fIxed Backend

backend/ai-hint-service/.gitignore

@@ -1 +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/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

@@ -1,20 +1,21 @@
-# backend/ai-hint-service/app/main.py
-
 from fastapi import FastAPI
+from openai import OpenAI
 from .routes import hint, code_analysis, model_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",
 )
 
-# CORS settings - adjust origins as needed
+
 origins = [
-    "http://localhost",
-    "http://localhost:80",
-    # Add other frontend URLs here
+    "*",
 ]
 
 app.add_middleware(

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

@@ -2,6 +2,8 @@
 from ..services.openai_service import generate_hint
 from ..schemas.hint import HintResponse
 from typing import Optional
+import requests
+import os
 
 router = APIRouter()
 
@@ -10,13 +12,11 @@ async def get_hint(question_id: int):
     """
     Generate a hint for the given question ID.
     """
-    # Placeholder: Fetch question description from the question service
-    # You need to integrate with your question service's API to get the description
     question_description = fetch_question_description(question_id)
     if not question_description:
         raise HTTPException(status_code=404, detail="Question not found.")
 
-    try:
+    try:       
         hint = generate_hint(question_description)
         return HintResponse(hint=hint)
     except Exception as e:
@@ -25,14 +25,12 @@ async def get_hint(question_id: int):
 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
-    import requests
-    QUESTION_SERVICE_URL = "http://localhost/api/questions"
-    QUESTION_SERVICE_URL = "http://question:3002"
+
+    QUESTION_SERVICE_URL = os.getenv("QUESTION_SERVICE_URL")
     try:
         link = f"{QUESTION_SERVICE_URL}/{question_id}"
         print(link)
         response = requests.get(link)
-        print(response)
         if response.status_code == 200:
             data = response.json()
             return data.get("description")

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

@@ -1,40 +1,33 @@
-from openai import OpenAI
-# from ..dependencies import OPENAI_API_KEY
-from dotenv import load_dotenv
-import os
+import openai
+
 
-load_dotenv()
 
-client = OpenAI(
-    api_key = os.getenv("OPENAI_API_KEY")
-)
 
 model = 'gpt-3.5-turbo-0125'
 
 def generate_hint(question_description: str) -> str:
     prompt = f"Provide a concise hint for the following programming problem:\n\n{question_description}\n\nHint:"
-    completion = client.chat.completions.create(
+    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
-    print(hint)
     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:"
-    completion = client.completions.create(
+    completion = openai.completions.create(
         model=model,
         messages=[
             {"role": "system", "content": "You are a helpful assistant."},
             {"role": "user", "content": prompt}
         ]
     )
     analysis = completion.choices[0].message['content']
-    # Simple heuristic to determine complexity
     if "O(" in analysis:
         start = analysis.find("O(")
         end = analysis.find(")", start) + 1
@@ -45,7 +38,7 @@ def analyze_code_complexity(code: str, language: str) -> dict:
 
 def generate_model_answer(question_description: str, language: str) -> str:
     prompt = f"Provide a complete and optimized {language} solution for the following programming problem:\n\n{question_description}\n\nSolution:"
-    completion = client.completions.create(
+    completion = openai.completions.create(
         model=model,
         messages=[
             {"role": "system", "content": "You are a helpful assistant."},

docker-compose.yml

@@ -78,12 +78,9 @@ services:
   ai-hint-service:
     build: ./backend/ai-hint-service
     ports:
-      - "8000:8000"
-    environment:
-      - OPENAI_API_KEY=${OPENAI_API_KEY}  # Ensure you set this in your environment or .env file
+      - "3006:8000"
     depends_on:
-      - question  # Assuming 'question' service is needed to fetch question descriptions
-
+      - question # Assuming 'question' service is needed to fetch question descriptions
 
   frontend:
     build: ./frontend