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First backend commit #2

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4 changes: 4 additions & 0 deletions .gitignore
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@simrathanspal simrathanspal Dec 5, 2024

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Push sample.env and add .env to gitignore

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backend/.env
backend/venv
backend/mem0_client.py
backend/dashboard.py
6 changes: 5 additions & 1 deletion README.md
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@simrathanspal simrathanspal Dec 5, 2024

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We need to dockerise the application.
The docker files can go under the global project along side README.md

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# AITutor
# AITutor backend

GET /api/survey/questions - get all questions
GET /api/survey/questions/{category} - get questions by category
POST /api/survey/submit - submit answers
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Add to .gitignore

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73 changes: 73 additions & 0 deletions backend/app/api/survey.py
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from fastapi import APIRouter, HTTPException, Depends
from typing import List
from app.models.survey import SurveyQuestion, UserSurveyResponse
from app.services.survey import SurveyService
from app.services.mem0 import Mem0Service
from config import get_settings

router = APIRouter(prefix="/survey", tags=["survey"])

@router.get("/questions", response_model=List[SurveyQuestion])
async def get_survey_questions(
survey_service: SurveyService = Depends(lambda: SurveyService())
):
"""
Retrieves all available survey questions.
Returns a list of questions with their options.
"""
return survey_service.get_all_questions()

@router.get("/questions/{category}", response_model=List[SurveyQuestion])
async def get_questions_by_category(
category: str,
survey_service: SurveyService = Depends(lambda: SurveyService())
):
"""
Retrieves questions for a specific category.
Args:
category: The category of questions to retrieve (e.g., 'learning_format', 'learning_pace')
Returns:
List of questions for the specified category
"""
questions = survey_service.get_questions_by_category(category)
if not questions:
raise HTTPException(
status_code=404,
detail=f"No questions found for category: {category}"
)
return questions

@router.post("/submit")
async def submit_survey(
response: UserSurveyResponse,
survey_service: SurveyService = Depends(lambda: SurveyService()),
settings = Depends(get_settings)
):
"""
Submits survey responses and stores them in Mem0.ai.
Args:
response: User's survey responses including user_id and answers
Returns:
Processed survey data and initial learning path
"""
# Process survey responses
processed_data = survey_service.process_answers(response.dict())

# Store in Mem0.ai and get learning path
mem0_service = Mem0Service(api_key=settings.MEM0_API_KEY)
try:
result = await mem0_service.store_survey_response(
response.user_id,
processed_data
)
return {
"status": "success",
"message": "Survey responses successfully processed",
"learning_path": result,
"user_id": response.user_id
}
except Exception as e:
raise HTTPException(
status_code=400,
detail=f"Failed to process survey: {str(e)}"
)
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49 changes: 49 additions & 0 deletions backend/app/main.py
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from fastapi import FastAPI
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@simrathanspal simrathanspal Dec 5, 2024

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Let's keep all endpoints under api folder

from app.api import survey
from fastapi.middleware.cors import CORSMiddleware
from config import get_settings

# Initialize FastAPI app
app = FastAPI(
title="AI Tutor API",
description="API for personalized JavaScript learning experience",
version="1.0.0"
)

# Configure CORS
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # In production, replace with actual frontend domain
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)

# Include routers
app.include_router(survey.router, prefix="/api")

@app.get("/")
async def root():
"""
Root endpoint returning API information
"""
return {
"message": "Welcome to AI Tutor API",
"version": "1.0.0",
"documentation": "/docs",
"health_check": "/health"
}

@app.get("/health")
async def health_check():
"""
Health check endpoint for monitoring
"""
return {
"status": "healthy",
"api_version": "1.0.0"
}

if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
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20 changes: 20 additions & 0 deletions backend/app/models/survey.py
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from pydantic import BaseModel
from typing import List, Optional

class SurveyOption(BaseModel):
id: int
text: str

class SurveyQuestion(BaseModel):
id: int
question: str
options: List[SurveyOption]
category: str

class UserAnswer(BaseModel):
question_id: int
selected_option_id: int

class UserSurveyResponse(BaseModel):
user_id: str
answers: List[UserAnswer]
22 changes: 22 additions & 0 deletions backend/app/models/tutor.py
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from pydantic import BaseModel
from typing import Optional, Dict, List

class LearningPreferences(BaseModel):
learning_format: str
weekly_time: str
learning_pace: str
theory_vs_practice: str
independence_level: str

class LearningSession(BaseModel):
user_id: str
topic: str
session_type: str
completion_status: str
feedback: Optional[str]

class LearningPath(BaseModel):
user_id: str
current_topic: str
next_topics: List[str]
recommendations: Dict[str, str]
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58 changes: 58 additions & 0 deletions backend/app/services/mem0.py
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@simrathanspal simrathanspal Dec 5, 2024

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Will need to be adapted to our usecase

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import aiohttp
from typing import Dict, Any, Optional
import json

class Mem0Service:
def __init__(self, api_key: str, base_url: str = "https://api.mem0.ai/v1"):
self.api_key = api_key
self.base_url = base_url
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}

async def store_survey_response(self, user_id: str, survey_data: Dict) -> Dict[str, Any]:
"""Stores survey responses in Mem0.ai"""
prompt = f"""
Analyze the learning preferences for user {user_id}:
{json.dumps(survey_data, indent=2)}

Based on these preferences, create a personalized learning path and provide recommendations.
Include:
1. Suggested learning format
2. Recommended pace
3. Theory/practice balance
4. Initial topics to cover
"""

async with aiohttp.ClientSession() as session:
async with session.post(
f"{self.base_url}/store",
headers=self.headers,
json={
"user_id": user_id,
"content": survey_data,
"metadata": {"type": "survey_response"}
}
) as response:
return await response.json()

async def get_learning_path(self, user_id: str) -> Dict[str, Any]:
"""Retrieves personalized learning path based on survey responses"""
async with aiohttp.ClientSession() as session:
async with session.get(
f"{self.base_url}/users/{user_id}/learning_path",
headers=self.headers
) as response:
return await response.json()

async def update_progress(self, user_id: str, progress_data: Dict) -> Dict[str, Any]:
"""Updates learning progress and gets recommendations"""
async with aiohttp.ClientSession() as session:
async with session.post(
f"{self.base_url}/users/{user_id}/progress",
headers=self.headers,
json=progress_data
) as response:
return await response.json()

83 changes: 83 additions & 0 deletions backend/app/services/survey.py
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Will need to be adapted to our usecase.

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from typing import List, Dict
from app.models.survey import SurveyQuestion, SurveyOption

class SurveyService:
def __init__(self):
self.questions = [
SurveyQuestion(
id=1,
category="learning_format",
question="What's the best way for you to receive information during learning?",
options=[
SurveyOption(id=1, text="Text-based guide with examples"),
SurveyOption(id=2, text="Video with step-by-step explanations"),
SurveyOption(id=3, text="Practical tasks to solve on your own"),
SurveyOption(id=4, text="Combined format (all options together)")
]
),
SurveyQuestion(
id=2,
category="learning_pace",
question="How much time per week can you dedicate to learning?",
options=[
SurveyOption(id=1, text="Less than 3 hours"),
SurveyOption(id=2, text="3-5 hours"),
SurveyOption(id=3, text="5-10 hours"),
SurveyOption(id=4, text="More than 10 hours")
]
),
SurveyQuestion(
id=3,
category="learning_style",
question="What learning pace do you prefer?",
options=[
SurveyOption(id=1, text="Fast-paced (intensive material coverage)"),
SurveyOption(id=2, text="Balanced (mix of learning and breaks)"),
SurveyOption(id=3, text="Slow-paced (step-by-step learning)")
]
),
SurveyQuestion(
id=4,
category="theory_practice",
question="How do you prefer to learn new concepts?",
options=[
SurveyOption(id=1, text="Read theory first, then practice"),
SurveyOption(id=2, text="Learn by doing, reference theory as needed")
]
)
]

def get_all_questions(self) -> List[SurveyQuestion]:
"""Retrieves all survey questions"""
return self.questions

def get_questions_by_category(self, category: str) -> List[SurveyQuestion]:
"""Retrieves questions filtered by category"""
return [q for q in self.questions if q.category == category]

def validate_answer(self, question_id: int, option_id: int) -> bool:
"""Validates if the answer option exists for the given question"""
question = next((q for q in self.questions if q.id == question_id), None)
if not question:
return False
return any(opt.id == option_id for opt in question.options)

def process_answers(self, answers: Dict) -> Dict:
"""Processes survey answers and formats data for Mem0.ai storage"""
processed_data = {
"learning_preferences": {},
"metadata": {
"timestamp": "2024-01-01T00:00:00Z",
"survey_version": "1.0"
}
}

for answer in answers["answers"]:
question = next(q for q in self.questions if q.id == answer["question_id"])
option = next(opt for opt in question.options if opt.id == answer["selected_option_id"])
processed_data["learning_preferences"][question.category] = {
"selected_option": option.text,
"question_id": question.id
}

return processed_data
28 changes: 28 additions & 0 deletions backend/config.py
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# config.py
from pydantic_settings import BaseSettings
from functools import lru_cache

class Settings(BaseSettings):
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Separate classes for different configs will be good.

# Mem0.ai settings
MEM0_API_KEY: str
MEM0_API_URL: str = "https://api.mem0.ai/v1"

# Database settings (if needed later)
DATABASE_URL: str = "sqlite:///./test.db"

# API settings
API_V1_PREFIX: str = "/api/v1"
PROJECT_NAME: str = "AI Tutor"

# Security settings
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WHat is the use of this code

SECRET_KEY: str
ALGORITHM: str = "HS256"
ACCESS_TOKEN_EXPIRE_MINUTES: int = 30

class Config:
env_file = ".env"
case_sensitive = True

@lru_cache()
def get_settings():
return Settings()
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Verify this

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