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AILearningsystem / app.py

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Rename AI_python_learning_system.ipynb to app.py

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	{
	"nbformat": 4,
	"nbformat_minor": 0,
	"metadata": {
	"colab": {
	"provenance": []
	},
	"kernelspec": {
	"name": "python3",
	"display_name": "Python 3"
	},
	"language_info": {
	"name": "python"
	}
	},
	"cells": [
	{
	"cell_type": "code",
	"source": [
	"# Install required packages\n",
	"!pip install gradio langchain openai python-dotenv sqlalchemy pandas numpy matplotlib seaborn pydantic langchain_openai chromadb tiktoken"
	],
	"metadata": {
	"colab": {
	"base_uri": "https://localhost:8080/"
	},
	"id": "0FO3eb2vNuhz",
	"outputId": "08c18148-e281-4cb5-fd92-b42189a4727a"
	},
	"execution_count": null,
	"outputs": [
	{
	"output_type": "stream",
	"name": "stdout",
	"text": [
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	]
	}
	]
	},
	{
	"cell_type": "markdown",
	"source": [
	"#Core system"
	],
	"metadata": {
	"id": "WpW8pKv1Zi3r"
	}
	},
	{
	"cell_type": "code",
	"source": [
	"# core_system.py - Modified with fixed exam functionality\n",
	"\n",
	"import os\n",
	"import json\n",
	"import datetime\n",
	"import time\n",
	"from datetime import timedelta\n",
	"from typing import List, Dict, Any, Optional\n",
	"\n",
	"# LLM Integration using LangChain\n",
	"class LLMService:\n",
	" def __init__(self, api_key):\n",
	" self.api_key = api_key\n",
	" # Changed from ChatOpenAI to ChatGroq\n",
	" try:\n",
	" from langchain_groq import ChatGroq\n",
	" self.chat_model = ChatGroq(\n",
	" model=\"llama3-70b-8192\", # Using a Groq compatible model\n",
	" temperature=0.2,\n",
	" groq_api_key=api_key\n",
	" )\n",
	" except ImportError:\n",
	" # Fallback to direct API calls if langchain_groq is not available\n",
	" import requests\n",
	" self.chat_model = None\n",
	"\n",
	" def create_chain(self, template: str, output_key: str = \"result\"):\n",
	" if self.chat_model:\n",
	" from langchain.prompts import ChatPromptTemplate\n",
	" from langchain.chains import LLMChain\n",
	"\n",
	" chat_prompt = ChatPromptTemplate.from_template(template)\n",
	" return LLMChain(\n",
	" llm=self.chat_model,\n",
	" prompt=chat_prompt,\n",
	" output_key=output_key,\n",
	" verbose=True\n",
	" )\n",
	" return None\n",
	"\n",
	" def get_completion(self, prompt: str) -> str:\n",
	" if self.chat_model:\n",
	" chain = self.create_chain(prompt)\n",
	" response = chain.invoke({\"input\": \"\"})\n",
	" return response[\"result\"]\n",
	" else:\n",
	" # Direct API call if langchain is not available\n",
	" import requests\n",
	" headers = {\n",
	" \"Authorization\": f\"Bearer {self.api_key}\",\n",
	" \"Content-Type\": \"application/json\"\n",
	" }\n",
	" data = {\n",
	" \"model\": \"llama3-70b-8192\",\n",
	" \"messages\": [{\"role\": \"user\", \"content\": prompt}],\n",
	" \"temperature\": 0.7,\n",
	" \"max_tokens\": 2048\n",
	" }\n",
	" response = requests.post(\n",
	" \"https://api.groq.com/openai/v1/chat/completions\",\n",
	" headers=headers,\n",
	" json=data\n",
	" )\n",
	"\n",
	" if response.status_code == 200:\n",
	" return response.json()[\"choices\"][0][\"message\"][\"content\"]\n",
	" else:\n",
	" raise Exception(f\"API Error: {response.status_code} - {response.text}\")\n",
	"\n",
	" def generate_module_content(self, day: int, topic: str) -> str:\n",
	" prompt = f\"\"\"\n",
	" Create a comprehensive Python programming module for Day {day} covering {topic}.\n",
	" The module should follow this structure in Markdown format:\n",
	"\n",
	" # [Module Title]\n",
	"\n",
	" ## Introduction\n",
	" [A brief introduction to the day's topics]\n",
	"\n",
	" ## Section 1: [Section Title]\n",
	" [Detailed explanation of concepts]\n",
	"\n",
	" ### Code Examples\n",
	" ```python\n",
	" # Example code with comments\n",
	" ```\n",
	"\n",
	" ### Practice Exercises\n",
	" [2-3 exercises with clear instructions]\n",
	"\n",
	" ## Section 2: [Section Title]\n",
	" [Repeat the pattern for all relevant topics]\n",
	"\n",
	" Make sure the content is:\n",
	" - Comprehensive but focused on the day's topic\n",
	" - Includes clear examples with comments\n",
	" - Has practice exercises that build skills progressively\n",
	" - Uses proper Markdown formatting\n",
	" \"\"\"\n",
	" return self.get_completion(prompt)\n",
	"\n",
	" def generate_exam_questions(self, day: int, topic: str, previous_mistakes: List[Dict] = None) -> List[Dict]:\n",
	" mistake_context = \"\"\n",
	" if previous_mistakes and len(previous_mistakes) > 0:\n",
	" mistakes = \"\\n\".join([\n",
	" f\"- Question: {m['question']}\\n Wrong Answer: {m['user_answer']}\\n Correct Answer: {m['correct_answer']}\"\n",
	" for m in previous_mistakes[:3]\n",
	" ])\n",
	" mistake_context = f\"\"\"\n",
	" Include variations of questions related to these previous mistakes:\n",
	" {mistakes}\n",
	" \"\"\"\n",
	"\n",
	" prompt = f\"\"\"\n",
	" Create a 1-hour Python exam for Day {day} covering {topic}.\n",
	" {mistake_context}\n",
	"\n",
	" Include 5 questions with a mix of:\n",
	" - Multiple-choice (4 options each)\n",
	" - Short-answer (requiring 1-3 lines of text)\n",
	" - Coding exercises (simple functions or snippets)\n",
	"\n",
	" Return your response as a JSON array where each question is an object with these fields:\n",
	" - question_type: \"multiple-choice\", \"short-answer\", or \"coding\"\n",
	" - question_text: The full question text\n",
	" - options: Array of options (for multiple-choice only)\n",
	" - correct_answer: The correct answer or solution\n",
	" - explanation: Detailed explanation of the correct answer\n",
	" - difficulty: Number from 1 (easiest) to 5 (hardest)\n",
	"\n",
	" Example:\n",
	" [\n",
	" {{\n",
	" \"question_type\": \"multiple-choice\",\n",
	" \"question_text\": \"What is the output of print(3 * '4' + '5')?\",\n",
	" \"options\": [\"12\", \"445\", \"4445\", \"Error\"],\n",
	" \"correct_answer\": \"4445\",\n",
	" \"explanation\": \"The * operator with a string repeats it, and + concatenates strings\",\n",
	" \"difficulty\": 2\n",
	" }},\n",
	" {{\n",
	" \"question_type\": \"coding\",\n",
	" \"question_text\": \"Write a function that returns the sum of all even numbers in a list.\",\n",
	" \"options\": null,\n",
	" \"correct_answer\": \"def sum_even(numbers):\\\\n return sum(x for x in numbers if x % 2 == 0)\",\n",
	" \"explanation\": \"This solution uses a generator expression with the sum function to add only even numbers\",\n",
	" \"difficulty\": 3\n",
	" }}\n",
	" ]\n",
	"\n",
	" ONLY return the valid JSON array. Do NOT include any explanatory text or code fences.\n",
	" \"\"\"\n",
	"\n",
	" result = self.get_completion(prompt)\n",
	"\n",
	" # Clean up potential formatting issues\n",
	" result = result.strip()\n",
	" if result.startswith(\"```json\"):\n",
	" result = result.split(\"```json\")[1]\n",
	" if result.endswith(\"```\"):\n",
	" result = result.rsplit(\"```\", 1)[0]\n",
	"\n",
	" try:\n",
	" return json.loads(result)\n",
	" except json.JSONDecodeError as e:\n",
	" print(f\"JSON decode error: {e}\")\n",
	" print(f\"Raw response: {result}\")\n",
	" # Fall back to creating a minimal structure\n",
	" return [{\"question_type\": \"short-answer\",\n",
	" \"question_text\": \"There was an error generating questions. Please describe what you've learned today.\",\n",
	" \"options\": None,\n",
	" \"correct_answer\": \"Any reasonable summary\",\n",
	" \"explanation\": \"This is a backup question\",\n",
	" \"difficulty\": 1}]\n",
	"\n",
	" def evaluate_answer(self, question: Dict, user_answer: str) -> Dict:\n",
	" prompt = f\"\"\"\n",
	" Grade this response to a Python programming question:\n",
	"\n",
	" Question Type: {question[\"question_type\"]}\n",
	" Question: {question[\"question_text\"]}\n",
	" Correct Answer: {question[\"correct_answer\"]}\n",
	" Student's Answer: {user_answer}\n",
	"\n",
	" Return your evaluation as a JSON object with these fields:\n",
	" - is_correct: boolean (true/false)\n",
	" - feedback: detailed explanation of what was correct/incorrect\n",
	" - correct_solution: the correct solution with explanation if the answer was wrong\n",
	"\n",
	" For coding questions, be somewhat lenient - focus on logic correctness rather than exact syntax matching.\n",
	" For multiple choice, it must match the correct option.\n",
	" For short answer, assess if the key concepts are present and correct.\n",
	"\n",
	" ONLY return the valid JSON object. Do NOT include any explanatory text.\n",
	" \"\"\"\n",
	"\n",
	" result = self.get_completion(prompt)\n",
	"\n",
	" # Clean up potential formatting issues\n",
	" result = result.strip()\n",
	" if result.startswith(\"```json\"):\n",
	" result = result.split(\"```json\")[1]\n",
	" if result.endswith(\"```\"):\n",
	" result = result.rsplit(\"```\", 1)[0]\n",
	"\n",
	" try:\n",
	" return json.loads(result)\n",
	" except json.JSONDecodeError as e:\n",
	" print(f\"JSON decode error: {e}\")\n",
	" print(f\"Raw response: {result}\")\n",
	" # Return a fallback response\n",
	" return {\n",
	" \"is_correct\": False,\n",
	" \"feedback\": \"There was an error evaluating your answer. Please try again.\",\n",
	" \"correct_solution\": question[\"correct_answer\"]\n",
	" }\n",
	"\n",
	" def answer_student_question(self, question: str, context: Optional[str] = None) -> str:\n",
	" context_text = f\"Context from previous questions: {context}\\n\\n\" if context else \"\"\n",
	"\n",
	" prompt = f\"\"\"\n",
	" {context_text}You are an expert Python tutor. Answer this student's question clearly with explanations and examples:\n",
	"\n",
	" {question}\n",
	"\n",
	" - Use code examples where appropriate\n",
	" - Break down complex concepts step by step\n",
	" - Be comprehensive but concise\n",
	" - Use proper Markdown formatting for code\n",
	" \"\"\"\n",
	"\n",
	" return self.get_completion(prompt)\n",
	"\n",
	"# Content Generator with simplified storage\n",
	"class ContentGenerator:\n",
	" def __init__(self, api_key):\n",
	" self.llm_service = LLMService(api_key)\n",
	" # Simplified in-memory storage\n",
	" self.modules = []\n",
	" self.questions = []\n",
	" self.responses = []\n",
	" self.chat_logs = []\n",
	"\n",
	" def generate_module(self, day: int) -> tuple:\n",
	" day_topics = {\n",
	" 1: \"Python fundamentals (variables, data types, control structures)\",\n",
	" 2: \"Intermediate Python (functions, modules, error handling)\",\n",
	" 3: \"Advanced Python (file I/O, object-oriented programming, key libraries)\"\n",
	" }\n",
	" topic = day_topics.get(day, \"Python programming\")\n",
	"\n",
	" content = self.llm_service.generate_module_content(day, topic)\n",
	"\n",
	" # Extract title from content\n",
	" title = f\"Day {day} Python Module\"\n",
	" if content.startswith(\"# \"):\n",
	" title_line = content.split(\"\\n\", 1)[0]\n",
	" title = title_line.replace(\"# \", \"\").strip()\n",
	"\n",
	" # Save to in-memory storage\n",
	" module_id = len(self.modules) + 1\n",
	" self.modules.append({\n",
	" \"id\": module_id,\n",
	" \"day\": day,\n",
	" \"title\": title,\n",
	" \"content\": content,\n",
	" \"created_at\": datetime.datetime.utcnow()\n",
	" })\n",
	"\n",
	" return content, module_id\n",
	"\n",
	" def generate_exam(self, day: int, module_id: int, previous_mistakes: List = None) -> tuple:\n",
	" day_topics = {\n",
	" 1: \"Python fundamentals (variables, data types, control structures)\",\n",
	" 2: \"Intermediate Python (functions, modules, error handling)\",\n",
	" 3: \"Advanced Python (file I/O, object-oriented programming, key libraries)\"\n",
	" }\n",
	" topic = day_topics.get(day, \"Python programming\")\n",
	"\n",
	" # Generate questions for this day's topics\n",
	" try:\n",
	" questions_data = self.llm_service.generate_exam_questions(day, topic, previous_mistakes)\n",
	"\n",
	" if not questions_data:\n",
	" raise ValueError(\"Failed to generate exam questions\")\n",
	"\n",
	" saved_questions = []\n",
	" for q_data in questions_data:\n",
	" question_id = len(self.questions) + 1\n",
	"\n",
	" question = {\n",
	" \"id\": question_id,\n",
	" \"module_id\": module_id,\n",
	" \"question_type\": q_data[\"question_type\"],\n",
	" \"question_text\": q_data[\"question_text\"],\n",
	" \"options\": q_data.get(\"options\"),\n",
	" \"correct_answer\": q_data[\"correct_answer\"],\n",
	" \"explanation\": q_data[\"explanation\"],\n",
	" \"difficulty\": q_data.get(\"difficulty\", 3)\n",
	" }\n",
	"\n",
	" self.questions.append(question)\n",
	" saved_questions.append(question)\n",
	"\n",
	" return questions_data, saved_questions\n",
	" except Exception as e:\n",
	" print(f\"Error generating exam: {str(e)}\")\n",
	" # Create a simple fallback question\n",
	" fallback_question = {\n",
	" \"question_type\": \"short-answer\",\n",
	" \"question_text\": f\"Explain a key concept you learned in Day {day} about {topic}.\",\n",
	" \"options\": None,\n",
	" \"correct_answer\": \"Any reasonable explanation\",\n",
	" \"explanation\": \"This is a fallback question due to an error in question generation\",\n",
	" \"difficulty\": 2\n",
	" }\n",
	"\n",
	" question_id = len(self.questions) + 1\n",
	" question = {\n",
	" \"id\": question_id,\n",
	" \"module_id\": module_id,\n",
	" \"question_type\": fallback_question[\"question_type\"],\n",
	" \"question_text\": fallback_question[\"question_text\"],\n",
	" \"options\": fallback_question.get(\"options\"),\n",
	" \"correct_answer\": fallback_question[\"correct_answer\"],\n",
	" \"explanation\": fallback_question[\"explanation\"],\n",
	" \"difficulty\": fallback_question[\"difficulty\"]\n",
	" }\n",
	"\n",
	" self.questions.append(question)\n",
	" return [fallback_question], [question]\n",
	"\n",
	" def grade_response(self, question_id: int, user_answer: str) -> Dict:\n",
	" # Find question in memory\n",
	" question = next((q for q in self.questions if q[\"id\"] == question_id), None)\n",
	"\n",
	" if not question:\n",
	" return {\"error\": \"Question not found\"}\n",
	"\n",
	" try:\n",
	" feedback_data = self.llm_service.evaluate_answer(question, user_answer)\n",
	"\n",
	" # Save response to in-memory storage\n",
	" response_id = len(self.responses) + 1\n",
	" response = {\n",
	" \"id\": response_id,\n",
	" \"question_id\": question_id,\n",
	" \"user_answer\": user_answer,\n",
	" \"is_correct\": feedback_data.get(\"is_correct\", False),\n",
	" \"feedback\": feedback_data.get(\"feedback\", \"\"),\n",
	" \"timestamp\": datetime.datetime.utcnow()\n",
	" }\n",
	" self.responses.append(response)\n",
	"\n",
	" return feedback_data\n",
	" except Exception as e:\n",
	" print(f\"Error grading response: {str(e)}\")\n",
	" # Create a fallback response\n",
	" response_id = len(self.responses) + 1\n",
	" response = {\n",
	" \"id\": response_id,\n",
	" \"question_id\": question_id,\n",
	" \"user_answer\": user_answer,\n",
	" \"is_correct\": False,\n",
	" \"feedback\": f\"Error evaluating answer: {str(e)}\",\n",
	" \"timestamp\": datetime.datetime.utcnow()\n",
	" }\n",
	" self.responses.append(response)\n",
	"\n",
	" return {\n",
	" \"is_correct\": False,\n",
	" \"feedback\": f\"Error evaluating answer: {str(e)}\",\n",
	" \"correct_solution\": question[\"correct_answer\"]\n",
	" }\n",
	"\n",
	" def get_previous_mistakes(self, day: int) -> List:\n",
	" \"\"\"Get mistakes from previous days to inform adaptive content\"\"\"\n",
	" if day <= 1:\n",
	" return []\n",
	"\n",
	" previous_day = day - 1\n",
	"\n",
	" # Find modules from previous day\n",
	" previous_modules = [m for m in self.modules if m[\"day\"] == previous_day]\n",
	"\n",
	" if not previous_modules:\n",
	" return []\n",
	"\n",
	" module_ids = [module[\"id\"] for module in previous_modules]\n",
	" questions = [q for q in self.questions if q[\"module_id\"] in module_ids]\n",
	"\n",
	" if not questions:\n",
	" return []\n",
	"\n",
	" question_ids = [question[\"id\"] for question in questions]\n",
	" incorrect_responses = [r for r in self.responses if r[\"question_id\"] in question_ids and not r[\"is_correct\"]]\n",
	"\n",
	" mistakes = []\n",
	" for response in incorrect_responses:\n",
	" question = next((q for q in self.questions if q[\"id\"] == response[\"question_id\"]), None)\n",
	" if question:\n",
	" mistakes.append({\n",
	" \"question\": question[\"question_text\"],\n",
	" \"user_answer\": response[\"user_answer\"],\n",
	" \"correct_answer\": question[\"correct_answer\"]\n",
	" })\n",
	"\n",
	" return mistakes\n",
	"\n",
	" def answer_question(self, user_question: str, related_question_id: Optional[int] = None) -> str:\n",
	" # Get context from related question if available\n",
	" context = None\n",
	" if related_question_id:\n",
	" question = next((q for q in self.questions if q[\"id\"] == related_question_id), None)\n",
	" if question:\n",
	" context = f\"Question: {question['question_text']}\\nCorrect Answer: {question['correct_answer']}\\nExplanation: {question['explanation']}\"\n",
	"\n",
	" response = self.llm_service.answer_student_question(user_question, context)\n",
	"\n",
	" # Log the interaction\n",
	" chat_log_id = len(self.chat_logs) + 1\n",
	" chat_log = {\n",
	" \"id\": chat_log_id,\n",
	" \"user_question\": user_question,\n",
	" \"ai_response\": response,\n",
	" \"related_question_id\": related_question_id,\n",
	" \"timestamp\": datetime.datetime.utcnow()\n",
	" }\n",
	" self.chat_logs.append(chat_log)\n",
	"\n",
	" return response\n",
	"\n",
	"# Learning System Class\n",
	"class LearningSystem:\n",
	" def __init__(self, api_key):\n",
	" self.content_generator = ContentGenerator(api_key)\n",
	" self.current_day = 1\n",
	" self.current_module_id = None\n",
	" self.exam_start_time = None\n",
	" self.exam_in_progress = False\n",
	" self.exam_questions = []\n",
	" self.questions_data = [] # Store the questions data for display\n",
	"\n",
	" def generate_day_content(self):\n",
	" content, module_id = self.content_generator.generate_module(self.current_day)\n",
	" self.current_module_id = module_id\n",
	" return content\n",
	"\n",
	" def start_exam(self):\n",
	" try:\n",
	" if not self.current_module_id:\n",
	" # Check if we already have a module for this day\n",
	" existing_module = next((m for m in self.content_generator.modules if m[\"day\"] == self.current_day), None)\n",
	" if existing_module:\n",
	" self.current_module_id = existing_module[\"id\"]\n",
	" else:\n",
	" # Generate content for the day if not already done\n",
	" content, module_id = self.content_generator.generate_module(self.current_day)\n",
	" self.current_module_id = module_id\n",
	"\n",
	" # Get previous mistakes for adaptive learning\n",
	" previous_mistakes = self.content_generator.get_previous_mistakes(self.current_day)\n",
	"\n",
	" # Generate exam questions\n",
	" self.questions_data, self.exam_questions = self.content_generator.generate_exam(\n",
	" self.current_day,\n",
	" self.current_module_id,\n",
	" previous_mistakes\n",
	" )\n",
	"\n",
	" if not self.questions_data or not self.exam_questions:\n",
	" return \"Failed to generate exam questions. Please try again.\"\n",
	"\n",
	" self.exam_start_time = datetime.datetime.now()\n",
	" self.exam_in_progress = True\n",
	"\n",
	" # Format the exam for display\n",
	" exam_text = f\"# Day {self.current_day} Python Exam\\n\\n\"\n",
	" exam_text += f\"Time Limit: 1 hour\\n\"\n",
	" exam_text += f\"Start Time: {self.exam_start_time.strftime('%H:%M:%S')}\\n\"\n",
	" exam_text += f\"End Time: {(self.exam_start_time + timedelta(hours=1)).strftime('%H:%M:%S')}\\n\\n\"\n",
	"\n",
	" # Add adaptive learning notice if applicable\n",
	" if previous_mistakes and len(previous_mistakes) > 0:\n",
	" exam_text += f\"Note: This exam includes questions based on topics you had difficulty with previously.\\n\\n\"\n",
	"\n",
	" for i, question in enumerate(self.questions_data):\n",
	" exam_text += f\"## Question {i+1}: {question['question_type'].title()}\\n\\n\"\n",
	" exam_text += f\"{question['question_text']}\\n\\n\"\n",
	"\n",
	" if question['question_type'] == \"multiple-choice\" and question.get('options'):\n",
	" for j, option in enumerate(question['options']):\n",
	" exam_text += f\"- {chr(65+j)}. {option}\\n\"\n",
	"\n",
	" exam_text += \"\\n\"\n",
	"\n",
	" exam_text += \"## Instructions for submitting answers:\\n\\n\"\n",
	" exam_text += \"1. For multiple-choice questions, input the letter of your answer (A, B, C, or D)\\n\"\n",
	" exam_text += \"2. For short-answer questions, write your complete answer\\n\"\n",
	" exam_text += \"3. For coding questions, write your complete code solution\\n\"\n",
	" exam_text += \"4. Separate each answer with two line breaks\\n\\n\"\n",
	"\n",
	" return exam_text\n",
	" except Exception as e:\n",
	" self.exam_in_progress = False\n",
	" return f\"Error starting exam: {str(e)}\"\n",
	"\n",
	" def submit_exam(self, answers_text):\n",
	" try:\n",
	" if not self.exam_in_progress:\n",
	" return \"No exam is currently in progress. Please start an exam first.\"\n",
	"\n",
	" if not self.exam_questions:\n",
	" return \"No exam questions available. Please restart the exam.\"\n",
	"\n",
	" # Check time\n",
	" current_time = datetime.datetime.now()\n",
	" if current_time > self.exam_start_time + timedelta(hours=1):\n",
	" time_overrun = current_time - (self.exam_start_time + timedelta(hours=1))\n",
	" overrun_minutes = time_overrun.total_seconds() / 60\n",
	" time_notice = f\"Time limit exceeded by {overrun_minutes:.1f} minutes. Your answers are being processed anyway.\"\n",
	" else:\n",
	" time_notice = \"Exam completed within the time limit.\"\n",
	"\n",
	" # Split answers by question (double newline separator)\n",
	" answers = [ans.strip() for ans in answers_text.split(\"\\n\\n\") if ans.strip()]\n",
	"\n",
	" feedback_text = f\"# Day {self.current_day} Exam Results\\n\\n\"\n",
	" feedback_text += f\"{time_notice}\\n\\n\"\n",
	"\n",
	" correct_count = 0\n",
	" total_evaluated = 0\n",
	"\n",
	" # Ensure we don't exceed the number of questions\n",
	" num_questions = min(len(self.exam_questions), len(answers))\n",
	"\n",
	" # If the user provided fewer answers than questions, fill in blanks\n",
	" while len(answers) < len(self.exam_questions):\n",
	" answers.append(\"\")\n",
	"\n",
	" for i in range(len(self.exam_questions)):\n",
	" question = self.exam_questions[i]\n",
	" answer = answers[i] if i < len(answers) else \"\"\n",
	"\n",
	" # Handle empty answers\n",
	" if not answer:\n",
	" feedback_text += f\"## Question {i+1}\\n\\n\"\n",
	" feedback_text += \"Your Answer: No answer provided\\n\\n\"\n",
	" feedback_text += \"Result: Incorrect\\n\\n\"\n",
	" feedback_text += f\"Correct Solution: {question['correct_answer']}\\n\\n\"\n",
	" total_evaluated += 1\n",
	" continue\n",
	"\n",
	" try:\n",
	" # Grade the response\n",
	" feedback = self.content_generator.grade_response(question[\"id\"], answer)\n",
	" total_evaluated += 1\n",
	"\n",
	" # Format feedback\n",
	" feedback_text += f\"## Question {i+1}\\n\\n\"\n",
	" feedback_text += f\"Your Answer:\\n{answer}\\n\\n\"\n",
	" feedback_text += f\"Result: {'✅ Correct' if feedback.get('is_correct', False) else '❌ Incorrect'}\\n\\n\"\n",
	" feedback_text += f\"Feedback:\\n{feedback.get('feedback', '')}\\n\\n\"\n",
	"\n",
	" if feedback.get('is_correct', False):\n",
	" correct_count += 1\n",
	" else:\n",
	" feedback_text += f\"Correct Solution:\\n{feedback.get('correct_solution', '')}\\n\\n\"\n",
	" except Exception as e:\n",
	" feedback_text += f\"## Question {i+1}\\n\\n\"\n",
	" feedback_text += f\"Error grading answer: {str(e)}\\n\\n\"\n",
	"\n",
	" # Calculate score\n",
	" if total_evaluated > 0:\n",
	" score = correct_count / total_evaluated * 100\n",
	" else:\n",
	" score = 0\n",
	"\n",
	" feedback_text += f\"# Final Score: {score:.1f}%\\n\\n\"\n",
	"\n",
	" # Suggestions for improvement\n",
	" if score < 100:\n",
	" feedback_text += \"## Suggestions for Improvement\\n\\n\"\n",
	" if score < 60:\n",
	" feedback_text += \"- Review the fundamental concepts again\\n\"\n",
	" feedback_text += \"- Practice more with the code examples\\n\"\n",
	" feedback_text += \"- Use the Q&A Sandbox to ask about difficult topics\\n\"\n",
	" elif score < 80:\n",
	" feedback_text += \"- Focus on the specific areas where you made mistakes\\n\"\n",
	" feedback_text += \"- Try rewriting the solutions for incorrect answers\\n\"\n",
	" else:\n",
	" feedback_text += \"- Great job! Just a few minor issues to review\\n\"\n",
	" feedback_text += \"- Look at the explanations for the few questions you missed\\n\"\n",
	" else:\n",
	" feedback_text += \"## Excellent Work!\\n\\n\"\n",
	" feedback_text += \"You've mastered today's content. Ready for the next day's material!\\n\"\n",
	"\n",
	" self.exam_in_progress = False\n",
	" return feedback_text\n",
	" except Exception as e:\n",
	" self.exam_in_progress = False\n",
	" return f\"Error submitting exam: {str(e)}\"\n",
	"\n",
	" def answer_sandbox_question(self, question):\n",
	" return self.content_generator.answer_question(question)\n",
	"\n",
	" def advance_to_next_day(self):\n",
	" if self.current_day < 3:\n",
	" self.current_day += 1\n",
	" self.current_module_id = None\n",
	" self.exam_questions = []\n",
	" return f\"Advanced to Day {self.current_day}.\"\n",
	" else:\n",
	" return \"You have completed the 3-day curriculum.\"\n",
	"\n",
	" def get_learning_progress(self):\n",
	" try:\n",
	" modules = self.content_generator.modules\n",
	" questions = self.content_generator.questions\n",
	" responses = self.content_generator.responses\n",
	"\n",
	" total_questions = len(questions)\n",
	" answered_questions = len(responses)\n",
	" correct_answers = sum(1 for r in responses if r[\"is_correct\"])\n",
	"\n",
	" if answered_questions > 0:\n",
	" accuracy = correct_answers / answered_questions * 100\n",
	" else:\n",
	" accuracy = 0\n",
	"\n",
	" report = \"# Learning Progress Summary\\n\\n\"\n",
	" report += f\"## Overall Statistics\\n\"\n",
	" report += f\"- Total modules completed: {len(modules)}\\n\"\n",
	" report += f\"- Total questions attempted: {answered_questions}/{total_questions}\\n\"\n",
	" report += f\"- Overall accuracy: {accuracy:.1f}%\\n\\n\"\n",
	"\n",
	" # Day-by-day progress with adaptive learning info\n",
	" for day in range(1, 4):\n",
	" day_modules = [m for m in modules if m[\"day\"] == day]\n",
	"\n",
	" report += f\"## Day {day}: \"\n",
	" if day_modules:\n",
	" report += f\"{day_modules[0]['title']}\\n\"\n",
	"\n",
	" day_questions = [q for q in questions if q[\"module_id\"] in [m[\"id\"] for m in day_modules]]\n",
	" day_responses = [r for r in responses if r[\"question_id\"] in [q[\"id\"] for q in day_questions]]\n",
	"\n",
	" day_total = len(day_questions)\n",
	" day_answered = len(day_responses)\n",
	" day_correct = sum(1 for r in day_responses if r[\"is_correct\"])\n",
	"\n",
	" if day_answered > 0:\n",
	" day_accuracy = day_correct / day_answered * 100\n",
	" report += f\"- Exam Score: {day_accuracy:.1f}%\\n\"\n",
	" else:\n",
	" report += \"- Exam: Not taken yet\\n\"\n",
	"\n",
	" report += f\"- Questions attempted: {day_answered}/{day_total}\\n\"\n",
	"\n",
	" # Show adaptive learning details\n",
	" if day > 1:\n",
	" previous_mistakes = self.content_generator.get_previous_mistakes(day)\n",
	" if previous_mistakes:\n",
	" report += f\"- Adaptive Learning: {len(previous_mistakes)} topics from Day {day-1} reinforced\\n\"\n",
	"\n",
	" # Show exam results if available\n",
	" if day_answered > 0:\n",
	" report += \"### Exam Performance\\n\"\n",
	"\n",
	" # Group by question type\n",
	" question_types = set(q[\"question_type\"] for q in day_questions)\n",
	" for q_type in question_types:\n",
	" type_questions = [q for q in day_questions if q[\"question_type\"] == q_type]\n",
	" type_responses = [r for r in day_responses if r[\"question_id\"] in [q[\"id\"] for q in type_questions]]\n",
	" type_correct = sum(1 for r in type_responses if r[\"is_correct\"])\n",
	"\n",
	" if type_responses:\n",
	" type_accuracy = type_correct / len(type_responses) * 100\n",
	" report += f\"- {q_type.title()}: {type_accuracy:.1f}% correct\\n\"\n",
	"\n",
	" # Common mistakes\n",
	" incorrect_responses = [r for r in day_responses if not r[\"is_correct\"]]\n",
	" if incorrect_responses:\n",
	" report += \"\\n### Areas for Improvement\\n\"\n",
	"\n",
	" for resp in incorrect_responses[:3]: # Show top 3 mistakes\n",
	" question = next((q for q in questions if q[\"id\"] == resp[\"question_id\"]), None)\n",
	" if question:\n",
	" report += f\"- Question: {question['question_text'][:100]}...\\n\"\n",
	" report += f\" Your Answer: {resp['user_answer'][:100]}...\\n\"\n",
	" report += f\" Correct Answer: {question['correct_answer'][:100]}...\\n\\n\"\n",
	" else:\n",
	" report += \"Not started yet\\n\"\n",
	"\n",
	" report += \"\\n\"\n",
	"\n",
	" # Learning recommendations\n",
	" report += \"## Recommendations\\n\\n\"\n",
	" if correct_answers < answered_questions * 0.7:\n",
	" report += \"- Review the modules before moving to the next day\\n\"\n",
	" report += \"- Focus on practicing code examples\\n\"\n",
	" report += \"- Use the Q&A Sandbox to clarify difficult concepts\\n\"\n",
	" else:\n",
	" report += \"- Continue with the current pace\\n\"\n",
	" report += \"- Try to implement small projects using what you've learned\\n\"\n",
	"\n",
	" return report\n",
	" except Exception as e:\n",
	" return f\"Error generating progress report: {str(e)}\""
	],
	"metadata": {
	"id": "pJaXDk_Lmyg9"
	},
	"execution_count": 19,
	"outputs": []
	},
	{
	"cell_type": "markdown",
	"source": [
	"#gradio"
	],
	"metadata": {
	"id": "GFXvlb36fKOd"
	}
	},
	{
	"cell_type": "code",
	"source": [
	"# Gradio UI - Modified for Google Colab\n",
	"import os\n",
	"import gradio as gr\n",
	"\n",
	"# Note: We're not importing from core_system\n",
	"# Instead, we'll use the classes already defined in the previous cell\n",
	"\n",
	"def create_interface():\n",
	" # System initialization section\n",
	" def initialize_system(api_key_value):\n",
	" if not api_key_value or len(api_key_value) < 10: # Basic validation\n",
	" return \"Please enter a valid API key.\", gr.update(visible=False), None\n",
	"\n",
	" try:\n",
	" # Test API connection\n",
	" test_service = LLMService(api_key_value)\n",
	" test_response = test_service.get_completion(\"Say hello\")\n",
	"\n",
	" if len(test_response) > 0:\n",
	" learning_system = LearningSystem(api_key_value)\n",
	" return \"✅ System initialized successfully! You can now use the learning system.\", gr.update(visible=True), learning_system\n",
	" else:\n",
	" return \"❌ API connection test failed. Please check your API key.\", gr.update(visible=False), None\n",
	" except Exception as e:\n",
	" return f\"❌ Error initializing system: {str(e)}\", gr.update(visible=False), None\n",
	"\n",
	" with gr.Blocks(title=\"AI-Powered Python Learning System\", theme=\"soft\") as interface:\n",
	" # Store learning system state\n",
	" learning_system_state = gr.State(None)\n",
	"\n",
	" # Header\n",
	" gr.Markdown(\n",
	" \"\"\"\n",
	" <div style=\"text-align: center; margin-bottom: 20px;\">\n",
	" <h1 style=\"color: #4a69bd; font-size: 2.5em;\">AI-Powered Python Learning System</h1>\n",
	" <p style=\"font-size: 1.2em; color: #444;\">Master Python programming with personalized AI tutoring</p>\n",
	" </div>\n",
	" \"\"\"\n",
	" )\n",
	"\n",
	" # API Key input - outside the tabs\n",
	" with gr.Row():\n",
	" # Try to get API key from environment variable\n",
	" API_KEY = os.environ.get(\"GROQ_API_KEY\", \"\")\n",
	" api_key_input = gr.Textbox(\n",
	" label=\"Enter your Groq API Key\",\n",
	" placeholder=\"gsk_...\",\n",
	" type=\"password\",\n",
	" value=API_KEY # Use environment variable if available\n",
	" )\n",
	" init_btn = gr.Button(\"Initialize System\", variant=\"primary\")\n",
	"\n",
	" init_status = gr.Markdown(\"Enter your Groq API key and click 'Initialize System' to begin.\")\n",
	"\n",
	" # Main interface container - hidden until initialized\n",
	" with gr.Column(visible=False) as main_interface:\n",
	" with gr.Tabs() as tabs:\n",
	" # Content & Learning tab\n",
	" with gr.Tab(\"Content & Learning\"):\n",
	" with gr.Row():\n",
	" day_display = gr.Markdown(\"## Current Day: 1\")\n",
	"\n",
	" with gr.Row():\n",
	" generate_content_btn = gr.Button(\"Generate Today's Content\", variant=\"primary\")\n",
	" next_day_btn = gr.Button(\"Advance to Next Day\", variant=\"secondary\")\n",
	"\n",
	" content_display = gr.Markdown(\"Click 'Generate Today's Content' to begin.\")\n",
	"\n",
	" # Exam tab\n",
	" with gr.Tab(\"Exam\"):\n",
	" with gr.Row():\n",
	" start_exam_btn = gr.Button(\"Start Exam\", variant=\"primary\")\n",
	"\n",
	" exam_display = gr.Markdown(\"Click 'Start Exam' to begin the assessment.\")\n",
	"\n",
	" with gr.Row():\n",
	" exam_answers = gr.Textbox(\n",
	" label=\"Enter your answers (separate each answer with two line breaks)\",\n",
	" placeholder=\"Answer 1\\n\\nAnswer 2\\n\\nAnswer 3...\",\n",
	" lines=15\n",
	" )\n",
	"\n",
	" submit_exam_btn = gr.Button(\"Submit Exam\", variant=\"primary\")\n",
	"\n",
	" exam_feedback = gr.Markdown(\"Your exam results will appear here.\")\n",
	"\n",
	" # Q&A Sandbox tab\n",
	" with gr.Tab(\"Q&A Sandbox\"):\n",
	" with gr.Row():\n",
	" question_input = gr.Textbox(\n",
	" label=\"Ask any question about Python\",\n",
	" placeholder=\"Enter your question here...\",\n",
	" lines=3\n",
	" )\n",
	"\n",
	" ask_btn = gr.Button(\"Ask Question\", variant=\"primary\")\n",
	"\n",
	" answer_display = gr.Markdown(\"Ask a question to get started.\")\n",
	"\n",
	" # Progress Report tab\n",
	" with gr.Tab(\"Progress Report\"):\n",
	" with gr.Row():\n",
	" report_btn = gr.Button(\"Generate Progress Report\", variant=\"primary\")\n",
	"\n",
	" progress_display = gr.Markdown(\"Click 'Generate Progress Report' to see your learning statistics.\")\n",
	"\n",
	" # Custom functions to handle state\n",
	" def generate_content(learning_system):\n",
	" if not learning_system:\n",
	" return \"Please initialize the system first.\"\n",
	" return learning_system.generate_day_content()\n",
	"\n",
	" def advance_day(learning_system):\n",
	" if not learning_system:\n",
	" return \"Please initialize the system first.\", \"## Current Day: 1\"\n",
	" result = learning_system.advance_to_next_day()\n",
	" return result, f\"## Current Day: {learning_system.current_day}\"\n",
	"\n",
	" def start_exam(learning_system):\n",
	" if not learning_system:\n",
	" return \"Please initialize the system first.\"\n",
	" try:\n",
	" exam_content = learning_system.start_exam()\n",
	" return exam_content\n",
	" except Exception as e:\n",
	" return f\"Error starting exam: {str(e)}\"\n",
	"\n",
	" def submit_exam(learning_system, answers):\n",
	" if not learning_system:\n",
	" return \"Please initialize the system first.\"\n",
	" if not answers.strip():\n",
	" return \"Please provide answers before submitting.\"\n",
	"\n",
	" try:\n",
	" feedback = learning_system.submit_exam(answers)\n",
	" return feedback\n",
	" except Exception as e:\n",
	" return f\"Error evaluating exam: {str(e)}\"\n",
	"\n",
	" def ask_question(learning_system, question):\n",
	" if not learning_system:\n",
	" return \"Please initialize the system first.\"\n",
	" if not question.strip():\n",
	" return \"Please enter a question.\"\n",
	"\n",
	" try:\n",
	" answer = learning_system.answer_sandbox_question(question)\n",
	" return answer\n",
	" except Exception as e:\n",
	" return f\"Error processing question: {str(e)}\"\n",
	"\n",
	" def generate_progress_report(learning_system):\n",
	" if not learning_system:\n",
	" return \"Please initialize the system first.\"\n",
	"\n",
	" try:\n",
	" report = learning_system.get_learning_progress()\n",
	" return report\n",
	" except Exception as e:\n",
	" return f\"Error generating progress report: {str(e)}\"\n",
	"\n",
	" # Set up event handlers\n",
	" init_btn.click(\n",
	" initialize_system,\n",
	" inputs=[api_key_input],\n",
	" outputs=[init_status, main_interface, learning_system_state]\n",
	" )\n",
	"\n",
	" generate_content_btn.click(\n",
	" generate_content,\n",
	" inputs=[learning_system_state],\n",
	" outputs=[content_display]\n",
	" )\n",
	"\n",
	" next_day_btn.click(\n",
	" advance_day,\n",
	" inputs=[learning_system_state],\n",
	" outputs=[content_display, day_display]\n",
	" )\n",
	"\n",
	" start_exam_btn.click(\n",
	" start_exam,\n",
	" inputs=[learning_system_state],\n",
	" outputs=[exam_display]\n",
	" )\n",
	"\n",
	" submit_exam_btn.click(\n",
	" submit_exam,\n",
	" inputs=[learning_system_state, exam_answers],\n",
	" outputs=[exam_feedback]\n",
	" )\n",
	"\n",
	" ask_btn.click(\n",
	" ask_question,\n",
	" inputs=[learning_system_state, question_input],\n",
	" outputs=[answer_display]\n",
	" )\n",
	"\n",
	" report_btn.click(\n",
	" generate_progress_report,\n",
	" inputs=[learning_system_state],\n",
	" outputs=[progress_display]\n",
	" )\n",
	"\n",
	" return interface\n",
	"\n",
	"# Create and launch the interface\n",
	"# For Colab, make sure to install gradio first if you haven't\n",
	"# !pip install gradio\n",
	"interface = create_interface()\n",
	"interface.launch(share=True)"
	],
	"metadata": {
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 611
	},
	"id": "XMuEPwonhq5L",
	"outputId": "5fcd89e5-4004-4b4e-e7f1-b01b84bcc5fe"
	},
	"execution_count": 20,
	"outputs": [
	{
	"output_type": "stream",
	"name": "stdout",
	"text": [
	"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
	"* Running on public URL: https://af28b80e4f184f47a8.gradio.live\n",
	"\n",
	"This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
	]
	},
	{
	"output_type": "display_data",
	"data": {
	"text/plain": [
	"<IPython.core.display.HTML object>"
	],
	"text/html": [
	"<div><iframe src=\"https://af28b80e4f184f47a8.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
	]
	},
	"metadata": {}
	},
	{
	"output_type": "execute_result",
	"data": {
	"text/plain": []
	},
	"metadata": {},
	"execution_count": 20
	}
	]
	},
	{
	"cell_type": "code",
	"source": [],
	"metadata": {
	"id": "Chns0fHgNGxU"
	},
	"execution_count": null,
	"outputs": []
	},
	{
	"cell_type": "code",
	"source": [],
	"metadata": {
	"id": "3a6ZeomONG0M"
	},
	"execution_count": null,
	"outputs": []
	}
	]
	}