Merge pull request #42 from CS3219-AY2425S1/yitong/d5-frontend

Add AI Hint Function

backend/ai-hint-service/.gitignore

@@ -0,0 +1,103 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Virtual Environment
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# FastAPI specific
+.env
+.env.local
+.env.*.local
+
+# IDE - VSCode
+.vscode/
+*.code-workspace
+.history/
+
+# IDE - PyCharm
+.idea/
+*.iml
+*.iws
+.idea_modules/
+
+# IDE - Jupyter Notebook
+.ipynb_checkpoints
+*.ipynb
+
+# Logs
+logs/
+*.log
+log.txt
+
+# Database
+*.db
+*.sqlite
+*.sqlite3
+
+# Coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# macOS
+.DS_Store
+.AppleDouble
+.LSOverride
+._*
+
+# Windows
+Thumbs.db
+ehthumbs.db
+Desktop.ini
+$RECYCLE.BIN/
+
+# Linux
+*~
+.fuse_hidden*
+.directory
+.Trash-*
+.nfs*
+
+# Project specific
+data/
+temp/
+config.local.py
+local_settings.py
+
+.env

backend/ai-hint-service/Dockerfile

@@ -0,0 +1,24 @@
+# backend/ai-hint-service/Dockerfile
+
+FROM python:3.9-slim
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+# Set work directory
+WORKDIR /app
+
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+# Copy application code
+COPY ./app /app/app
+
+# Expose port
+EXPOSE 8000
+
+# Command to run the application
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

backend/ai-hint-service/app/.env.sample

@@ -0,0 +1,2 @@
+OPENAI_API_KEY="API_KEY"
+QUESTION_SERVICE_URL = "http://question:3002"

backend/ai-hint-service/app/main.py

@@ -0,0 +1,37 @@
+from fastapi import FastAPI
+from openai import OpenAI
+from .routes import hint, code_analysis, ai_answer
+from fastapi.middleware.cors import CORSMiddleware
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+app = FastAPI(
+    title="AI Hint Service",
+    description="Provides AI-generated hints, code complexity analysis, and model answers.",
+    version="1.0.0",
+)
+
+
+origins = [
+    "*",
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,  # Or ["*"] to allow all
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Include routers
+app.include_router(hint.router, prefix="/api/hint", tags=["Hint"])
+app.include_router(code_analysis.router, prefix="/api/code-analysis", tags=["Code Analysis"])
+app.include_router(ai_answer.router, prefix="/api/ai_answer", tags=["Model Answer"])
+
+# Root endpoint
+@app.get("/")
+def read_root():
+    return {"message": "AI Hint Service is up and running."}

backend/ai-hint-service/app/routes/ai_answer.py

@@ -0,0 +1,37 @@
+from fastapi import APIRouter, HTTPException
+from ..services.openai_service import generate_ai_answer
+from ..schemas.ai_answer import AiAnswerRequest, AiAnswerResponse
+from typing import Optional
+
+router = APIRouter()
+
+@router.post("/", response_model=AiAnswerResponse)
+async def get_ai_answer(request: AiAnswerRequest):
+    """
+    Generate a model answer for the given question ID.
+    """
+    # Placeholder: Fetch question description from the question service
+    question_description = fetch_question_description(request.question_id)
+    if not question_description:
+        raise HTTPException(status_code=404, detail="Question not found.")
+
+    try:
+        ai_answer = generate_ai_answer(question_description, language=request.language)  
+        print(ai_answer)
+        return AiAnswerResponse(ai_answer=ai_answer)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+def fetch_question_description(question_id: int) -> Optional[str]:
+    # Implement the logic to fetch question description from the question service
+    import requests
+    QUESTION_SERVICE_URL = "http://question:3002"
+    try:
+        response = requests.get(f"{QUESTION_SERVICE_URL}/{question_id}")
+        if response.status_code == 200:
+            data = response.json()
+            return data.get("description")
+        else:
+            return None
+    except Exception as e:
+        return None

backend/ai-hint-service/app/routes/code_analysis.py

@@ -0,0 +1,16 @@
+from fastapi import APIRouter, HTTPException
+from ..services.openai_service import analyze_code_complexity
+from ..schemas.code_analysis import CodeAnalysisRequest, CodeAnalysisResponse
+
+router = APIRouter()
+
+@router.post("/", response_model=CodeAnalysisResponse)
+async def get_code_analysis(request: CodeAnalysisRequest):
+    """
+    Analyze the complexity of the provided code.
+    """
+    try:
+        result = analyze_code_complexity(request.code, request.language)
+        return CodeAnalysisResponse(**result)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

backend/ai-hint-service/app/routes/hint.py

@@ -0,0 +1,40 @@
+from fastapi import APIRouter, HTTPException
+from ..services.openai_service import generate_hint
+from ..schemas.hint import HintResponse
+from typing import Optional
+import requests
+import os
+
+router = APIRouter()
+
+@router.get("/{question_id}", response_model=HintResponse)
+async def get_hint(question_id: int):
+    """
+    Generate a hint for the given question ID.
+    """
+    question_description = fetch_question_description(question_id)
+    if not question_description:
+        raise HTTPException(status_code=404, detail="Question not found.")
+
+    try:       
+        hint = generate_hint(question_description)
+        return HintResponse(hint=hint)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+def fetch_question_description(question_id: int) -> Optional[str]:
+    # Implement the logic to fetch question description from the question service
+    # For example, make an HTTP request to the question service's API
+
+    QUESTION_SERVICE_URL = os.getenv("QUESTION_SERVICE_URL")
+    try:
+        link = f"{QUESTION_SERVICE_URL}/{question_id}"
+        print(link)
+        response = requests.get(link)
+        if response.status_code == 200:
+            data = response.json()
+            return data.get("description")
+        else:
+            return None
+    except Exception as e:
+        return None

backend/ai-hint-service/app/schemas/ai_answer.py

@@ -0,0 +1,8 @@
+from pydantic import BaseModel
+
+class AiAnswerRequest(BaseModel):
+    question_id: int
+    language: str
+
+class AiAnswerResponse(BaseModel):
+    ai_answer: str

backend/ai-hint-service/app/schemas/code_analysis.py

@@ -0,0 +1,9 @@
+from pydantic import BaseModel
+
+class CodeAnalysisRequest(BaseModel):
+    code: str
+    language: str  # e.g., "python", "cpp", "java"
+
+class CodeAnalysisResponse(BaseModel):
+    # complexity: str
+    analysis: str

backend/ai-hint-service/app/schemas/hint.py

@@ -0,0 +1,4 @@
+from pydantic import BaseModel
+
+class HintResponse(BaseModel):
+    hint: str

backend/ai-hint-service/app/services/openai_service.py

@@ -0,0 +1,49 @@
+import openai
+
+model = 'gpt-3.5-turbo-0125'
+
+def generate_hint(question_description: str) -> str:
+    prompt = f"Provide a concise hint to achieve the most efficient time complexity for the following programming problem:\n\n{question_description}\n\nHint:"
+    completion = openai.chat.completions.create(
+        model=model,
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+        ]
+    )
+
+    hint = completion.choices[0].message.content
+    return hint
+
+def analyze_code_complexity(code: str, language: str) -> dict:
+    prompt = f"Analyze the following {language} code for its time and space complexity. Provide a detailed explanation.\n\nCode:\n{code}\n\nAnalysis:"
+    print(prompt)
+    completion = openai.chat.completions.create(
+        model=model,
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+        ]
+    )
+    analysis = completion.choices[0].message.content
+    # if "O(" in analysis:
+    #     start = analysis.find("O(")
+    #     end = analysis.find(")", start) + 1
+    #     complexity = analysis[start:end]
+    # else:
+    #     complexity = "Complexity could not be determined."
+    # return {"complexity": complexity, "analysis": analysis}
+    return {"analysis": analysis}
+
+def generate_ai_answer(question_description: str, language: str) -> str:
+    prompt = f"Provide a complete and optimized {language} solution to achieve the most efficient time complexity for the following programming problem:\n\n{question_description}\n\nSolution:"
+    print(prompt)
+    completion = openai.chat.completions.create(
+        model=model,
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+        ]
+    )
+    ai_answer = completion.choices[0].message.content
+    return ai_answer

backend/ai-hint-service/requirements.txt

@@ -0,0 +1,6 @@
+fastapi
+uvicorn
+openai
+pydantic
+python-dotenv
+requests

docker-compose.yml

@@ -75,6 +75,13 @@ services:
     ports:
       - "3005:3005"
 
+  ai-hint-service:
+    build: ./backend/ai-hint-service
+    ports:
+      - "3006:8000"
+    depends_on:
+      - question # Assuming 'question' service is needed to fetch question descriptions
+
   frontend:
     build: ./frontend
     env_file:
@@ -87,3 +94,4 @@ services:
       - question
       - collaboration
       - rabbitmq
+      - ai-hint-service # Ensure frontend waits for ai-hint-service

frontend/.env.sample

@@ -14,4 +14,5 @@ REACT_APP_USER_SERVICE_URL=http://localhost/api/users/
 REACT_APP_MATCHING_SERVICE_URL=http://localhost:3003
 REACT_APP_COMMUNICATION_SERVICE_URL=http://localhost:3004
 REACT_APP_COLLABORATION_SERVICE_URL=http://localhost:3005
+REACT_APP_AI_HINT_URL=http://localhost:3006
 REACT_APP_VIDEO_SERVICE_PORT=9000