app.py

import json
import os
import copy
import sqlite3
import operator
import streamlit as st
from openai import OpenAI
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage
from typing import Annotated, List
from pydantic import BaseModel, Field
from typing_extensions import TypedDict, Literal
from langgraph.graph import StateGraph, START, END
from langgraph.constants import Send

# Page configuration
st.set_page_config(layout="wide", page_title="JEE Roadmap Planner")

# Initialize session state variables
if "data" not in st.session_state:
    st.session_state.data = None
if "data_old" not in st.session_state:
    st.session_state.data_old = None
if "report_data" not in st.session_state:
    st.session_state.report_data = None
if "incomplete_tasks" not in st.session_state:
    st.session_state.incomplete_tasks = None
if "incomplete_task_list" not in st.session_state:
    st.session_state.incomplete_task_list = None
if "final_report" not in st.session_state:
    st.session_state.final_report = None
if "shifted_roadmap" not in st.session_state:
    st.session_state.shifted_roadmap = None
if "available_dates" not in st.session_state:
    st.session_state.available_dates = []
if "updated_roadmap" not in st.session_state:
    st.session_state.updated_roadmap = None
if "max_optimizer_iterations" not in st.session_state:
    st.session_state.max_optimizer_iterations = 3  # Limit optimizer to 3 iterations

# Navigation sidebar setup
st.sidebar.title("JEE Roadmap Planner")
page = st.sidebar.radio("Navigation", ["Home", "Roadmap Manager", "Task Analysis","Roadmap Chatbot"])


# For roadmap chatbot
def get_chapters_and_subtopics():
    with open("full_roadmap.json", "r") as f:
        data = json.load(f)

    ch_subt = {
        "Physics": {},
        "Chemistry": {},
        "Maths": {}
    }

    for day in data["schedule"]:
        for subject in day['subjects']:
            sub = ch_subt[subject['name']]
            for task in subject['tasks']:
                sub[task['ChapterName']] = []

    for day in data["schedule"]:
        for subject in day['subjects']:
            sub = ch_subt[subject['name']]
            for task in subject['tasks']:
                sub[task['ChapterName']].append(task['subtopic'])

    return ch_subt

# Function to convert NL query to SQL
def generate_sql_from_nl(prompt):
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

    table_struct = """CREATE TABLE IF NOT EXISTS roadmap (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        day_num INTEGER,
                        date TEXT, -- [yyyy-mm-dd]
                        subject TEXT, -- [Physics, Chemistry or Maths]
                        chapter_name TEXT,
                        task_type TEXT, -- (Concept Understanding, Question Practice, Revision, Test)
                        time TEXT, -- formatted like '0.5 hour', '1 hour', '2 Hours', and so on
                        subtopic TEXT,
                    )"""
    
    ch_subt = get_chapters_and_subtopics()

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": f""""You are an helper who runs in the background of an AI agent, 
             which helps students for their JEE Preparation. Now your Job is to analyze the users prompt and 
             create an SQL query to extract the related Information from an sqlite3 database with the table 
             structure: {table_struct}. 

             Note: 
             - For the time column, the data is formatted like '0.5 hour', '1 hour', '2 hours' and 
               so on. So make sure to create queries that compare just the numbers within the text.
             - If the student mention about any chapters or subtopics, browse through this json file {ch_subt},
               find the one with the closest match to the users query and use only those exact names of Chapers 
               and Subtopics present in this file to create SQL the query.
             
             You will also make sure multiple times that you give an SQL
             Query that adheres to the given table structure, and you Output just the SQL query.
             Do not include anyting else like new line statements, ```sql or any other text. Your output
             is going to be directly fed into a Python script to extract the required information. So,
             please follow all the given Instructions.
             """},
            {"role": "user", "content": f"""Keeping the table structure in mind: {table_struct}, 
             Convert this prompt to an SQL query for the given table: {prompt}. Make sure your
             output is just the SQL query, which can directly be used to extract required content"""}
        ]
    )
    return response.choices[0].message.content.strip()

# Function to convert SQL output to natural language
def generate_nl_from_sql_output(prompt, query, data):
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": f"""You are an helpful AI chatbot working under the roadmap 
             section of an AI Agent, whose role is to aid students in their preparation for the JEE examination. 
             You are going to play a very crucial role of a Roadmap Assistant, who helps the student out with whatever query
             they have related to their roadmap, the data required to answer the users query is already extracted 
             from the Roadmap table of a SQLite3 database and given to you here {data}. Analyse the users query deeply and 
             reply to it with the relevant information from the given data in a supportive manner. If you get empty data
             as an input, deeply analyze the user's prompt and the sql query and give a suitable reply."""},
            {"role": "user", "content": f"""Answer to this users query using the data given to you, while keeping 
             your role in mind: {prompt}"""}
        ]
    )
    return response.choices[0].message.content.strip()

# Function to fetch data from SQLite
def fetch_data_from_sql(sql_query):
    conn = sqlite3.connect("jee_roadmap.db")
    cursor = conn.cursor()
    cursor.execute(sql_query)
    rows = cursor.fetchall()
    conn.close()
    return rows

# Main function for chatbot
def answer_user_query(prompt):
    initialize_roadmap_db()
    query = generate_sql_from_nl(prompt)
    st.write(query)
    rows = fetch_data_from_sql(query)
    st.write(rows)
    return generate_nl_from_sql_output(prompt, query, rows)
    
def initialize_roadmap_db():
    if not os.path.exists("jee_roadmap.db"):
        try:
            with open("full_roadmap.json") as f:
                roadmap_data = json.load(f)

            conn = sqlite3.connect("jee_roadmap.db")
            cursor = conn.cursor()

            cursor.execute("""
            CREATE TABLE IF NOT EXISTS roadmap (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                day_num INTEGER,
                date TEXT,
                subject TEXT,
                chapter_name TEXT,
                task_type TEXT,
                time TEXT,
                subtopic TEXT
            )
            """)

            for day in roadmap_data["schedule"]:
                date = day["date"]
                day_num = day["dayNumber"]
                for subj in day["subjects"]:
                    subject = subj["name"]
                    for task in subj["tasks"]:
                        cursor.execute("""
                            INSERT INTO roadmap (day_num, date, subject, chapter_name, task_type, time, subtopic)
                            VALUES (?, ?, ?, ?, ?, ?, ?)
                        """, (
                            day_num,
                            date,
                            subject,
                            task["ChapterName"],
                            task["type"],
                            task["time"],
                            task["subtopic"]
                        ))

            conn.commit()
            conn.close()
            print("✅ Database created and data inserted successfully.")
        except Exception as e:
            print(f"⚠️ Error initializing database: {e}")

# Function to load initial data
def load_initial_data():
    with st.spinner("Loading roadmap data..."):
        try:
            with open('fourdayRoadmap.json', 'r') as file:
                data = json.load(file)
                st.session_state.data = data
                st.session_state.data_old = copy.deepcopy(data)
                st.success("Data loaded successfully!")
                return True
        except Exception as e:
            st.error(f"Error loading data: {e}")
            return False

# Function to mark tasks as incomplete
def process_task_completion_data():
    with st.spinner("Processing task completion data..."):
        data = st.session_state.data
        for day in data["schedule"]:
            for subject in day["subjects"]:
                for task in subject["tasks"]:
                    task["task_completed"] = False
                    task["completion_timestamp"] = None
        st.session_state.data = data
        st.success("Task completion data processed!")

# Function to extract incomplete tasks
def extract_incomplete_tasks():
    with st.spinner("Extracting incomplete tasks..."):
        data = st.session_state.data
        previous_day = data["schedule"][0]
        
        incomplete_tasks = {
            "dayNumber": previous_day["dayNumber"],
            "date": previous_day["date"],
            "subjects": []
        }

        for subject in previous_day["subjects"]:
            incomplete_subject_tasks = [
                {
                    "ChapterName": task["ChapterName"],
                    "type": task["type"],
                    "subtopic": task["subtopic"],
                    "time": task["time"],
                    "task_completed": False,
                    "completion_timestamp": None
                }
                for task in subject["tasks"] if not task["task_completed"]
            ]

            if incomplete_subject_tasks:
                incomplete_tasks["subjects"].append({
                    "name": subject["name"],
                    "tasks": incomplete_subject_tasks
                })

        # Convert to JSON format
        incomplete_tasks_json = json.dumps(incomplete_tasks, indent=4)
        st.session_state.incomplete_tasks = incomplete_tasks
        
        # Generate a list of incomplete tasks for the agent
        incomplete_task_list = []
        for subject in incomplete_tasks["subjects"]:
            for task in subject["tasks"]:
                if not task["task_completed"]:
                    incomplete_task = {
                        "subject": subject["name"],
                        "ChapterName": task["ChapterName"],
                        "type": task["type"],
                        "subtopic": task["subtopic"],
                        "time": task["time"],
                        "task_completed": task["task_completed"],
                        "completion_timestamp": task["completion_timestamp"]
                    }
                    incomplete_task_list.append(incomplete_task)
        
        st.session_state.incomplete_task_list = incomplete_task_list
        st.success("Incomplete tasks extracted!")

def generate_sql_for_report(llm, prompt):
    table_struct = """
      CREATE TABLE IF NOT EXISTS roadmap (
          id INTEGER PRIMARY KEY AUTOINCREMENT,
          day_num INTEGER,
          date TEXT,
          subject TEXT,
          chapter_name TEXT,
          task_type TEXT,
          time TEXT,
          subtopic TEXT,
          task_completed BOOLEAN,
          completion_timestamp TEXT
      )
    """

    response = llm.invoke(
        [
        SystemMessage(content=f"""You are a helper who runs in the background of an AI agent,
        which helps students for their JEE Preparation. Now your job is to analyze the user's prompt and
        create an SQL query to extract the related Information from an sqlite3 database with the table
        structure: {table_struct}.

        Note: For the time column, the data is formatted like '0.5 hour', '1 hour', '2 hours' and
        so on, it tells the amount of time required to complete that specific task. So make sure
        to create queries that compare just the numbers within the text. For the task_type column,
        the data is either of these (Concept Understanding, Question Practice, Revision or Test)

        You will also make sure multiple times that you give an SQL
        Query that adheres to the given table structure, and you output just the SQL query.
        Do not include anything else like new line statements, ```sql or any other text. Your output
        is going to be directly fed into a Python script to extract the required information. So,
        please follow all the given instructions.
        Verify multiple times that the SQL query is error free for the SQLite3 format."""),
        HumanMessage(content=f"""Keeping the table structure in mind: {table_struct},
        Convert this prompt to an SQL query for the given table: {prompt}. Make sure your
        output is just the SQL query, which can directly be used to extract required content.""")
        ]
    )
    return response.content.strip()

def get_sql_data_for_report(sql_query):
    conn = sqlite3.connect("jee_full_roadmap.db")
    cursor = conn.cursor()

    results = []
    queries = [q.strip() for q in sql_query.strip().split(';') if q.strip()]

    for query in queries:
        cursor.execute(query)
        columns = [desc[0] for desc in cursor.description]
        rows = cursor.fetchall()
        results.append({
            "query": query,
            "columns": columns,
            "rows": rows
        })
    conn.close()

    return results

def create_db_for_report(roadmap_data):
    if not os.path.exists("jee_full_roadmap.db"):
        try:
            conn = sqlite3.connect("jee_full_roadmap.db")
            cursor = conn.cursor()

            cursor.execute("DROP TABLE IF EXISTS roadmap")
            cursor.execute("""
                CREATE TABLE roadmap (
                    id INTEGER PRIMARY KEY AUTOINCREMENT,
                    day_num INTEGER,
                    date TEXT,
                    subject TEXT,
                    chapter_name TEXT,
                    task_type TEXT,
                    time TEXT,
                    subtopic TEXT,
                    task_completed BOOLEAN,
                    completion_timestamp TEXT
                )
            """)

            for day in roadmap_data["schedule"]:
                date = day["date"]
                day_num = day["dayNumber"]
                for subj in day["subjects"]:
                    subject = subj["name"]
                    for task in subj["tasks"]:
                        cursor.execute("""
                            INSERT INTO roadmap (day_num, date, subject, chapter_name, task_type, time, subtopic, task_completed, completion_timestamp)
                            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
                        """, (
                            day_num,
                            date,
                            subject,
                            task["ChapterName"],
                            task["type"],
                            task["time"],
                            task["subtopic"],
                            task["task_completed"],
                            task["completion_timestamp"]
                        ))
            conn.commit()
            conn.close()
            print("✅ Database created and data inserted successfully.")
        except Exception as e:
            print(f"⚠️ Error initializing database: {e}")

# Function to generate report
llm = ChatOpenAI(model="gpt-4o-mini")
class Section(BaseModel):
    name: str = Field(
        description="Name for this section of the report.",
    )
    description: str = Field(
        description="Brief overview of the main topics and concepts to be covered in this section.",
    )
    data_requirements: str = Field(
        description="Description of the data needed from the roadmap database to write this section.",
    )

class Sections(BaseModel):
    sections: List[Section] = Field(
        description="Sections of the report.",
    )

planner = llm.with_structured_output(Sections)

class State(TypedDict):
    sections: list[Section]  # List of report sections
    completed_sections: Annotated[list, operator.add]  # All workers write to this key in parallel
    final_report: str  # Final report

# Combined helper-worker state
class ProcessorState(TypedDict):
    section: Section
    completed_sections: Annotated[list, operator.add]

def orchestrator(state: State):
    """Orchestrator that generates a plan for the report with data requirements"""

    schema = """CREATE TABLE IF NOT EXISTS roadmap (
                  id INTEGER PRIMARY KEY AUTOINCREMENT,
                  day_num INTEGER,
                  date TEXT, -- [yyyy-mm-dd]
                  subject TEXT, -- (Physics, Chemistry or Maths)
                  chapter_name TEXT,
                  task_type TEXT, -- (Concept Understanding, Question Practice, Revision, Test)
                  time TEXT, -- formatted like '0.5 hour', '1 hour', '2 Hours', and so on -- Tells the amount of time required to finish the task
                  subtopic TEXT,
                  task_completed BOOLEAN, -- 0/1 indicates task completion status
                  completion_timestamp TEXT
                )"""

    # Generate queries
    report_sections = planner.invoke(
        [
            SystemMessage(content=f"""You are responsible for creating a structured plan for a JEE preparation analysis report.

            Audience: The report is intended primarily for students, but must also be insightful to mentors and parents. 
            Keep the language motivational and supportive, with actionable insights backed by data.

            Report Format: The report will be composed of exactly 4 concise sections. Your job is to define these sections. Each section must include:
            - **Name**: A short, descriptive title
            - **Description**: What the section analyzes and how it helps the student
            - **Data Requirements**: A plain-English description of what fields and metrics are needed from the roadmap 
                database whose schema is given here: {schema}

            DO NOT invent new sections or formats. Use exactly the following four section templates and fill in the 
            descriptions and data requirements precisely.

            ---

            ### Study Time Analysis

            **Description**: Analyze how much total time the student planned to spend vs how much they actually completed, 
            across different subjects and task types. This will help the student understand where their time is really going.

            **Data Requirements**:
            - Fields: `subject`, `task_type`, `time`, `task_completed`
            - Metrics: 
              - Total planned time → SUM of all `time`
              - Total actual time → SUM of `time` where `task_completed = 1`
              - Grouped by both `subject` and `task_type`

            ---

            ### Task Completion Metrics

            **Description**: Measure the student’s consistency and follow-through by looking at completion rates across 
            subjects and task types.

            **Data Requirements**:
            - Fields: `subject`, `task_type`, `task_completed`
            - Metrics:
              - Total tasks → COUNT of all tasks
              - Completed tasks → COUNT of tasks where `task_completed = 1`
              - Completion percentage per subject and task type

            ---

            ### Study Balance Analysis

            **Description**: Evaluate how the student's study time is distributed across task types (e.g., Practice, Revision, Test) 
            within each subject. This highlights over- or under-emphasis on any category.

            **Data Requirements**:
            - Fields: `subject`, `task_type`, `time`
            - Metrics:
              - SUM of `time` for each (subject, task_type) pair where task_completed = 1
              - Relative distribution of time per subject to detect imbalance

            ---

            ### Strengths and Areas for Improvement

            **Description**:
            This section analyzes how the student's effort is distributed — not by estimating how long they spent, 
            but by combining how many tasks they completed and how much time those completed tasks represent. 
            This helps identify:
              - Subjects and task types where the student is showing strong commitment
              - Areas that may be neglected or inconsistently approached

            **Data Requirements**:
            - Fields: subject, task_type, task_completed, time
            - Metrics (filtered where task_completed = 1):
              - Total Number of completed tasks
              - Total amount of time spent
              - Grouped by subject and task_type
            ---

            Important Constraints:
            - You must include **all the mentioned fields** in the `data_requirements` — no assumptions
            - Use only **aggregate metrics** — no need for per-task or per-day analysis
            - Keep descriptions student-focused, clear, and motivational
            - Do not alter section names or invent new ones
            - Do not output anything outside the strict format above

            Your output will be passed into a structured data pipeline. Return only the filled-out section definitions as described above.
            """),
            HumanMessage(content="""Use the given table structure of the roadmap and decide all the sections of
            the report along with what should be in it and the clearly mention all the data thats required for it
            from the roadmap table"""),
        ]
    )

    return {"sections": report_sections.sections}

def processor(state: ProcessorState):
    """Combined helper and worker - gets data and writes section in one step"""

    section = state['section']

    # HELPER PART: Get data for this section
    sql_query = generate_sql_for_report(llm, section.data_requirements)
    rows = get_sql_data_for_report(sql_query)
    # WORKER PART: Write the section using the data
    section_result = llm.invoke(
        [
            SystemMessage(
                content=f"""Create a concise, data-driven JEE preparation report section that provides actionable insights for students,
                parents, and mentors.

                Requirements:
                1. Begin directly with key metrics and insights - no introductory preamble
                2. Use specific numbers, percentages, and ratios to quantify performance
                3. Include concise tables or bullet points for clarity where appropriate
                4. Highlight patterns related to:
                   - Task completion rates
                   - Time allocation efficiency
                   - Subject/topic focus distribution
                   - Study consistency patterns
                5. For each observation, provide a brief actionable recommendation focused on student improvement.
                6. Use professional but motivational tone appropriate for academic context
                7. Strictly use Markdown for formatting all the tables and the numbers
                8. Strictly keep each section very focused and write it under 0 to 50 words
                9. Verify the formatting of all the tables multiple times to ensure the markdown is correct.
                10. Check all the numbers and calculations made by you multiple times to ensure accuracy

                Base all analysis strictly on the provided data - avoid assumptions beyond what's explicitly given to you.
                Don't assume anything else, even a little bit.

                *Important*
                If you receive an empty data input, understand that the student hasn't done tasks matching the given data description. Also, 
                know that this report is for the student to improve themselves, and they have no part in making sure the data is logged for
                this analysis. Deeply analyze the SQL query ->{sql_query} and the data description ->{section.data_requirements} used to  
                extract the data and figure out why there was no data available in the roadmap, which the student went through and write 
                the section accordingly.
                """
            ),
            HumanMessage(
                content=f"""Here is the section name: {section.name} and description: {section.description}
                Data for writing this section: {rows}"""
            ),
        ]
    )

    # Return completed section
    return {"completed_sections": [section_result.content]}

def synthesizer(state: State):
    """Synthesize full report from sections"""

    # List of completed sections
    completed_sections = state["completed_sections"]

    # Format completed section to str to use as context for final sections
    completed_report_sections = "\n\n---\n\n".join(completed_sections)

    return  {"final_report": completed_report_sections}

# Assign processors function
def assign_processors(state: State):
    """Assign a processor to each section in the plan"""
    return [Send("processor", {"section": s}) for s in state["sections"]]

def generate_report(full_roadmap):
    with st.spinner("Generating performance report using AI..."):        
        # Build workflow
        workflow_builder = StateGraph(State)

        # Add the nodes
        workflow_builder.add_node("orchestrator", orchestrator)
        workflow_builder.add_node("processor", processor)
        workflow_builder.add_node("synthesizer", synthesizer)

        # Add edges to connect nodes
        workflow_builder.add_edge(START, "orchestrator")
        workflow_builder.add_conditional_edges("orchestrator", assign_processors, ["processor"])
        workflow_builder.add_edge("processor", "synthesizer")
        workflow_builder.add_edge("synthesizer", END)

        # Compile the workflow
        workflow = workflow_builder.compile()

        # Initialize database
        create_db_for_report(full_roadmap)

        # Invoke
        state = workflow.invoke({})

        st.session_state.final_report = state["final_report"]

# Function to extract available dates
def extract_available_dates():
    with st.spinner("Extracting available dates for rescheduling..."):
        data = st.session_state.data
        
        def remove_the_first_day(roadmap):
            new_roadmap = {
                "schedule": []
            }
            for day in roadmap['schedule']:
                if day['dayNumber'] != 1:
                    new_roadmap['schedule'].append(day)
            return new_roadmap
        
        roadmap = remove_the_first_day(data)
        
        available_dates = []
        for day in roadmap['schedule']:
            available_dates.append(day['date'])
        
        st.session_state.available_dates = available_dates
        st.success(f"Found {len(available_dates)} available dates for rescheduling!")

# Function to shift incomplete tasks using the evaluator-optimizer approach
def shift_incomplete_tasks():
    with st.spinner("Optimizing task distribution using evaluator-optimizer approach..."):
        try:
            # Initialize needed components
            llm = ChatOpenAI(model="gpt-4o-mini", api_key = os.getenv("OPENAI_API_KEY"))
            
            # Schema for structured output to use in evaluation
            class Feedback(BaseModel):
                grade: Literal["added", "not added"] = Field(
                    description="Check if all the incomplete tasks are added to the roadmap or not",
                )
                feedback: str = Field(
                    description="If some tasks are not added, give feedback to add those tasks also",
                )
            
            # Augment the LLM with schema for structured output
            evaluator = llm.with_structured_output(Feedback)
            
            # Graph state
            class State(TypedDict):
                roadmap: dict
                available_dates: list
                incomplete_task_list: list
                feedback: str
                added_or_not: str
                iteration_count: int
            
            # Initialize state
            current_state = {
                "roadmap": {},
                "available_dates": st.session_state.available_dates,
                "incomplete_task_list": st.session_state.incomplete_task_list,
                "feedback": "",
                "added_or_not": "",
                "iteration_count": 0
            }
            
            # Progress bar for iterations
            progress_bar = st.progress(0)
            iteration_status = st.empty()
            
            # First call to generator
            iteration_status.write("Iteration 1: Generating initial task distribution...")
            if current_state.get("feedback"):
                msg = llm.invoke(
                    f"""Add the following incomplete_tasks {current_state['incomplete_task_list']} to the roadmap key and take into account the feedback {current_state['feedback']}
                    and make sure that we dynamically add the tasks not increasing the load on just one day, also we have to add the tasks on
                    following dates only {current_state['available_dates']} Make sure you only give roadmap json as output and nothing else, strictly follow the output structure:
                    ```json
                    {{
                      "roadmap": [
                        {{
                          "date": "YYYY-MM-DD",
                          "tasks": [
                            {{
                              "subject": "Subject Name",
                              "ChapterName": "Chapter Name",
                              "type": "Type of Task",
                              "subtopic": "Subtopic Name",
                              "time": "estimated time"
                            }}
                          ]
                        }}
                      ]
                    }}
                    ```
                    """
                )
            else:
                msg = llm.invoke(
                    f"""Add the following incomplete_tasks {current_state['incomplete_task_list']} to the roadmap key
                    and make sure that we dynamically add the tasks not increasing the load on just one day, also we have to add the tasks on
                    following dates only {current_state['available_dates']} Make sure you only give roadmap json as output and nothing else, strictly follow the output structure:
                    ```json
                    {{
                      "roadmap": [
                        {{
                          "date": "YYYY-MM-DD",
                          "tasks": [
                            {{
                              "subject": "Subject Name",
                              "ChapterName": "Chapter Name",
                              "type": "Type of Task",
                              "subtopic": "Subtopic Name",
                              "time": "estimated time"
                            }}
                          ]
                        }}
                      ]
                    }}
                    ```
                    """
                )
            
            current_state["roadmap"] = msg.content
            progress_bar.progress(1/6)
            
            # Enter optimization loop
            max_iterations = st.session_state.max_optimizer_iterations
            current_iteration = 1
            
            while current_iteration <= max_iterations:
                # Evaluate the current roadmap
                iteration_status.write(f"Iteration {current_iteration}: Evaluating task distribution...")
                
                grade = evaluator.invoke(
                    f"Grade the roadmap {current_state['roadmap']} by checking if {current_state['incomplete_task_list']} are all added or not and make sure that we dynamically add the tasks not increasing the load on just one day"
                )
                
                current_state["added_or_not"] = grade.grade
                current_state["feedback"] = grade.feedback
                current_state["iteration_count"] += 1
                
                progress_bar.progress((current_iteration * 2 - 1)/6)
                
                # Check if we're done or need another iteration
                if current_state["added_or_not"] == "added":
                    iteration_status.write(f"✅ Success! All tasks distributed after {current_iteration} iterations.")
                    break
                
                if current_iteration == max_iterations:
                    iteration_status.write(f"⚠️ Reached maximum iterations ({max_iterations}). Using best result so far.")
                    break
                
                # Generate an improved roadmap based on feedback
                iteration_status.write(f"Iteration {current_iteration + 1}: Improving task distribution based on feedback...")
                
                msg = llm.invoke(
                    f"""Add the following incomplete_tasks {current_state['incomplete_task_list']} to the roadmap key 
                    and take into account the feedback: {current_state['feedback']}
                    Make sure that we dynamically add the tasks not increasing the load on just one day.
                    Only add tasks on these available dates: {current_state['available_dates']}
                    Make sure you only give roadmap json as output and nothing else, strictly follow the output structure:
                    ```json
                    {{
                      "roadmap": [
                        {{
                          "date": "YYYY-MM-DD",
                          "tasks": [
                            {{
                              "subject": "Subject Name",
                              "ChapterName": "Chapter Name",
                              "type": "Type of Task",
                              "subtopic": "Subtopic Name",
                              "time": "estimated time"
                            }}
                          ]
                        }}
                      ]
                    }}
                    ```
                    """
                )
                
                current_state["roadmap"] = msg.content
                current_iteration += 1
                progress_bar.progress((current_iteration * 2 - 2)/6)
            
            # Process the final roadmap content
            shifted_tasks_roadmap = current_state["roadmap"]
            
            # Extract JSON part from the response
            if "```json" in shifted_tasks_roadmap:
                shifted_tasks_roadmap = shifted_tasks_roadmap.split("```json")[1].split("```")[0]
            elif "```" in shifted_tasks_roadmap:
                shifted_tasks_roadmap = shifted_tasks_roadmap.split("```")[1].split("```")[0]
            
            st.session_state.shifted_roadmap = shifted_tasks_roadmap
            st.success(f"Tasks rescheduled successfully after {current_iteration} iterations!")
            
            progress_bar.progress(1.0)
            
        except Exception as e:
            st.error(f"Error in optimization process: {e}")

# Function to merge shifted tasks into main roadmap
def merge_shifted_tasks():
    with st.spinner("Merging rescheduled tasks into main roadmap..."):
        try:
            data = st.session_state.data
            shifted_roadmap = json.loads(st.session_state.shifted_roadmap)
            
            def add_task(roadmap, task, date_task_to_be_added):
                subject_name = task["subject"]
                chapter_name = task["ChapterName"]
                topic_name = task["subtopic"]
                type_name = task["type"]

                # Check if the date exists
                for day in roadmap['schedule']:
                    if day['date'] == date_task_to_be_added:
                        # Find or create subject
                        subject_exists = False
                        for subject in day['subjects']:
                            if subject['name'] == subject_name:
                                subject_exists = True
                                # Check if task already exists
                                task_exists = False
                                for existing_task in subject['tasks']:
                                    if (existing_task.get('ChapterName') == chapter_name and 
                                        existing_task.get('type') == type_name and 
                                        (existing_task.get('subtopic', '') == topic_name or 
                                         existing_task.get('topic', '') == topic_name)):
                                        task_exists = True
                                        break
                                
                                if not task_exists:
                                    # Add task
                                    temp_task = {
                                        "ChapterName": chapter_name,
                                        "type": type_name,
                                        "subtopic": topic_name,
                                        "time": task["time"],
                                        "task_completed": False,
                                        "completion_timestamp": None
                                    }
                                    subject['tasks'].append(temp_task)
                                break
                        
                        # If subject doesn't exist, create it
                        if not subject_exists:
                            new_subject = {
                                "name": subject_name,
                                "tasks": [{
                                    "ChapterName": chapter_name,
                                    "type": type_name,
                                    "subtopic": topic_name,
                                    "time": task["time"],
                                    "task_completed": False,
                                    "completion_timestamp": None
                                }]
                            }
                            day['subjects'].append(new_subject)
                        break
                        
                return roadmap

            # Process each task in the shifted roadmap
            for day in shifted_roadmap["roadmap"]:
                date = day['date']
                tasks = day['tasks']
                for task in tasks:
                    data = add_task(data, task, date)
            
            st.session_state.updated_roadmap = data
            st.success("Tasks merged into roadmap successfully!")
            
        except Exception as e:
            st.error(f"Error merging tasks: {e}")

# ---- HOME PAGE ----
if page == "Home":
    st.title("📚 JEE Roadmap Planner")
    
    st.markdown("""
    ### Welcome to your JEE Study Roadmap Planner!
    
    This tool helps you manage your JEE preparation schedule by:
    
    1. 📊 **Analyzing your study performance**
    2. 🔄 **Redistributing incomplete tasks**
    3. 📝 **Providing personalized feedback**
    
    Get started by loading your roadmap data and following the step-by-step process.
    """)
    
    # Settings section
    with st.expander("⚙️ Advanced Settings"):
        st.session_state.max_optimizer_iterations = st.slider(
            "Maximum Optimizer Iterations", 
            min_value=1, 
            max_value=5, 
            value=st.session_state.max_optimizer_iterations,
            help="Limit how many times the optimizer will try to improve task distribution"
        )
    
    st.info("Navigate using the sidebar to access different features of the app.")
    
    # Initial data loading
    if st.button("📂 Load Roadmap Data"):
        success = load_initial_data()
        if success:
            st.session_state.first_load = True

# ---- ROADMAP MANAGER PAGE ----
elif page == "Roadmap Manager":
    st.title("🗓️ Roadmap Manager")
    
    if st.session_state.data is None:
        st.warning("Please load roadmap data first from the Home page.")
    else:
        st.markdown("### Roadmap Management Steps")
        
        col1, col2 = st.columns(2)
        
        with col1:
            st.subheader("Step 1: Process Tasks")
            if st.button("1️⃣ Mark Tasks as Incomplete"):
                process_task_completion_data()
            
            st.subheader("Step 2: Extract Tasks")
            if st.button("2️⃣ Extract Incomplete Tasks"):
                extract_incomplete_tasks()
                
            if st.session_state.incomplete_task_list:
                st.write(f"Found {len(st.session_state.incomplete_task_list)} incomplete tasks")
                with st.expander("View Incomplete Tasks"):
                    st.json(st.session_state.incomplete_task_list)
        
        with col2:
            st.subheader("Step 3: Prepare for Rescheduling")
            if st.button("3️⃣ Extract Available Dates"):
                extract_available_dates()
                
            if st.session_state.available_dates:
                with st.expander("View Available Dates"):
                    st.write(st.session_state.available_dates)
                    
            st.subheader("Step 4: Reschedule Tasks")
            if st.button("4️⃣ Optimize Task Distribution"):
                if not st.session_state.incomplete_task_list or not st.session_state.available_dates:
                    st.error("Please complete steps 2 and 3 first!")
                else:
                    shift_incomplete_tasks()
            
            if st.session_state.shifted_roadmap:
                with st.expander("View Task Distribution Plan"):
                    try:
                        st.json(json.loads(st.session_state.shifted_roadmap))
                    except:
                        st.text(st.session_state.shifted_roadmap)
        
        st.subheader("Step 5: Update Roadmap")
        if st.button("5️⃣ Merge Tasks into Main Roadmap"):
            if not st.session_state.shifted_roadmap:
                st.error("Please complete step 4 first!")
            else:
                merge_shifted_tasks()
                
        # Display original and updated roadmaps side by side
        if st.session_state.data_old and st.session_state.updated_roadmap:
            st.subheader("Roadmap Comparison")
            col1, col2 = st.columns(2)
            
            with col1:
                st.markdown("#### Original Roadmap")
                with st.expander("View Original Roadmap"):
                    st.json(st.session_state.data_old)
            
            with col2:
                st.markdown("#### Updated Roadmap")
                with st.expander("View Updated Roadmap"):
                    st.json(st.session_state.updated_roadmap)

# ---- TASK ANALYSIS PAGE ----
elif page == "Task Analysis":
    st.title("📊 Task Analysis")
    
    choice = st.selectbox("Choose the roadmap to use for building report", ["Four Day Roadmap", "Full Roadmap"])
    if choice == "Four Day Roadmap":
        if st.session_state.data is None:
            st.warning("Please load roadmap data first from the Home page.")
        st.session_state.report_data = st.session_state.data
    elif choice == "Full Roadmap":
        with open("synthesized_full_roadmap.json", "r") as f:
            st.session_state.report_data = json.load(f)

    st.markdown("### Performance Report")
    
    if st.button("🔍 Generate Performance Report"):
        generate_report(st.session_state.report_data)
    
    if st.session_state.final_report:
        st.markdown(st.session_state.final_report)
    else:
        st.info("Click the button above to generate your performance report.")
    
    # Add visualization options
    if st.session_state.data:
        st.subheader("Task Breakdown")
        
        # Simple task statistics
        if st.checkbox("Show Task Statistics"):
            task_count = 0
            subject_counts = {}
            type_counts = {}
            
            for day in st.session_state.report_data["schedule"]:
                for subject in day["subjects"]:
                    subject_name = subject["name"]
                    if subject_name not in subject_counts:
                        subject_counts[subject_name] = 0
                    
                    for task in subject["tasks"]:
                        subject_counts[subject_name] += 1
                        task_count += 1
                        
                        # Count by task type
                        task_type = task.get("type", "Unknown")
                        if task_type not in type_counts:
                            type_counts[task_type] = 0
                        type_counts[task_type] += 1
            
            st.write(f"Total tasks: {task_count}")
            
            # Create charts for data visualization
            col1, col2 = st.columns(2)
            
            with col1:
                st.subheader("Subject Distribution")
                st.bar_chart(subject_counts)
            
            with col2:
                st.subheader("Task Type Distribution")
                st.bar_chart(type_counts)
# ---- ROADMAP CHATBOT PAGE ----
elif page == "Roadmap Chatbot":
    st.title("🤖 Roadmap Chatbot Assistant")

    user_query = st.text_input("Ask a question about your roadmap:", placeholder="e.g., What are my tasks on 14 Feb 2025?")

    if st.button("Ask") and user_query:
        with st.spinner("Thinking..."):
            try:
                response = answer_user_query(user_query)
                st.markdown(response)
            except Exception as e:
                st.error(f"Error: {e}")