app.py

import json
import sqlite3
import operator
from math import ceil
from fastapi import FastAPI, Query, Body
from contextlib import asynccontextmanager
from datetime import datetime
from collections import defaultdict
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage
from typing import Annotated, List, Optional, Dict, Any
from pydantic import BaseModel, Field
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.constants import Send
from fastapi.responses import HTMLResponse

# Session State for storing runtime data
session_state = {
    "report_data": None,
    "final_report": None
}

# Initialize FastAPI app
app = FastAPI(
    title="JEE Roadmap Planner API",
    description="API for managing and analyzing JEE Roadmaps",
    version="1.0.0"
)

# Models for data input
class RoadmapData(BaseModel):
    schedule: List[Dict[str, Any]]
    # Add any other fields from the roadmap schema

# AGENT 1
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.",
    )

# Initialize LLM
llm = ChatOpenAI(model="gpt-4o-mini")
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_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):
    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}")

def generate_report(roadmap_data):
    # 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(roadmap_data)

    # Invoke
    state = workflow.invoke({})

    session_state['final_report'] = state["final_report"]
    
    return state["final_report"]

# AGENT 3
def get_chapters_and_subtopics(roadmap_data):
    ch_subt = {
        "Physics": {},
        "Chemistry": {},
        "Maths": {}
    }

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

    for day in roadmap_data["schedule"]:
        for subject in day['subjects']:
            sub = ch_subt[subject['name']]
            for task in subject['tasks']:
                if task['subtopic'] not in sub[task['ChapterName']]:
                  sub[task['ChapterName']].append(task['subtopic'])

    return ch_subt

def create_roadmap_db(roadmap_data):
    try:
        conn = sqlite3.connect("jee_roadmap.db")
        cursor = conn.cursor()

        cursor.execute("DROP TABLE IF EXISTS roadmap")
        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.")
        return True
    except Exception as e:
        print(f"⚠️ Error initializing database: {e}")
        return False

# Function to convert NL query to SQL
def generate_sql_from_nl(prompt, ch_subt):
    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
                    )"""

    response = llm.invoke(
        [
            SystemMessage(
                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.
             - For date related queries, refer today's date {datetime.now().date()}
             - If the user ask's you general questions, Return a Dummy query like {"SELECT * FROM your_table WHERE FALSE;"}
             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.
             """
            ),
            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()

# 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)
    columns = [desc[0] for desc in cursor.description]
    rows = cursor.fetchall()
    data = {
        "query": sql_query,
        "columns": columns,
        "rows": rows
        }
    conn.close()
    return data

# Function to convert SQL output to natural language
def generate_nl_from_sql_output(prompt, data):
    response = llm.invoke(
        [
            SystemMessage(
                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. If you find the
             user's prompt to be conversational in nature, please respond accordingly."""
            ),
            HumanMessage(
                content=f"""Answer to this users query using the data given to you, while keeping
             your role in mind: {prompt}"""
            ),
        ]
    )

    return response.content.strip()

# Main function for chatbot
def answer_user_query(prompt, roadmap_data):
    ch_subt = get_chapters_and_subtopics(roadmap_data)
    query = generate_sql_from_nl(prompt, ch_subt)
    data = fetch_data_from_sql(query)
    return generate_nl_from_sql_output(prompt, data)


@app.get("/", response_class=HTMLResponse)
def root():
    return """
    <html>
        <head><title>Sstudize Agents</title></head>
        <body style="font-family: Arial, sans-serif; text-align: center; margin-top: 50px;">
            <h1>Welcome to Sstudize Agents!</h1>
            <p>Select an agent:</p>
            <ul style="list-style-type: none;">
                <li><a href="/docs#/default/agent1_agent1_post">Agent 1: Task Analysis</a></li>
                <li><a href="/docs#/default/agent3_agent3_post">Agent 3: Chatbot Assistant</a></li>
            </ul>
        </body>
    </html>
    """


# --- AGENT 1: Task Analysis (Task Analysis Page) ---
@app.post("/agent1")
def agent1(roadmap_data: RoadmapData = Body(..., description="Complete roadmap data in JSON format")):
    """
    Agent 1 - Task Analysis: Builds a performance report based on provided roadmap data.
    """
    try:
        # Store the roadmap data
        session_state["report_data"] = roadmap_data.dict()
        
        # Generate performance report
        report = generate_report(session_state["report_data"])
        
        return {
            "status": "success",
            "final_report": report
        }
    except Exception as e:
        return {"status": "error", "message": str(e)}


# --- AGENT 3: Roadmap Chatbot (Roadmap Chatbot Page) ---
@app.post("/agent3")
def agent3(
    query: str = Body(..., description="User's message to the chatbot"),
    roadmap_data: RoadmapData = Body(..., description="Complete roadmap data in JSON format")
):
    """
    Agent 3 - Roadmap Chatbot Assistant: Answers user questions about the roadmap in a chat-like style.
    """
    try:
        # Create DB from provided roadmap data
        db_created = create_roadmap_db(roadmap_data.dict())
        if not db_created:
            return {"status": "error", "message": "Failed to create database from roadmap data"}
            
        # Generate response to user query
        response = answer_user_query(query, roadmap_data.dict())
        
        return {
            "status": "success",
            "chat_response": {
                "user": query,
                "assistant": response
            }
        }
    except Exception as e:
        return {
            "status": "error", 
            "message": f"Error processing query: {str(e)}"
        }