFFFFF;
+        }
+        </style>
+    """, unsafe_allow_html=True)
+
+    st.title("🧠 S.H.E.R.L.O.C.K. Study Roadmap Generator")
+    st.write("Generate a comprehensive study roadmap based on first principles for any topic.")
+
+    # Sidebar
+    with st.sidebar:
+        st.image("https://placekitten.com/300/200", caption="S.H.E.R.L.O.C.K.", use_column_width=True)
+        st.markdown("""
+        ## About S.H.E.R.L.O.C.K.
+        **S**tudy **H**elper for **E**fficient **R**oadmaps and **L**earning **O**ptimization using **C**omprehensive **K**nowledge
+
+        S.H.E.R.L.O.C.K. is your AI-powered study companion, designed to create personalized learning roadmaps for any topic. It breaks down complex subjects into manageable steps, ensuring a comprehensive understanding from fundamentals to advanced concepts.
+        """)
+
+        st.subheader("📋 Todo List")
+        if 'todos' not in st.session_state:
+            st.session_state.todos = []
+        
+        new_todo = st.text_input("Add a new todo:")
+        if st.button("Add Todo", key="add_todo"):
+            if new_todo:
+                st.session_state.todos.append({"task": new_todo, "completed": False})
+                st.success("Todo added successfully!")
+            else:
+                st.warning("Please enter a todo item.")
+        
+        for i, todo in enumerate(st.session_state.todos):
+            col1, col2, col3 = st.columns([0.05, 0.8, 0.15])
+            with col1:
+                todo['completed'] = st.checkbox("", todo['completed'], key=f"todo_{i}")
+            with col2:
+                st.write(todo['task'], key=f"todo_text_{i}")
+            with col3:
+                if st.button("🗑️", key=f"delete_{i}", help="Delete todo"):
+                    st.session_state.todos.pop(i)
+                    st.experimental_rerun()
+
+        st.subheader("⏱️ Pomodoro Timer")
+        pomodoro_duration = st.slider("Pomodoro Duration (minutes)", 1, 60, 25)
+        if st.button("Start Pomodoro"):
+            progress_bar = st.progress(0)
+            for i in range(pomodoro_duration * 60):
+                time.sleep(1)
+                progress_bar.progress((i + 1) / (pomodoro_duration * 60))
+            st.success("Pomodoro completed!")
+            if 'achievements' not in st.session_state:
+                st.session_state.achievements = set()
+            st.session_state.achievements.add("Consistent Learner")
+
+    topic = st.text_input("📚 Enter the topic you want to master:")
+    
+    if st.button("🚀 Generate Roadmap"):
+        if topic:
+            with st.spinner("🧠 Generating your personalized study roadmap..."):
+                try:
+                    logger.info(f"Starting roadmap generation for topic: {topic}")
+                    roadmap = generate_roadmap(topic)
+                    if roadmap and roadmap.steps:
+                        logger.info("Roadmap generated successfully")
+                        st.session_state.current_roadmap = roadmap
+                        st.session_state.current_topic = topic
+                        st.success("Roadmap generated successfully!")
+                    else:
+                        logger.warning("Generated roadmap is empty or invalid")
+                        st.error("Failed to generate a valid roadmap. Please try again with a different topic.")
+                except Exception as e:
+                    logger.error(f"Error during roadmap generation: {str(e)}", exc_info=True)
+                    st.error(f"An error occurred while generating the roadmap: {str(e)}")
+    
+    if 'current_roadmap' in st.session_state:
+        st.subheader(f"📊 Study Roadmap for: {st.session_state.current_topic}")
+        
+        roadmap = st.session_state.current_roadmap
+        fig = create_interactive_graph(roadmap)
+        fig.update_layout(
+            plot_bgcolor='rgba(0,0,0,0)',
+            paper_bgcolor='rgba(0,0,0,0)',
+            font_color='#FFFFFF'
+        )
+        st.plotly_chart(fig, use_container_width=True)
+        
+        user_progress = get_user_progress(roadmap)
+        
+        levels_description = {
+            "knowledge": "Understanding and remembering basic facts and concepts",
+            "comprehension": "Grasping the meaning and interpreting information",
+            "application": "Using knowledge in new situations",
+            "analysis": "Breaking information into parts and examining relationships",
+            "synthesis": "Combining elements to form a new whole",
+            "evaluation": "Making judgments based on criteria and standards"
+        }
+        
+        for level, step in roadmap.steps.items():
+            st.header(f"{level.capitalize()} Level")
+            st.write(f"**Description:** {levels_description[level]}")
+            st.write("**How to master this level:**")
+            st.write(f"To master the {level} level, focus on {levels_description[level].lower()}. Engage with the resources provided, practice applying the concepts, and gradually build your understanding. Remember that mastery at this level is crucial before moving to the next.")
+            display_step(step, level, user_progress)
+        
+        overall_progress = calculate_overall_progress(user_progress)
+        st.progress(overall_progress / 100)
+        st.write(f"Overall progress: {overall_progress:.2f}%")
+        
+        roadmap_json = json.dumps(roadmap.dict(), indent=2)
+        st.download_button(
+            label="📥 Download Roadmap as JSON",
+            data=roadmap_json,
+            file_name="study_roadmap.json",
+            mime="application/json"
+        )
+
+if __name__ == "__main__":
     main()